Azulene Studio Quick Start Guide:

This guide walks you through a complete working example of using Azulene Studio to submit and monitor an Absolute Binding Free Energy (ABFE) job using a protein and ligand file.

You will learn how to:

a. Install azulene-studio from PyPI
b. Log in
c. Place your protein + ligand files in the correct location
d. Submit an absolute_binding job (including automatic file upload)
e. Check job status and retrieve results

Create and activate a virtual environment (optional)

For conda

conda create -n azulene-env python=3.11 -y
conda activate azulene-env

For Python

# Create a virtual environment
python -m venv azulene-env

# Activate the environment on Windows
azulene-env\Scripts\activate

# Activate the environment on macOS / Linux
source azulene-env/bin/activate

a. Install azulene-studio from PyPI

pip install azulene-studio

Confirm installation:

python -m azulene.main --help

CLI commands: azulene / azu

Installing the package adds two equivalent console commands — azulene and its short alias azu — so every example below can be run as azulene <command> or azu <command> (e.g. azulene login / azu login), or as python -m azulene.main <command>.

Deprecation: the old opal command (and import opal) still work but are deprecated aliases from the pre–Azulene Studio naming, and will be removed in a future release. Prefer azulene / azu (and import azulene).

Choosing a backend (azulene config env)

Everything talks to production unless you say otherwise, and most people never need to change that. If you are testing against the development backend:

azulene config env            # which one am I on, and what else is there
azulene config env devel      # use development from now on
azulene config env --reset    # back to production

Each environment is a separate account with its own jobs and credits, so each needs its own azulene login; sessions are kept apart, and switching back does not ask you to log in again. azulene whoami always prints which one you are on.

For a single command, or in a script, AZULENE_ENV=devel azulene ... does the same without saving anything. To reach a project neither name covers — a branch deployment, a local stack — set AZULENE_SUPABASE_URL and AZULENE_SUPABASE_ANON_KEY together; setting only one is an error rather than a silent fallback to the production value for the other. In Python these are read when azulene is imported, so export them before import azulene, not after.

b. Log in

Run:

python -m azulene.main login

The CLI will securely prompt you:

Your email: example@gmail.com
Your password: **********

After this, Azulene Studio stores your auth tokens locally so you don’t need to log in again.

c. Example protein and ligand files

Azulene Studio ships with bundled example files you can use right away:

from azulene.examples import T4_LYSOZYME_BENZENE_PDB, BENZENE_BOUND_SDF, TOLUENE_SDF

The CLI will automatically detect these as local files, upload them to Azulene Studio, and replace the paths with storage URLs.

d. Submit an absolute_binding job

The job type is:

absolute_binding

The required parameters are:

{
  "pdb_file": "",
  "ligand_file": "",
  "ligand_smiles": ""
}

Example 1: Using the bundled sample files

from azulene import jobs
from azulene.examples import T4_LYSOZYME_BENZENE_PDB, BENZENE_BOUND_SDF

result = jobs.submit(
    job_type="absolute_binding",
    input_data={
        "pdb_file": str(T4_LYSOZYME_BENZENE_PDB),
        "ligand_file": str(BENZENE_BOUND_SDF),
        "ligand_smiles": "c1ccccc1"
    }
)

print(result)

You should see something like:

📤 Uploading local files...
✅ Job submitted successfully
{
  "job_id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
  "status": "submitted",
  "message": "Job submitted successfully"
}

Example 2: Absolute Binding Free Energy (ABFE) (Ligand Already in PDB)

This example runs an absolute_binding job using:

  • A protein PDB file
  • A ligand inside the PDB file
  • A SMILES string for the ligand
  • Shortened equilibration & production lengths
  • Only 1 protocol repeat
from azulene import auth, jobs
from azulene.examples import FKB_MODEL_PDB

# 1. Log in (Only if you haven't logged in)
auth.login(email="your@email.com", password="yourpassword")

# 2. Submit the job
input_data = {
    "pdb_file": str(FKB_MODEL_PDB),
    "ligand_smiles": "CS(=O)C",
    "protocol_repeats": 1,
    "ligand_in_pdb_file": True,
    "complex_prod_length": 0.1,
    "solvent_prod_length": 0.1,
    "complex_equil_length": 0.02,
    "solvent_equil_length": 0.02
}

result = jobs.submit(
    job_type="absolute_binding",
    input_data=input_data
)

print(result)

e. Submit an Absolute Hydration Free Energy job

The job type is:

aqueous_solvation

The required parameters are:

{
  "smiles": ""
}

Example: Using a simple SMILES (CCO)

from azulene import jobs

result = jobs.submit(
    job_type="aqueous_solvation",
    input_data={
        "smiles": "CCO"
    }
)

print(result)

You should see something like:

✅ Job submitted successfully
{
  "job_id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
  "status": "submitted",
  "message": "Job submitted successfully"
}

f. Other useful commands

1. List your jobs

Last 5 jobs (default):

python -m azulene.main jobs get-jobs

All jobs:

python -m azulene.main jobs get-jobs --all

Filter by job type and status:

python -m azulene.main jobs get-jobs \
  --job-type absolute_binding \
  --status completed

Filter by date:

python -m azulene.main jobs get-jobs \
  --start-date 2025-12-01T00:00:00Z \
  --end-date   2025-12-05T00:00:00Z

2. Check a specific job

python -m azulene.main jobs get --job-id YOUR_JOB_ID

This returns:

  • job status
  • input data
  • results (if completed)
  • timestamps
  • any error messages

3. Poll running jobs

python -m azulene.main jobs check-running-jobs

Use this to check on running jobs.

4. Cancel a job

python -m azulene.main jobs cancel --job-id YOUR_JOB_ID

Only works if the job is still running.

Azulene Studio

This guide shows how to:

  1. Log in
  2. Submit a job
  3. Inspect and filter jobs
  4. Get / cancel a specific job
  5. Poll running jobs
  6. Check service health & job types
  7. Submit a batch of jobs

How to download Azulene Studio

pip install azulene-studio

0. Imports (Python)

from azulene import auth, jobs

1. Log in

from azulene import auth

res = auth.login(email="test@example.com", password="pass123")
print(res)  # {"ok": True, "message": "Logged in successfully!"}

You stay logged in via locally stored tokens until you explicitly log out.

2. Check who you are

from azulene import auth

res = auth.whoami()
print(res)
# {
#   "ok": True,
#   "user": { ... full user object ... },
#   "slim": {
#       "email": "...",
#       "role": "...",
#       "approved": True,
#       ...
#   }
# }

3. Submit a single job

Example job type: generate_conformers with a SMILES and number of conformers.

from azulene import jobs

res = jobs.submit(
    job_type="generate_conformers",
    input_data={"smiles": "CCO", "num_conformers": 5},  # dict
)
print(res)
# {"ok": True, "data": {"job_id": "...", "status": "submitted", ...}}

(You can also pass a JSON string instead of a dict if you want.)

4. List and filter jobs

By default, the server returns the last 5 jobs for the current user. You can ask for all jobs, limit, or filter by job_type, status, or date range.

from azulene import jobs

# Last 5 jobs (default)
print(jobs.get_jobs())

# All jobs
print(jobs.get_jobs(all_jobs=True))

# Last 10 jobs
print(jobs.get_jobs(limit=10))

# Filter by job_type and status
print(
    jobs.get_jobs(
        job_type="generate_conformers",
        status="completed",
    )
)

# Filter by created_at date range (ISO timestamps)
print(
    jobs.get_jobs(
        start_date="2025-12-01T00:00:00Z",
        end_date="2025-12-05T00:00:00Z",
    )
)

Each call returns something like:

{"ok": True, "data": [ { "id": "...", "job_type": "...", ... }, ... ]}

5. Get a specific job

Once you have a job_id, you can fetch that job’s details.

from azulene import jobs

res = jobs.get(job_id="YOUR_JOB_ID")
print(res)
# {"ok": True, "data": { "id": "...", "status": "...", "input_data": {...}, "results": {...}, ... }}

6. Cancel a job

If a job is still running, you can cancel it.

from azulene import jobs

res = jobs.cancel(job_id="YOUR_JOB_ID")
print(res)
# {"ok": True, "data": {...}}

7. Poll running jobs

This endpoint checks any currently running jobs and update their statuses.

from azulene import jobs

res = jobs.check_running_jobs()
print(res)
# {"ok": True, "data": {...}}  # depends on your backend payload

8. Health check

Check that the Azulene Studio backend is reachable.

from azulene import jobs

res = jobs.check_health()
print(res)
# {"ok": True, "data": {...}}  on success

9. Discover available job types

List job types supported by the current backend.

from azulene import jobs

res = jobs.get_job_types()
print(res)
# {"ok": True, "data": {"job_types": [{"id": "generate_conformers", "name": "3D Molecular Conformer Generation", ...}, ...]}}

10. Submit a batch of jobs (same job_type, many inputs)

You can submit multiple jobs at once for a single job_type. Each entry in the list becomes a separate job under the hood.

from azulene import jobs

small_input_list = [
    {"smiles": "CCO",   "num_conformers": 5},
    {"smiles": "CCCO",  "num_conformers": 3},
    {"smiles": "CCcndO","num_conformers": 2},
]

res = jobs.submit_batch_jobs(
    job_type="generate_conformers",
    input_data=small_input_list,
)
print(res)
# {
#   "ok": True,
#   "results": [
#       {
#         "index": 0,
#         "input": {...},
#         "response": {
#             "ok": True,
#             "data": {
#                 "job_id": "...",
#                 "status": "submitted",
#                 "message": "Job submitted successfully",
#                 ...
#             }
#         }
#       },
#       ...
#   ]
# }

11. Log out

When you’re done, you can clear the local tokens.

from azulene import auth

res = auth.logout()
print(res)  # {"ok": True}

TL;DR minimal workflows

from azulene import auth, jobs

# 1) Log in
auth.login(email="test@example.com", password="pass123")

# 2) Submit a job
submit_res = jobs.submit(
    job_type="generate_conformers",
    input_data={"smiles": "CCO", "num_conformers": 5},
)
print(submit_res)

# 3) List recent jobs
print(jobs.get_jobs())

# 4) Fetch that job by ID
job_id = submit_res["data"]["job_id"]
print(jobs.get(job_id=job_id))

Help Commands

python -m azulene.main --help

python -m azulene.main jobs --help

python -m azulene.main jobs submit --help

Chaining a macrocycle run into Protein-Ligand Interaction Energy / Protein-Ligand Pose Refinement

boltz_macrocycle folds the complex twice.

The peptide fold is the tool's primary output and is unchanged: it produces affinity_pkd, the crosslink report, the top_k_* interface scores and the pose_*.pdb files shown in the viewer.

The ligand fold co-folds the same system a second time with the binder declared as a molecule from its own SMILES, affinity head off. It exists only for what happens after prediction: Protein-Ligand Interaction Energy (opal_ml_score) and Protein-Ligand Pose Refinement (opal_ml_optimize) take a ligand, so without it the binder's chemistry has to be re-perceived from diffusion coordinates — which on a stapled peptide returned an amide as a hemiaminal and a hydrocarbon staple as a bicyclobutane. Set the ligand_arm input to false to skip it: the result goes back to a single fold and the run takes roughly half as long.

The one thing to get right when you chain

Submit the opal_ml_inputs block the result publishes, and read opal_ml_inputs.from_arm to know which fold it describes.

  • from_arm: "ligand" — the ligand fold ran, and this block is its complex, chains and ligand_smiles. Those coordinates are not the pose_*.pdb files, and affinity_pkd does not refer to them. arm_note says the same thing in the payload.
  • from_arm: "peptide" — the ligand fold was turned off, or could not resolve the binder to a molecule. The block is the two-chain peptide hand-off as before, and a warnings entry says which of the two it was.

Either way the block is submittable as it stands. Do not rebuild it from pose_*.pdb.

Both folds' hand-offs are reachable. opal_ml_inputs is the recommended one; opal_ml_inputs_by_arm is {"peptide": {...}, "ligand": {...}}, carrying whichever arms produced a usable hand-off. Reach for it when you specifically want to score the peptide complex affinity_pkd describes rather than the recommended one — which is the one case where the recommended block is not what you want.

from azulene import jobs

results = jobs.get(job_id=macrocycle_job_id)["data"]["results"]
handoff = results["opal_ml_inputs"]

print(handoff["from_arm"])        # "ligand" or "peptide"
print(handoff["usage"])           # the routing this job wants, in words

payload = {
    "protein_file": handoff["complex_file"]["download_url"],
    "chain_id": handoff["receptor_chain"],
    "binder_chain": handoff["binder_chain"],
}
if handoff.get("ligand_smiles"):
    payload["ligand_smiles"] = handoff["ligand_smiles"]

jobs.submit("opal_ml_score", payload)

Naming the binder's chemistry

The ligand fold needs a SMILES for the binder. Three ways to get one, in the order the job tries them:

Input ligand_smiles_source When to use it
ligand_smiles supplied You want a specific molecule or protonation state. Used verbatim — it is not re-charged at pH 7.4.
helm helm You already describe the binder in HELM2. Read for CHEMISTRY only, never as a starting conformer. The reader here knows far fewer monomers than the one peptide_structure uses, so a valid HELM naming an ncAA may not parse; it then falls through to the CCD assembly with a warning and ligand_smiles_source comes back ccd, so the fold still runs and the envelope records that the HELM was not the source.
neither ccd The default. Assembled from binder.sequence, modifications and bonds using the same PDB Chemical Component Dictionary entries the peptide fold uses.

Result keys the ligand fold adds

Present only when the ligand fold ran.

Key Type Meaning
ligand_smiles string The molecule that was folded.
ligand_smiles_source string ccd, helm or supplied — whether the chemistry was derived or asserted.
ligand_n_heavy_atoms integer Heavy-atom count of that molecule.
ligand_net_charge integer Formal net charge of that molecule.
arm_interface_agreement number Jaccard overlap of the receptor residues each fold's top pose contacts. The one cross-arm number that means something: it needs no atom correspondence between a polymer and a ligand, and it answers whether representing the binder differently moved it to a different site.
arm_binder_centroid_shift_a number Distance in Ångström between the two folds' top-pose binder centroids.
opal_ml_inputs_by_arm object {"peptide": …, "ligand": …} — both folds' hand-offs, for when you want the one opal_ml_inputs did not recommend. Only arms that produced a usable hand-off appear.
ligand_arm object The ligand fold's own block — smiles, smiles_source, n_heavy_atoms, net_charge, largest_ring, n_samples, n_poses_returned, n_binder_tokens and its own top_k_labels / top_k_ipsae / top_k_iptm / top_k_pdockq2 / top_k_mean_plddt / top_k_cluster_size.

The ligand fold's top_k_ipsae and top_k_pdockq2 are nested inside ligand_arm rather than placed beside the peptide fold's, because they are not on the same scale: a polymer is one token per residue and a ligand one per atom, so the same binder is 14 tokens in one fold and 119 in the other. Rank the ligand fold's poses against each other with them; do not compare them across folds. arm_interface_agreement is the number that does compare.

Opal PPI naming compatibility

Use opal_ppi for Rank protein-protein interfaces using Opal. Existing boltz_ppi submissions and example selectors continue to address the same function; permissions, credits, inputs, scores, and pose ordering are unchanged. Other Boltz tools keep their names.

This SDK requests X-Opal-Public-Names: 1 when calling the API. PPI catalog and result responses then use opal_ppi, public example URLs, and Opal download filenames. Internal worker paths are omitted. If an existing result reader requires the legacy response fields, set AZULENE_LEGACY_TOOL_NAMES=1 before calling the SDK. API clients that omit the header retain the legacy response format. Previously generated archives retain their original contents.

Release dependency: publish the three v1/opal/opal_ppi/ example aliases and deploy the backend alias and response support before releasing these CLI/SDK changes. The catalog snapshot in this branch records source changes; it is not proof that a production deployment is ready.

Azulene Studio Job Types

Every tool below is generated from the live Azulene Studio catalog — the same definitions the web wizard and azulene jobs get-job-types read. Do not hand-edit this section; run python tools/regen_job_types_docs.py instead. The same content is kept standalone in Job_Types.md.

Field names are not shared between tools. For the same protein, absolute_binding takes pdb_file and docking takes structure_file. Copy each tool's own table.

This is the full submittable surface: fields that the Studio wizard or azulene jobs get-job-types hide as operational detail (platform, derived values) are documented here too.

You can fetch the same catalog live:

azulene jobs get-job-types              # id / name table
azulene jobs get-job-types --markdown   # this reference, regenerated

Overview

ID Name Category Description
protac_pose_prediction PROTAC Ternary Complex Pose Prediction Docking & Pose Generate ranked 3D models of a PROTAC ternary complex from a target–warhead structure, an E3…
generate_conformers 3D Molecular Conformer Generation Structure Generation Generate a diverse 3D conformer ensemble directly from SMILES for docking, shape and…
aqueous_solvation Absolute Hydration Free Energy Solvation & Permeability Returns the hydration free energy of a molecule or peptide in kcal/mol, computed by alchemical…
predict_protein_properties Protein and Peptide Developability Prediction Mutation & Design Predict key developability properties of a protein or peptide directly from its sequence…
relative_fe Relative Hydration Free Energy Solvation & Permeability Calculates the relative free energy between two similar molecules in water using alchemical…
relative_fe_uaa Relative Hydration Free Energy for ncAAs Solvation & Permeability Calculates the relative free energy between two (unnatural) amino acid structures in water…
solvent_transfer_free_energy Solvent Transfer Free Energy Solvation & Permeability Calculates the transfer free energy of a molecule (SMILES) or peptide (HELM) between two…
nonaqueous_solvation Absolute Solvation Free Energy (Non-Aqueous) Solvation & Permeability Calculates the absolute solvation free energy of a molecule (SMILES) or peptide (HELM) in a…
reaction_free_energy Aqueous Reaction Free Energy Reactivity & pKa Calculates the reaction free energy in aqueous solution.
deprotonation_fe Deprotonation Free Energy (pKa) Reactivity & pKa Estimate deprotonation free energy in water by combining gas-phase and solvation…
absolute_binding Absolute Binding Free Energy (ABFE) Binding Affinity Calculates the absolute binding free energy of a ligand to a protein in aqueous solution. The…
relative_binding Relative Binding Free Energy (RBFE) Binding Affinity Calculates the relative binding free energy between two ligands to a protein in aqueous…
lipid_permeation Lipid Bilayer Permeation Free Energy Solvation & Permeability Calculates the free energy of a molecule (SMILES) or peptide (HELM) permeating through a lipid…
lipid_permeation_rfe Relative Lipid Bilayer Permeation Free Energy Solvation & Permeability Calculates the relative free energy of permeation through a lipid bilayer between two…
covalent_docking Covalent Docking Docking & Pose Predict and refine 3D binding poses of a covalent inhibitor at a specified protein residue…
docking Docking Docking & Pose Physics-based docking of a ligand into a binding site you supply — there is no pocket finding…
sequential_docking Cofactor & Ligand Docking Docking & Pose Docks ligands, fragments or cofactors one at a time, each stage taking the previous result as…
opal_ml_optimize Protein-Ligand Pose Refinement Docking & Pose Refine a supplied protein–ligand or protein–peptide binding pose using the Opal ML model. The…
opal_ml_score Protein-Ligand Scoring Binding Affinity Evaluate a supplied protein–ligand pose using the Opal ML model, with no relaxation. For…
peptide_structure Peptide 3D Structure Generation (HELM) Structure Generation Generate 3D structures from HELM notation for linear and cyclic peptides, including…
protein_mutation_ddg_fold Protein Mutation Folding Stability (Physics-based ΔΔG) Protein Stability Predict the change in folding free energy on amino-acid mutation (natural AAs + 14 ncAAs: Aib…
boltz_prediction Boltz-2 Structure + Affinity Prediction Co-folding Co-fold up to 12 protein chains with up to 8 cofactors and 1 ligand using Boltz-2. The minimal…
opal_ppi Rank protein-protein interfaces using Opal Co-folding Predict and rank candidate protein-protein interfaces from two protein sequences. Returns…
boltz_macrocycle Macrocycle and Cyclic Peptide Binding Prediction Co-folding Folds a macrocyclic or stapled peptide against a receptor and scores the interface; the binder…
chai_prediction Chai-1 Complex Structure Prediction Co-folding All-atom co-folding with Chai-1 (Apache-2.0). Supports protein, RNA, DNA, and small-molecule…
openfold3_prediction OpenFold3 Complex Structure Prediction Co-folding AF3-parity all-atom structure prediction with OpenFold3 (Apache-2.0). Within experimental…
openfold2_prediction OpenFold2 Protein Structure Prediction Co-folding Single-chain and multimer protein structure prediction with OpenFold2 (Apache-2.0). Folds from…
mpnn_design ProteinMPNN Sequence Design (Inverse Folding) Mutation & Design Design new amino-acid sequences that fold to a backbone structure you provide (inverse…
mpnn_stability Protein Stability Prediction (ThermoMPNN) Protein Stability Predict how mutations change protein folding stability (ΔΔG). ThermoMPNN uses a protein…
esm2_embed ESM-2 Protein Sequence Embeddings Mutation & Design Turn protein sequences into ESM-2 embeddings — numeric vectors that capture each protein's…
esm2_mutation_score ESM-2 Zero-Shot Mutation Scoring Mutation & Design Score how point mutations affect a protein, with no training data needed. ESM-2 rates each…
esmfold_predict ESMFold Single-Chain Structure Prediction Structure Generation Single-chain structure prediction from sequence with ESMFold — no MSA, up to 1024 residues…
crystal_prediction Organic Crystal Structure Prediction (CSP) Structure Generation Predict organic crystal structures with Opal from either a SMILES string or an uploaded…

1. PROTAC Ternary Complex Pose Prediction (protac_pose_prediction)

Description: Generate ranked 3D models of a PROTAC ternary complex from a target–warhead structure, an E3 ligase–recruiter structure, and a linker SMILES. The workflow models target–E3 orientations compatible with the specified linker and returns up to ten ranked poses, with PDB coordinates for the PROTAC and the complete ternary complex alongside per-pose ranking metrics. When an experimental reference structure is provided, predictions are evaluated using PROTAC RMSD, binding-pocket RMSD, and combined PROTAC–pocket RMSD.

Category: Docking & Pose · Docs: https://docs.azulenelabs.com/tools/protac_pose_prediction/ · Submission modes: single

Input Schema

Field Type Required Default Description
job_id string no — Set by the platform. Names the per-job working directory on the shared volume; a value you supply is replaced, because two jobs under one name would share one directory.
target_pdb_content file yes — A co-structure: the target protein with the warhead present as a bound ligand in the same file.
e3_pdb_content file yes — A co-structure: the E3 ligase with the recruiter present as a bound ligand in the same file.
linker_smiles string yes — A SMILES fragment with two attachment points: [:1] bonds to the warhead, [:2] to the E3 recruiter.
warhead_attach_atom_idx integer yes — The warhead atom the linker bonds to. Count heavy atoms only, from 0, in the order they appear in the warhead ligand in the target PDB. Minimum 0.
recruiter_attach_atom_idx integer yes — The recruiter atom the linker bonds to. Count heavy atoms only, from 0, in the order they appear in the recruiter ligand in the E3 PDB. Minimum 0.
target_chain string no A One or more chain IDs of the target protein, comma separated: A, B, C.
e3_chain_label string no B One or more chain IDs of the E3 complex, comma separated: A, B, C.
warhead_chain string no X Chain ID of the warhead ligand inside the target PDB, the small molecule bound to the target protein. One chain only.
recruiter_chain string no Y Chain ID of the recruiter ligand inside the E3 PDB, the small molecule bound to the E3 ligase. One chain only.
preset string no medium Choose how densely to sample target–E3 orientations. Finer searches use more compute. One of quick, medium, long.
n_prescan_chunks integer no 8 Splits the search across this many machines running at once. Between 1 and 16, 8 recommended. Range 1–16.
warhead_smiles string no — The warhead's SMILES, used to set bond orders.
e3_anchor_smiles string no — The recruiter's SMILES, used to set bond orders.
gt_complex_pdb_content file no — Poses are measured against it by PROTAC, pocket, and combined RMSD.
gt_ligand_resname string no — The PROTAC's residue name in the reference file. Required once a reference is supplied.
gt_target_chain string no A Chain ID of the target protein in the reference file.
gt_e3_chain string no B Chain ID(s) of the E3 ligase in the reference file.
keep_dirs boolean no true Persist full pose / overlay / summary outputs as a downloadable ZIP (retrievable via opal jobs download). Disable for the scalar JSON summary only.

Example Input

{
  "target_pdb_content": "<local path or storage key>",
  "e3_pdb_content": "<local path or storage key>",
  "linker_smiles": "[*:1]CCOCCOCC[*:2]",
  "warhead_attach_atom_idx": 15,
  "recruiter_attach_atom_idx": 17,
  "target_chain": "A",
  "e3_chain_label": "B,C,D",
  "preset": "quick"
}

target_pdb_content, e3_pdb_content take a local path — the SDK uploads it and substitutes the storage key — or a storage key you already have. Check the Required column above for which of them you have to supply.

Featured Examples

Run one with azulene examples submit protac_pose_prediction <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
7jtp-wdr5-vhl-quick WDR5-VHL (7JTP) - quick Use this example to learn how the workflow models a flexible, open-chain degrader. The…
6sis-brd4-vhl-quick BRD4-VHL (6SIS) - quick Use this example to learn how the workflow models a conformationally constrained macrocyclic…
7jtp-wdr5-vhl WDR5-VHL (7JTP) - medium Use this example to learn how orientation-sampling resolution affects pose generation and how…
6sis-brd4-vhl BRD4-VHL (6SIS) - medium Use this example to learn why the workflow returns several ranked poses rather than a single…

2. 3D Molecular Conformer Generation (generate_conformers)

Description: Generate a diverse 3D conformer ensemble directly from SMILES for docking, shape and pharmacophore screening, and free-energy workflows. Explore accessible torsional space to capture binding-relevant geometries that a single lowest-energy structure may miss. Generate 1–100 conformers per run.

Category: Structure Generation · Docs: https://docs.azulenelabs.com/tools/generate_conformers/ · Submission modes: single, batch, workflow_node

Input Schema

Field Type Required Default Description
smiles string yes — SMILES notation of the molecule
num_conformers integer yes 5 Number of conformers to generate. Range 1–100.

Example Input

{
  "smiles": "CCO",
  "num_conformers": 5
}

Featured Examples

Run one with azulene examples submit generate_conformers <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
aspirin-conformers Aspirin conformers Use this example to learn how the workflow generates 3D conformers from a SMILES string…
ibuprofen-conformers Ibuprofen conformers Use this example to see how conformational flexibility affects the returned ensemble…
caffeine-conformers Caffeine conformers Use this example to see what the workflow returns for a rigid molecule. Caffeine is preloaded…

3. Absolute Hydration Free Energy (aqueous_solvation)

Description: Returns the hydration free energy of a molecule or peptide in kcal/mol, computed by alchemical decoupling in explicit water. This quantifies how favorably the compound is solvated by water, helping explain differences in solubility, partitioning, and the energetic cost of leaving water to bind a target or cross a membrane. Use it to compare analogs and investigate unexpected property predictions, especially for novel scaffolds where ML models may be unreliable. Provide exactly one of a SMILES, a HELM peptide, or an SDF — an uploaded SDF is simulated with the coordinates you supply, where the SMILES route builds its own conformer.

Category: Solvation & Permeability · Docs: https://docs.azulenelabs.com/tools/aqueous_solvation/ · Submission modes: single, batch, workflow_node

Input Schema

Field Type Required Default Description
smiles string no — The molecule as SMILES. Peptides can go here too if you have the SMILES. Provide this, helm, or sdf_file.
sdf_file file no — Molecule as an SDF — provide this OR smiles OR helm. Unlike docking, the coordinates in the file ARE the starting geometry: a docked pose, a QM-optimized conformer or a crystal ligand is simulated as supplied, where the SMILES path embeds a fresh ETKDG conformer and discards yours. assign_protonation_states does not apply to this input — the file's own protonation is used as given, so protonate it yourself first if you need a specific state. A multi-record SDF is rejected. Accepts .sdf.
helm string no — Peptides in HELM2 notation, for example PEPTIDE1{A.G.F.K.L}$$$$V2.0. Describes a peptide by residue rather than by atom, which is easier for cyclic, disulfide-bonded or non-canonical peptides. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
conformer_method string no etkdg Reserved for HELM input. This pipeline builds its own conformer for the alchemical calculation, so this does not currently change the result. One of etkdg, xtb.
assign_protonation_states boolean no true Automatically assigns the correct solute protonation state at the specified pH. Disable if your inputs are already protonated.
ph number no 7 pH value for protonation state
solvent_equil_length number no 0.08 Solvent equilibration length in nanoseconds per replica. Minimum 0.
solvent_prod_length number no 0.4 Solvent production length in nanoseconds per replica. Minimum 0.
vacuum_equil_length number no 0.08 Vacuum equilibration length in nanoseconds per replica. Minimum 0.
vacuum_prod_length number no 0.4 Vacuum production length in nanoseconds per replica. Minimum 0.
platform string no CUDA OpenMM compute platform for simulations. One of CUDA, OpenCL, CPU, Reference.
protocol_repeats integer no 3 Number of protocol repeats for uncertainty estimation. Minimum 1.
keep_dirs boolean no true Preserves full simulation outputs for download. Disable only if you don't need the raw data.

Example Input

{
  "smiles": "CCO"
}

Featured Examples

Run one with azulene examples submit aqueous_solvation <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
ethanol Hydration free energy of ethanol Use this example to learn how a hydration free energy calculation is set up and what it…
methanol-aqueous-solvation Methanol hydration Use this example to see the result for a small hydrogen-bonding molecule. Methanol is…
toluene-aqueous-solvation Toluene hydration Use this example to see the result for a hydrophobic aromatic. Toluene is preloaded; with no…

4. Protein and Peptide Developability Prediction (predict_protein_properties)

Description: Predict key developability properties of a protein or peptide directly from its sequence: solubility, aggregation, subcellular localization, intrinsic disorder, toxicity, melting temperature (Tm), and MHC binding (class I and II). Use it to triage or rank candidate sequences before committing to wet-lab work. Provide exactly one of a plain amino-acid sequence or a HELM string.

Category: Mutation & Design · Docs: https://docs.azulenelabs.com/tools/predict_protein_properties/ · Submission modes: single, batch, workflow_node

Input Schema

Field Type Required Default Description
sequence string no — The protein or peptide sequence to score, using the 20 standard amino acids (e.g. MKTAYIAKQRQ...). Provide this or HELM, not both. Sequences longer than 1022 residues are truncated, and anything over 2044 residues is rejected.
helm string no — Peptide in HELM2 notation, e.g. PEPTIDE1{A.G.F.K.L}$$$$V2.0. Converted to a plain sequence for this tool: bracketed non-canonical residues are removed from the sequence, not substituted - [Aib] and [dF] are deleted, so the peptide scored is one residue shorter for each. Cyclization is ignored, and only the first PEPTIDE block is read. Provide this or the sequence field. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
properties array no [] Pick which properties to predict. Leave empty to run the full panel (all of them). Items are any of solubility, aggregation, disorder, localization, toxicity, tm, mhc.
properties_options object no {} Optional per-property settings. Only MHC binding and localization take any; the other five properties have no options and none are shown for them. For example, {"mhc": {"peptide_length": 15, "mhc_class": "II"}} runs MHC binding for class II at the given peptide length. Leave empty to use sensible defaults.
return_per_residue boolean no false Also return per-residue scores where available (currently disorder and aggregation hotspots), so you can see which regions drive each prediction. Off by default; large results are delivered as a downloadable attachment.
return_embedding boolean no false Also return the protein's sequence embedding (a numeric fingerprint), handy for downstream similarity search or clustering. Off by default.
model_variant string no 650M Model size to run. 650M (default) is fast and accurate for most uses; 3B is a larger model for slightly higher accuracy at higher cost. One of 650M, 3B.

Example Input

{
  "sequence": "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAYLAKHIGCNHIGFRLT",
  "properties": [
    "solubility",
    "disorder",
    "tm"
  ]
}

Featured Examples

Run one with azulene examples submit predict_protein_properties <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
protein-properties-alpha-synuclein α-synuclein (intrinsic disorder) Disorder / aggregation / solubility heads on alpha-synuclein, the canonical intrinsically…
protein-properties-melittin Melittin (toxicity / solubility) Toxicity / solubility / localization heads on melittin, the textbook membrane-lysing…
protein-properties-ova-peptide OVA323-339 epitope (MHC-II + solubility) MHC class-II (via properties_options) plus solubility heads on the OVA323-339 model epitope.
ubiquitin Ubiquitin property profile Sequence-based ML property profile (solubility, aggregation, disorder, Tm) for human…
amyloid-beta-42 Amyloid-beta(1-42) profile Property profile for amyloid-beta(1-42), the aggregation-prone Alzheimer's peptide -…

5. Relative Hydration Free Energy (relative_fe)

Description: Calculates the relative free energy between two similar molecules in water using alchemical transformations.

Category: Solvation & Permeability · Docs: https://docs.azulenelabs.com/tools/relative_fe/ · Submission modes: single

Input Schema

Field Type Required Default Description
smiles_a string yes — SMILES string representation of the first molecule
smiles_b string yes — SMILES string representation of the second molecule
assign_protonation_states boolean no true Automatically assigns the correct solute protonation state at the specified pH. Disable if your inputs are already protonated.
ph number no 7 pH value for protonation state
equil_length number no 0.08 Equilibration length in nanoseconds per replica. Minimum 0.
prod_length number no 0.4 Production length in nanoseconds per replica. Minimum 0.
platform string no CUDA OpenMM compute platform for simulations. One of CUDA, OpenCL, CPU, Reference.
protocol_repeats integer no 3 Number of protocol repeats for uncertainty estimation. Minimum 1.
keep_dirs boolean no true Preserves full simulation outputs for download. Disable only if you don't need the raw data.

Example Input

{
  "smiles_a": "CCO",
  "smiles_b": "CCC"
}

Featured Examples

Run one with azulene examples submit relative_fe <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
ethanol-to-propane Relative FE: ethanol to propane Relative solvation free energy between ethanol and propane via a single-topology alchemical…
benzene-to-toluene-fe Benzene to toluene Relative hydration free energy for adding a methyl to benzene.
phenol-to-catechol-fe Phenol to catechol Relative hydration free energy for adding a hydroxyl to phenol.
methanol-to-ethanol-fe Methanol to ethanol relative FE Relative solvation free energy for methanol to ethanol — a single methyl-group alchemical…

6. Relative Hydration Free Energy for ncAAs (relative_fe_uaa)

Description: Calculates the relative free energy between two (unnatural) amino acid structures in water using alchemical transformations.

Category: Solvation & Permeability · Docs: https://docs.azulenelabs.com/tools/relative_fe_uaa/ · Submission modes: single

Input Schema

Field Type Required Default Description
sequence_a string yes — Peptide sequence, N to C. Natural amino acids are single uppercase letters, e.g. GAG. A non-canonical residue goes in angle brackets, either by library name (<pF-Phe>) or as SMILES (<NC(C)(C)C(=O)O>). If you would rather not repeat a long SMILES, put a lowercase placeholder in the sequence (GxG) and say what it stands for under Advanced Settings → Placeholder definitions.
sequence_b string yes — The second sequence, written the same way. The result is the free energy of B relative to A, so this is usually A with one residue substituted.
uaa_map string no — Only needed when a sequence contains a lowercase placeholder. Each row binds one letter to the residue it stands for. Writing the residue inline instead, as <pF-Phe>, needs nothing here.
assign_protonation_states boolean no true Automatically assigns the correct solute protonation state at the specified pH. Disable if your inputs are already protonated.
ph number no 7 pH value for protonation state
equil_length number no 0.08 Equilibration length in nanoseconds per replica. Minimum 0.
prod_length number no 0.4 Production length in nanoseconds per replica. Minimum 0.
platform string no CUDA OpenMM compute platform for simulations. One of CUDA, OpenCL, CPU, Reference.
protocol_repeats integer no 3 Number of protocol repeats for uncertainty estimation. Minimum 1.
keep_dirs boolean no true Preserves full simulation outputs for download. Disable only if you don't need the raw data.
uaa_map — one entry
Field Type Required Default Description
placeholder string yes — The letter exactly as it appears in sequence A or B: one lowercase letter, optionally followed by digits — x, a2.
residue string yes — What that letter stands for — a library name, or a SMILES for any residue not in the library.

Example Input

{
  "sequence_a": "YGH",
  "sequence_b": "<pF-Phe>GH"
}

Featured Examples

Run one with azulene examples submit relative_fe_uaa <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
ygh-pf-phe-scan ncAA scan: Tyr to pF-Phe in YGH Relative free energy of substituting tyrosine with the non-canonical amino acid…

7. Solvent Transfer Free Energy (solvent_transfer_free_energy)

Description: Calculates the transfer free energy of a molecule (SMILES) or peptide (HELM) between two solvents using alchemical transformations. Provide exactly one of 'smiles_solute' or 'helm'.

Category: Solvation & Permeability · Docs: https://docs.azulenelabs.com/tools/solvent_transfer_free_energy/ · Submission modes: single

Input Schema

Field Type Required Default Description
smiles_solute string no — SMILES string of the solute. Provide this OR 'helm'.
helm string no — Peptide in HELM2 notation, e.g. PEPTIDE1{A.G.F.K.L}$$$$V2.0. Single-letter residues, dot-separated; non-canonical residues in brackets (e.g. [Aib], [dF]). Provide this or the SMILES field. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
conformer_method string no etkdg Reserved for HELM input. This pipeline builds its own conformer for the alchemical calculation, so this does not currently change the result. One of etkdg, xtb.
smiles_solvent_a string yes — SMILES string representation of the first solvent. Use `None' for vacuum.
smiles_solvent_b string yes — SMILES string representation of the second solvent. Use `None' for vacuum.
assign_protonation_states boolean no true Automatically assigns the correct solute protonation state at the specified pH. Disable if your input is already protonated.
ph number no 7 pH value for protonation state
solvent_equil_length number no 0.08 Equilibration length in nanoseconds for actual-solvent leg(s). Minimum 0.
solvent_prod_length number no 0.4 Production length in nanoseconds for actual-solvent leg(s). Minimum 0.
vacuum_equil_length number no 0.08 Equilibration length in nanoseconds for the vacuum leg (when smiles_solvent_a or _b is 'None'). Minimum 0.
vacuum_prod_length number no 0.4 Production length in nanoseconds for the vacuum leg. Minimum 0.
platform string no CUDA OpenMM compute platform for simulations. One of CUDA, OpenCL, CPU, Reference.
protocol_repeats integer no 3 Number of independent replicas. Reported uncertainty combines per-replica uncertainty with the replica-to-replica spread. Minimum 1.
keep_dirs boolean no false Preserve scratch / output directories for download. Disable to save storage.

Example Input

{
  "smiles_solute": "CCO",
  "smiles_solvent_a": "None",
  "smiles_solvent_b": "O"
}

Featured Examples

Run one with azulene examples submit solvent_transfer_free_energy <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
ethanol-water-to-methanol Ethanol transfer: water to methanol Transfer free energy of ethanol from water to methanol, from the difference of absolute…
benzene-water-to-cyclohexane Benzene water to cyclohexane Transfer free energy of benzene between water and cyclohexane.
toluene-water-to-octanol Toluene water to octanol Transfer free energy of toluene between water and octanol (logP-type).

8. Absolute Solvation Free Energy (Non-Aqueous) (nonaqueous_solvation)

Description: Calculates the absolute solvation free energy of a molecule (SMILES) or peptide (HELM) in a non-aqueous solvent using alchemical transformations. Provide exactly one of 'smiles_solute' or 'helm'.

Category: Solvation & Permeability · Docs: https://docs.azulenelabs.com/tools/nonaqueous_solvation/ · Submission modes: single

Input Schema

Field Type Required Default Description
smiles_solute string no — SMILES string of the solute. Provide this OR 'helm'.
helm string no — Peptide in HELM2 notation, e.g. PEPTIDE1{A.G.F.K.L}$$$$V2.0. Single-letter residues, dot-separated; non-canonical residues in brackets (e.g. [Aib], [dF]). Provide this or the SMILES field. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
conformer_method string no etkdg Reserved for HELM input. This pipeline builds its own conformer for the alchemical calculation, so this does not currently change the result. One of etkdg, xtb.
smiles_solvent string yes — SMILES string of the organic solvent (e.g. CCO ethanol, CO methanol, CCCCCC hexane, CS(=O)C DMSO)
assign_protonation_states boolean no true Automatically assigns the correct solute protonation state at the specified pH. Disable if your input is already protonated.
ph number no 7 pH value for protonation state
solvent_equil_length number no 0.08 Solvent equilibration length in nanoseconds per replica. Minimum 0.
solvent_prod_length number no 0.4 Solvent production length in nanoseconds per replica. Minimum 0.
vacuum_equil_length number no 0.08 Vacuum equilibration length in nanoseconds per replica. Minimum 0.
vacuum_prod_length number no 0.4 Vacuum production length in nanoseconds per replica. Minimum 0.
platform string no CUDA OpenMM compute platform for simulations. One of CUDA, OpenCL, CPU, Reference.
protocol_repeats integer no 3 Number of independent replicas. Reported uncertainty combines per-replica uncertainty with the replica-to-replica spread. Minimum 1.
keep_dirs boolean no false Preserve scratch / output directories for download. Disable to save storage.

Example Input

{
  "smiles_solute": "CCO",
  "smiles_solvent": "CO"
}

Featured Examples

Run one with azulene examples submit nonaqueous_solvation <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
ethanol-in-methanol Solvation of ethanol in methanol Absolute solvation free energy of ethanol in methanol by alchemical decoupling (short demo…

9. Aqueous Reaction Free Energy (reaction_free_energy)

Description: Calculates the reaction free energy in aqueous solution.

Category: Reactivity & pKa · Docs: https://docs.azulenelabs.com/tools/reaction_free_energy/ · Submission modes: single

Input Schema

Field Type Required Default Description
smiles_reactant string yes — SMILES string representations of the reactants, separated by commas
smiles_product string yes — SMILES string representations of the products, separated by commas
stoichiometry_reactant string yes — Stoichiometry of the reactants, separated by commas
stoichiometry_product string yes — Stoichiometry of the products, separated by commas
solvent_equil_length number no 0.08 Solvent equilibration length in nanoseconds per replica. Minimum 0.
solvent_prod_length number no 0.4 Solvent production length in nanoseconds per replica. Minimum 0.
vacuum_equil_length number no 0.08 Vacuum equilibration length in nanoseconds per replica. Minimum 0.
vacuum_prod_length number no 0.4 Vacuum production length in nanoseconds per replica. Minimum 0.
platform string no CUDA OpenMM compute platform for simulations. One of CUDA, OpenCL, CPU, Reference.
protocol_repeats integer no 3 Number of protocol repeats for uncertainty estimation. Minimum 1.
use_xtb boolean no true Compute the gas-phase reaction term with xTB. Leave this off and the gas-phase term is omitted entirely: dg_reaction is then the solvation contribution alone, not a reaction free energy.
keep_dirs boolean no true Preserves full simulation outputs for download. Disable only if you don't need the raw data.

Example Input

{
  "smiles_reactant": "CC=O",
  "smiles_product": "C=CO",
  "stoichiometry_reactant": "1",
  "stoichiometry_product": "1",
  "use_xtb": true
}

Featured Examples

Run one with azulene examples submit reaction_free_energy <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
keto-enol-acetaldehyde Acetaldehyde keto-enol tautomerization Reaction free energy of the acetaldehyde keto-enol tautomerization (CH3CHO -> CH2=CHOH) in…
keto-enol-acetone Acetone keto-enol Aqueous keto-enol tautomerization free energy of acetone.
cis-trans-2-butene 2-butene cis to trans Aqueous cis to trans isomerization free energy of 2-butene.
co2-hydration CO2 hydration to carbonic acid Reaction free energy for the hydration of carbon dioxide to carbonic acid (CO2 + H2O -&gt…
cyclohexanone-keto-enol Cyclohexanone keto-enol tautomerization Keto-enol tautomerization free energy for cyclohexanone to cyclohex-1-en-1-ol in aqueous…

10. Deprotonation Free Energy (pKa) (deprotonation_fe)

Description: Estimate deprotonation free energy in water by combining gas-phase and solvation contributions. Provide the corresponding protonated and deprotonated forms. Use the SMILES fields for small molecules and the HELM fields for peptides.

Category: Reactivity & pKa · Docs: https://docs.azulenelabs.com/tools/deprotonation_fe/ · Submission modes: single

Input Schema

Field Type Required Default Description
smiles_prot string no — SMILES of the protonated species. Provide this OR 'helm_protonated'.
helm_protonated string no — HELM2 notation for the protonated peptide (HA), e.g. PEPTIDE1{A.G.F.K.L}$$$$V2.0. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
helm_deprotonated string no — HELM2 notation for the deprotonated peptide (A-), e.g. PEPTIDE1{A.G.F.K.L}$$$$V2.0. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
conformer_method string no etkdg Reserved for HELM input. This pipeline builds its own conformer for the alchemical calculation, so this does not currently change the result. One of etkdg, xtb.
smiles_deprot string no — SMILES of the deprotonated species. Provide this OR 'helm_deprotonated'.
solvent_equil_length number no 0.08 Solvent equilibration length in nanoseconds per replica. Minimum 0.
solvent_prod_length number no 0.4 Solvent production length in nanoseconds per replica. Minimum 0.
vacuum_equil_length number no 0.08 Vacuum equilibration length in nanoseconds per replica. Minimum 0.
vacuum_prod_length number no 0.4 Vacuum production length in nanoseconds per replica. Minimum 0.
platform string no CUDA OpenMM compute platform for simulations. One of CUDA, OpenCL, CPU, Reference.
protocol_repeats integer no 3 Number of protocol repeats for uncertainty estimation. Minimum 1.
use_xtb boolean no true Compute the gas-phase reaction term with xTB. Leave this off and the gas-phase term is omitted entirely: dg_reaction is then the solvation contribution alone, not a reaction free energy.
keep_dirs boolean no true Preserves full simulation outputs for download. Disable only if you don't need the raw data.

Example Input

{
  "smiles_prot": "C(=O)O",
  "smiles_deprot": "C(=O)[O-]",
  "use_xtb": true
}

Featured Examples

Run one with azulene examples submit deprotonation_fe <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
formic-acid Deprotonation FE of formic acid Deprotonation (pKa-related) free energy of formic acid to formate in water with xTB reference…
acetic-acid-deprotonation Acetic acid pKa Deprotonation free energy of acetic acid (experimental pKa about 4.76).
phenol-deprotonation Phenol pKa Deprotonation free energy of phenol (experimental pKa about 10).
imidazole-deprotonation Imidazole deprotonation (histidine analog) Deprotonation free energy of imidazole to imidazolate (neutral N-H to anion). Imidazole is the…

11. Absolute Binding Free Energy (ABFE) (absolute_binding)

Description: Calculates the absolute binding free energy of a ligand to a protein in aqueous solution. The shipped defaults run a short screening protocol — 13.2 ns of alchemical sampling in total, which on the systems measured here takes about 110 minutes and costs around 480 credits, 400 of which is a fixed charge on every run whatever the settings. That is roughly one hundredth of the sampling a published reference protocol for this method uses, so read the result as a ranking signal rather than a converged binding free energy.

Category: Binding Affinity · Docs: https://docs.azulenelabs.com/tools/absolute_binding/ · Submission modes: single, workflow_node

Input Schema

Field Type Required Default Description
pdb_file file yes — PDB file of the protein. May optionally contain the ligand
ligand_file file no — File of the ligand (SDF, PDB, or CIF format)
ligand_in_pdb_file boolean no false Specifies whether the ligand is contained within the provided protein PDB file
ligand_chain_id string no — Chain ID of the ligand, used only when the ligand is read from a PDB or CIF — ignored for an SDF ligand. Detected only when that structure IS the protein file ('ligand_in_pdb_file' on) and ends .pdb: largest non-water HETATM residue wins. Anywhere else chain B is assumed with nothing logged, so set it. Docs: https://docs.azulenelabs.com/tools/absolute_binding/
ligand_residue_name string no — Residue name of the ligand (the 3-letter code on its HETATM records), used only when the ligand is read from a PDB or CIF — ignored for an SDF ligand. Detected under the same one condition as ligand_chain_id, and where that is not met, LIG is assumed. Docs: https://docs.azulenelabs.com/tools/absolute_binding/
ligand_smiles string yes — SMILES string representation of the ligand
assign_protonation_states boolean no true Automatically assigns the correct ligand protonation state at the specified pH. Disable if your inputs are already protonated.
ph number no 7 pH value for protonation state
solvent_equil_length number no 0.02 Equilibration length for the ligand-in-water leg, in nanoseconds per replica. That leg runs 14 replicas, and the whole leg runs once per protocol repeat, so the default 0.02 ns with 3 repeats is 0.84 ns of equilibration in total. The same length is also used for a short conventional-MD equilibration that precedes the alchemical stage. Minimum 0.
solvent_prod_length number no 0.1 Production length for the ligand-in-water leg, in nanoseconds per replica. That leg runs 14 replicas, and the whole leg runs once per protocol repeat, so the default 0.1 ns with 3 repeats is 4.2 ns of sampling in total. Minimum 0.
complex_equil_length number no 0.02 Equilibration length for the ligand-in-complex leg, in nanoseconds per replica. That leg runs 30 replicas — more than the water leg, because the restraint that holds the ligand in the site is switched on over its own set of windows — and runs once per protocol repeat, so the default 0.02 ns with 3 repeats is 1.8 ns of equilibration in total. The same length is also used for a short conventional-MD equilibration that precedes the alchemical stage. Minimum 0.
complex_prod_length number no 0.1 Production length for the ligand-in-complex leg, in nanoseconds per replica. That leg runs 30 replicas, and runs once per protocol repeat, so the default 0.1 ns with 3 repeats is 9 ns of sampling in total — the largest single contributor to both the run time and the metered part of the bill. Minimum 0.
platform string no CUDA OpenMM compute platform for simulations. One of CUDA, OpenCL, CPU, Reference.
protocol_repeats integer no 3 Number of protocol repeats for uncertainty estimation. Minimum 1.
keep_dirs boolean no true Preserves full simulation outputs for download. Disable only if you don't need the raw data.

Example Input

{
  "pdb_file": "<local path or storage key>",
  "ligand_file": "<local path or storage key>",
  "ligand_smiles": "CCO"
}

pdb_file, ligand_file take a local path — the SDK uploads it and substitutes the storage key — or a storage key you already have. Check the Required column above for which of them you have to supply.

Featured Examples

Run one with azulene examples submit absolute_binding <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
hif2a-belzutifan HIF-2α + belzutifan-class inhibitor ABFE with a PT-2385-class HIF-2α inhibitor. HIF-2α inhibition is the mechanism of FDA-approved…
cdk2-lig-1h1q CDK2 inhibitor (ABFE) Absolute binding free energy of a neutral aminopyrimidine inhibitor to CDK2, from the OpenFE…
pfkfb3-lig-24 PFKFB3 inhibitor (ABFE) Absolute binding free energy of a neutral inhibitor to PFKFB3, from the OpenFE benchmark…

12. Relative Binding Free Energy (RBFE) (relative_binding)

Description: Calculates the relative binding free energy between two ligands to a protein in aqueous solution.

Category: Binding Affinity · Docs: https://docs.azulenelabs.com/tools/relative_binding/ · Submission modes: single

Input Schema

Field Type Required Default Description
pdb_file file yes — Upload a PDB file or fetch from RCSB by ID
ligand_file_a file no — File of the first ligand (SDF, PDB, or CIF format)
ligand_file_b file no — File of the second ligand (SDF, PDB, or CIF format)
ligand_in_pdb_file_a boolean no false Specifies whether the first ligand is contained within the provided protein PDB file
ligand_in_pdb_file_b boolean no false Specifies whether the second ligand is contained within the provided protein PDB file
ligand_residue_name_a string no LIG Residue name of the first ligand in the PDB file
ligand_residue_name_b string no LIG Residue name of the second ligand in the PDB file
smiles_a string yes — SMILES string representation of the first ligand
smiles_b string yes — SMILES string representation of the second ligand
assign_protonation_states boolean no true Automatically assigns the correct ligand protonation state at the specified pH. Disable if your inputs are already protonated.
ph number no 7 pH value for protonation state
equil_length number no 0.08 Equilibration length in nanoseconds per replica. Minimum 0.
prod_length number no 0.4 Production length in nanoseconds per replica. Minimum 0.
platform string no CUDA OpenMM compute platform for simulations. One of CUDA, OpenCL, CPU, Reference.
protocol_repeats integer no 3 Number of protocol repeats for uncertainty estimation. Minimum 1.
keep_dirs boolean no true Preserves full simulation outputs for download. Disable only if you don't need the raw data.

Example Input

{
  "pdb_file": "<local path or storage key>",
  "ligand_file_a": "<local path or storage key>",
  "ligand_file_b": "<local path or storage key>",
  "smiles_a": "CCO",
  "smiles_b": "CCC"
}

pdb_file, ligand_file_a, ligand_file_b take a local path — the SDK uploads it and substitutes the storage key — or a storage key you already have. Check the Required column above for which of them you have to supply.

Featured Examples

Run one with azulene examples submit relative_binding <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
tyk2-ejm-31-ejm-50 TYK2 ejm_31 → ejm_50 (RBFE benchmark) RBFE between two TYK2 inhibitors from the OpenFreeEnergy benchmark (doi…
cdk2-lig-21-lig-22 CDK2 pair (RBFE) Relative binding free energy between two neutral CDK2 inhibitors from the OpenFE benchmark.
pfkfb3-lig-24-lig-33 PFKFB3 pair (RBFE) Relative binding free energy between two neutral PFKFB3 inhibitors from the OpenFE benchmark.

13. Lipid Bilayer Permeation Free Energy (lipid_permeation)

Description: Calculates the free energy of a molecule (SMILES) or peptide (HELM) permeating through a lipid bilayer using umbrella sampling. Provide exactly one of 'smiles' or 'helm'.

Category: Solvation & Permeability · Docs: https://docs.azulenelabs.com/tools/lipid_permeation/ · Submission modes: single

Input Schema

Field Type Required Default Description
smiles string no — SMILES string of the solute. Provide this OR 'helm'.
helm string no — Peptide in HELM2 notation, e.g. PEPTIDE1{A.G.F.K.L}$$$$V2.0. Single-letter residues, dot-separated; non-canonical residues in brackets (e.g. [Aib], [dF]). Provide this or the SMILES field. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
conformer_method string no etkdg Reserved for HELM input. This pipeline builds its own conformer for the alchemical calculation, so this does not currently change the result. One of etkdg, xtb.
lipid_type string yes — Lipid bilayer to permeate. Runs at 298.15 K. Fluid at that temperature: DOPC, DLPC (dilauroyl 12:0, not dilinoleoyl), POPC. Near their transition: DMPC (Tm 24 C), POPE (25 C). Below their transition and therefore gel-phase, so not comparable to literature fluid-phase permeabilities: DLPE (29 C), DPPC (41 C). One of DPPC, DMPC, DOPC, DLPE, DLPC, POPE, POPC.
equil_length number no 0.08 Equilibration length in nanoseconds per replica. Minimum 0.
prod_length number no 0.4 Production length in nanoseconds per replica. Minimum 0.
platform string no CUDA OpenMM compute platform for simulations. One of CUDA, OpenCL, CPU, Reference.
keep_dirs boolean no true Preserves full simulation outputs for download. Disable only if you don't need the raw data.

Example Input

{
  "smiles": "O",
  "lipid_type": "POPC"
}

Featured Examples

Run one with azulene examples submit lipid_permeation <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
ethanol-popc Ethanol permeation across POPC Membrane permeation free-energy profile of ethanol across a POPC lipid bilayer (short demo…

14. Relative Lipid Bilayer Permeation Free Energy (lipid_permeation_rfe)

Description: Calculates the relative free energy of permeation through a lipid bilayer between two molecules (SMILES) using an alchemical perturbation. Provide both 'smiles_a' and 'smiles_b'.

Category: Solvation & Permeability · Docs: https://docs.azulenelabs.com/tools/lipid_permeation_rfe/ · Submission modes: single

Input Schema

Field Type Required Default Description
smiles_a string yes — SMILES string of the first molecule.
smiles_b string yes — SMILES string of the second molecule (a small modification of the first works best).
lipid_type string yes — Lipid bilayer to permeate. Runs at 298.15 K. Fluid at that temperature: DOPC, DLPC (dilauroyl 12:0, not dilinoleoyl), POPC. Near their transition: DMPC (Tm 24 C), POPE (25 C). Below their transition and therefore gel-phase, so not comparable to literature fluid-phase permeabilities: DLPE (29 C), DPPC (41 C). One of DPPC, DMPC, DOPC, DLPE, DLPC, POPE, POPC.
equil_length number no 0.08 Equilibration length in nanoseconds per replica. Minimum 0.
prod_length number no 0.4 Production length in nanoseconds per replica. Minimum 0.
ion_concentration number no 0 Salt concentration in molar for the simulation box. Minimum 0.
platform string no CUDA OpenMM compute platform for simulations. One of CUDA, OpenCL, CPU, Reference.
protocol_repeats integer no 3 Number of independent repeats of the alchemical protocol. Minimum 1.
keep_dirs boolean no true Preserves full simulation outputs for download. Disable only if you don't need the raw data.

Example Input

{
  "smiles_a": "CCO",
  "smiles_b": "CCCO",
  "lipid_type": "POPC"
}

Featured Examples

Run one with azulene examples submit lipid_permeation_rfe <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
ethanol-to-propanol-popc Ethanol to propanol (POPC) Relative permeation free energy across a POPC bilayer for the single-methyl edit from ethanol…
methanol-to-ethanol-popc Methanol to ethanol (POPC) Relative permeation free energy across a POPC bilayer for the smallest alcohol edit, methanol…
benzene-to-toluene-popc Benzene to toluene (POPC) Relative permeation free energy across a POPC bilayer for adding a methyl to benzene.

15. Covalent Docking (covalent_docking)

Description: Predict and refine 3D binding poses of a covalent inhibitor at a specified protein residue, then evaluate the poses using the Opal ML model. Provide the protein structure, inhibitor SMILES, and the reactive residue's chain, name, number, and atom. Supports warheads including boronic acids, acrylamides, nitriles, and sulfonyl fluorides.

Category: Docking & Pose · Docs: https://docs.azulenelabs.com/tools/covalent_docking/ · Submission modes: single

Input Schema

Field Type Required Default Description
structure_file file yes — Protein structure file (PDB or CIF format)
ligand_smiles string yes — SMILES string of the covalent inhibitor (must contain a reactive warhead)
drug_smiles string no — Legacy name for 'ligand_smiles'. Accepted and rewritten onto 'ligand_smiles' on submission, so existing scripts, saved workflows and chained jobs keep working; new callers should use 'ligand_smiles'.
chain_id string yes — Chain identifier in the protein structure (e.g., 'A')
target_resname string yes — Three-letter name of the reactive residue. One of SER, CYS, LYS, THR, HIS, TYR.
target_resid integer yes — Sequence number of the reactive residue
target_atom string yes — Reactive atom name (e.g. OG for SER, SG for CYS). Required: it is not auto-detected, and omitting it makes the whole job run as ordinary non-covalent docking with no error - the same silent downgrade.
covalent_element string no B Element of the atom that actually reacts, not the warhead's most conspicuous heteroatom - a vinyl sulfone reacts at the beta-carbon, so choose C. Note the default here is B (boronic acid); sequential_docking defaults the same field to C. One of B, C, S, P.
warhead_smarts string no — Custom SMARTS pattern for warhead detection, with a :1 atom map marking the reactive atom. Accepted for request-shape compatibility and not used; the warhead is detected from built-in patterns and cannot be overridden. Docs: https://docs.azulenelabs.com/tools/covalent_docking/
protonate boolean no true Assign physiological protonation state at the target pH. Accepted for request-shape compatibility and not used — it does not change the result. The receptor and ligand are always protonated.
ph number no 7.4 pH for protonation state assignment. Accepted for request-shape compatibility and not used — it does not change the result. Protonation always runs at pH 7.4. Range 0–14.
placement string no combined Ligand placement strategy. 'combined' is most thorough. Accepted for request-shape compatibility and not used — it does not change the result. One of combined, directed, tetrahedral.
max_steps integer no 700 Maximum optimization steps per orientation trial. Accepted for request-shape compatibility and not used — it does not change the result. Range 100–2000.
keep_cofactors string no — Comma-separated cofactor residue names to retain, e.g. 'ZN,NDP'; empty strips every non-standard residue. Waters go unless you add HOH. Organic cofactors are scored as neutral, so an anionic cofactor offsets absolute dG while ranking within one receptor is unaffected. Docs: https://docs.azulenelabs.com/tools/covalent_docking/
extra_chains string no — Comma-separated additional chain IDs to include (e.g. 'B' or 'B,C').
optimize_and_score boolean no false Chain geometry optimization and ML scoring straight after covalent docking. Accepted for request-shape compatibility and not used — it does not change the result. Docs: https://docs.azulenelabs.com/tools/covalent_docking/
opt_fmax number no 0.05 Convergence force threshold (eV/Å) for pose refinement. Only used when Optimize + Score is enabled. Accepted for request-shape compatibility and not used — it does not change the result. Range 0.005–1.
opt_maxiter integer no 200 Maximum optimization iterations per pose. Only used when Optimize + Score is enabled. Accepted for request-shape compatibility and not used — it does not change the result. Range 10–2000.
top_k integer no 5 Number of top-ranked covalent poses to refine and score. Only used when Optimize + Score is enabled. Accepted for request-shape compatibility and not used — it does not change the result. Range 1–20.
n_conformers integer no — Classical arm sample count: how many ligand conformers are enumerated in the pocket. Each is tried in many orientations (~60 trials per conformer), so classical runtime scales roughly linearly with it. Leave empty for the automatic default — 8 for a small molecule, 12 when cyclic-peptide/macrocycle sampling is on. A value here overrides the macrocycle default too, so leave it empty unless you mean to. Docs: https://docs.azulenelabs.com/tools/docking/. Range 1–64.
n_generative_samples integer no — Generative arm sample count: how many poses OpalFM diffuses. This is GPU time and is the largest cost knob on the job. Leave empty for the automatic default — 30 for a small molecule, 48 when cyclic-peptide/macrocycle sampling is on. Set 0 to switch the generative arm OFF and dock with the classical arm alone: much cheaper, and it gives up the arm that finds near-native poses the classical score ranks low. Docs: https://docs.azulenelabs.com/tools/docking/. Range 0–256.
n_poses_per_arm integer no — Pose refinement pool size: how many poses from each arm are geometry-optimized and evaluated using the Opal ML model. The two arms are ranked separately by their own native metric, so the pool is 2 x this number — 3 is the 6-pose table you see in the results. Raising it searches more of what the arms already generated and costs one additional optimization with the Opal ML model per pose, which is the expensive end of the job. Leave empty for the automatic default — 3 per arm. Docs: https://docs.azulenelabs.com/tools/covalent_docking/. Range 1–10.
crop_receptor boolean no false Crop the receptor to the binding site before docking. Off (the default) uses the uploaded receptor exactly as given, so a pre-cropped receptor flows unchanged through the pipeline. On runs one Arm-E iterative crop (as in the docking pipeline), seeded by the covalent target residue, shared by both arms. Docs: https://docs.azulenelabs.com/tools/covalent_docking/

Example Input

{
  "structure_file": "<local path or storage key>",
  "ligand_smiles": "OB(O)c1ccccc1",
  "chain_id": "A",
  "target_resname": "SER",
  "target_resid": 70,
  "target_atom": "OG",
  "covalent_element": "B"
}

structure_file takes a local path — the SDK uploads it and substitutes the storage key — or a storage key you already have. Check the Required column above for which of them you have to supply.

Featured Examples

Run one with azulene examples submit covalent_docking <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
mpro-paxlovid Paxlovid → SARS-CoV-2 Mpro (Cys145) Covalently docks Paxlovid (nirmatrelvir) into SARS-CoV-2 main protease, forming the C–S…
proteasome-bortezomib Bortezomib → 20S proteasome (Thr1) Covalently docks the boronic-acid drug bortezomib (Velcade) to the catalytic N-terminal Thr1…
kras-g12c KRAS G12C → Cys12 (chloroacetamide) Covalently docks a research-class GTP-competitive chloroacetamide inhibitor to the oncogenic…

16. Docking (docking)

Description: Physics-based docking of a ligand into a binding site you supply — there is no pocket finding. Orientations are minimized in the pocket, with a dedicated arm for macrocycles. The center is in Angstroms in the uploaded structure's own frame. Which model scores the pose depends on the ligand: macrocycles and cyclic peptides go to Opal-Dock v3 (confidence: macrocycle), everything else to a regressor fitted on covalent complexes (noncovalent_low_confidence). Both are ranking scores, not affinities.

Category: Docking & Pose · Docs: https://docs.azulenelabs.com/tools/docking/ · Submission modes: single, batch, workflow_node

Input Schema

Field Type Required Default Description
structure_file file yes — Protein structure file (PDB or CIF format)
ligand_smiles string no — SMILES string of the ligand or cofactor to dock. Provide this OR 'helm'.
drug_smiles string no — Legacy name for 'ligand_smiles'. Accepted and rewritten onto 'ligand_smiles' on submission, so existing scripts, saved workflows and chained jobs keep working; new callers should use 'ligand_smiles'.
helm string no — Cyclic or linear peptide in HELM2 notation, e.g. PEPTIDE1{A.G.F.K.L}$$$$V2.0 — provide this OR 'ligand_smiles'. Declare ring closures in the connection section (head-to-tail 1:R1-N:R2; disulfide or side-chain bridge i:R3-j:R3): a declared macrocycle is docked with the cyclic-peptide sampling arm, and a ring you do not declare cannot be detected. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
ligand_file file no — Ligand as an SDF, MOL, MOL2 or PDB file — provide this OR ligand_smiles OR helm. Only the molecule is taken from it, not its coordinates: docking builds its own conformers in the pocket. A multi-record SDF docks the first record only. Docs: https://docs.azulenelabs.com/tools/docking/. Accepts .sdf,.mol,.mol2,.pdb.
conformer_method string no etkdg Reserved for HELM input. The docking pipeline builds its own conformer ensemble in the pocket, so this does not currently change the result. One of etkdg, xtb.
macrocycle_sampling string no auto Whether to dock with the macrocycle sampling arm. 'auto', the default, turns it on for a flexible ring larger than 8 atoms, which an ordinary small molecule never triggers; it is markedly slower. 'off' forces the small-molecule settings. Docs: https://docs.azulenelabs.com/tools/docking/. One of auto, on, off.
chain_id string yes — Chain identifier in the protein structure (e.g., 'A')
binding_site_center string yes — Pocket center in Angstroms, in the coordinate frame of the uploaded structure. This is the only input that steers the docking. The wizard can average binding_site_residues into it for you.
binding_site_residues string no — Pocket residues as (name, number) pairs, used only to fill in binding_site_center — it has NO effect once a center is set, which is required. Numbers are as written in the file, matched across every chain and ignoring insertion codes. Docs: https://docs.azulenelabs.com/tools/docking/
placement_radius number no 8 Radius (Angstroms) of the random placement sphere around the binding site center. Accepted for request-shape compatibility and not used — it does not change the result. Range 3–20.
n_orientations integer no 8 Number of random orientations per conformer. Accepted for request-shape compatibility and not used — it does not change the result. Range 1–50.
keep_cofactors string no — Comma-separated cofactor residue names to retain, e.g. 'ZN,NDP'; empty strips every non-standard residue. Waters go unless you add HOH. Organic cofactors are scored as neutral, so an anionic cofactor offsets absolute dG while ranking within one receptor is unaffected. Docs: https://docs.azulenelabs.com/tools/docking/
fmax number no 0.5 Force convergence threshold (eV/Ang). Accepted for request-shape compatibility and not used — it does not change the result.
max_steps integer no 700 Maximum optimization steps per trial. Accepted for request-shape compatibility and not used — it does not change the result. Range 100–2000.
use_electrostatics boolean no true Enable Coulomb interactions in classical scoring. Accepted for request-shape compatibility and not used — it does not change the result. Electrostatics are always on; setting this false does not disable them.
protonate boolean no true Assign ligand protonation state at the target pH. Accepted for request-shape compatibility and not used — it does not change the result. The receptor and ligand are always protonated.
ph number no 7.4 pH for ligand protonation state assignment. Accepted for request-shape compatibility and not used — it does not change the result. Protonation always runs at pH 7.4. Range 0–14.
n_conformers integer no — Classical arm sample count: how many ligand conformers are enumerated in the pocket. Each is tried in many orientations (~60 trials per conformer), so classical runtime scales roughly linearly with it. Leave empty for the automatic default — 8 for a small molecule, 12 when cyclic-peptide/macrocycle sampling is on. A value here overrides the macrocycle default too, so leave it empty unless you mean to. Docs: https://docs.azulenelabs.com/tools/docking/. Range 1–64.
n_generative_samples integer no — Generative arm sample count: how many poses OpalFM diffuses. This is GPU time and is the largest cost knob on the job. Leave empty for the automatic default — 30 for a small molecule, 48 when cyclic-peptide/macrocycle sampling is on. Set 0 to switch the generative arm OFF and dock with the classical arm alone: much cheaper, and it gives up the arm that finds near-native poses the classical score ranks low. Docs: https://docs.azulenelabs.com/tools/docking/. Range 0–256.
n_poses_per_arm integer no — Pose refinement pool size: how many poses from each arm are geometry-optimized and evaluated using the Opal ML model. The two arms are ranked separately by their own native metric, so the pool is 2 x this number — 3 is the 6-pose table you see in the results. Raising it searches more of what the arms already generated and costs one additional optimization with the Opal ML model per pose, which is the expensive end of the job. Leave empty for the automatic default — 3 per arm. Docs: https://docs.azulenelabs.com/tools/docking/. Range 1–10.
crop_receptor boolean no false Crop the receptor to the binding site before docking. Off (the default) uses the uploaded receptor exactly as given, so a pre-cropped receptor flows unchanged through docking → optimize → score. On runs one Arm-E iterative crop (as in the docking pipeline), seeded by the binding site and shared by both docking arms. Docs: https://docs.azulenelabs.com/tools/docking/
binding_site_residues — one entry
Field Type Required Default Description
resname string yes — Three-letter residue name, e.g. SER
resid integer yes — Residue number as written in the uploaded file

Example Input

{
  "structure_file": "<local path or storage key>",
  "ligand_smiles": "c1ccc(cc1)C(=N)N",
  "chain_id": "A",
  "binding_site_center": "[12.5, 8.3, -4.1]",
  "binding_site_residues": "[[\"BEN\",1]]"
}

structure_file takes a local path — the SDK uploads it and substitutes the storage key — or a storage key you already have. Check the Required column above for which of them you have to supply.

Featured Examples

Run one with azulene examples submit docking <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
t4-lysozyme-ethylbenzene Ethylbenzene → T4 lysozyme L99A cavity Non-covalent self-docking of ethylbenzene into the engineered hydrophobic cavity of T4…
dhfr-trimethoprim Trimethoprim → DHFR (keep NADPH) Cofactor-aware non-covalent docking of the antifolate antibiotic trimethoprim into human…
carbonic-anhydrase-sulfanilamide Sulfanilamide → carbonic anhydrase II (keep Zn²⁺) Cofactor-aware non-covalent docking of the prototypical sulfonamide inhibitor sulfanilamide…
er-estradiol 17-beta-estradiol to estrogen receptor alpha Non-covalent docking of the endogenous hormone 17-beta-estradiol into the ligand-binding…
streptavidin-cyclic-hexapeptide Cyclic hexapeptide → streptavidin (HELM, disulfide) Dock a disulfide-cyclized hexapeptide, Ac-CHPQFC-NH2, into streptavidin (PDB 1SLD). The binder…
spsb2-cyclic-rgd cyclo-RGDINNNV → SPSB2 (HELM, head-to-tail) Dock a head-to-tail cyclized octapeptide into the SPRY domain of SPSB2 (PDB 5XN3). Closed by a…
streptavidin-cyclic-hexapeptide-sdf Cyclic hexapeptide → streptavidin (SDF ligand file) The same binder and the same pocket as 'Cyclic hexapeptide → streptavidin', supplied as a 3D…

17. Cofactor & Ligand Docking (sequential_docking)

Description: Docks ligands, fragments or cofactors one at a time, each stage taking the previous result as a frozen receptor; covalent and non-covalent stages may be mixed. Every non-covalent stage needs its own binding_site_center or is rejected at submission.

Category: Docking & Pose · Docs: https://docs.azulenelabs.com/tools/sequential_docking/ · Submission modes: single

Input Schema

Field Type Required Default Description
structure_file file yes — Protein structure file (PDB or CIF format)
chain_id string yes — Chain identifier in the protein structure (e.g., 'A')
stages string yes — JSON array of docking stages, run in order — each stage docks into the previous stages' frozen ligands. Each stage: {smiles, binding_site_center?, label?}. For a covalent stage add is_covalent:true plus anchor_resname, anchor_resid and anchor_atom_name.
keep_cofactors string no — Comma-separated cofactor residue names to retain, e.g. 'ZN,NDP'; empty strips every non-standard residue. Waters go unless you add HOH. Organic cofactors are scored as neutral, so an anionic cofactor offsets absolute dG while ranking within one receptor is unaffected. Docs: https://docs.azulenelabs.com/tools/sequential_docking/
n_conformers integer no — Classical arm sample count: how many ligand conformers are enumerated in the pocket. Each is tried in many orientations (~60 trials per conformer), so classical runtime scales roughly linearly with it. Applies to every stage — each one runs the same two arms. Leave empty for the automatic default — 8 for a small molecule, 12 when cyclic-peptide/macrocycle sampling is on. A value here overrides the macrocycle default too, so leave it empty unless you mean to. Docs: https://docs.azulenelabs.com/tools/docking/. Range 1–64.
n_generative_samples integer no — Generative arm sample count: how many stages OpalFM diffuses. This is GPU time and is the largest cost knob on the job. Applies to every stage — each one runs the same two arms. Leave empty for the automatic default — 30 for a small molecule, 48 when cyclic-peptide/macrocycle sampling is on. Set 0 to switch the generative arm OFF and dock with the classical arm alone: much cheaper, and it gives up the arm that finds near-native poses the classical score ranks low. Docs: https://docs.azulenelabs.com/tools/docking/. Range 0–256.
n_poses_per_arm integer no — Pose refinement pool size: how many poses from each arm are geometry-optimized and evaluated using the Opal ML model. The two arms are ranked separately by their own native metric, so the pool is 2 x this number — 3 is the 6-pose table you see in the results. Raising it searches more of what the arms already generated and costs one additional optimization with the Opal ML model per pose, which is the expensive end of the job. Applies to the final stage only — every earlier stage freezes exactly one winning ligand into the receptor for the next, so a wider pool there has nothing to choose between. Leave empty for the automatic default — 3 per arm. Docs: https://docs.azulenelabs.com/tools/sequential_docking/. Range 1–10.
crop_receptor boolean no false Crop the receptor to the binding site before docking. Off (the default) uses the uploaded receptor exactly as given, so a pre-cropped receptor flows unchanged through the pipeline. On runs one Arm-E iterative crop (as in the docking pipeline), seeded by the first stage's binding site (applied once, across all stages), shared by both arms. Docs: https://docs.azulenelabs.com/tools/sequential_docking/
stages — one entry
Field Type Required Default Description
smiles string yes — Ligand SMILES for this stage
binding_site_center array no — Pocket center for this stage. Omit to reuse the anchor-atom coordinate.
label string no — Name for this stage in the results
is_covalent boolean no — Bond this ligand covalently to an anchor residue
anchor_resname string no — Three-letter name of the reactive residue. One of SER, CYS, LYS, THR, HIS, TYR.
anchor_resid integer no — Sequence number of the reactive residue, in the numbering of the uploaded file
anchor_atom_name string no — Reactive atom name (e.g. OG for SER, SG for CYS)
covalent_element string no C Element of the atom that actually reacts, not the warhead's most conspicuous heteroatom - a vinyl sulfone reacts at the beta-carbon, so choose C. One of B, C, S, P.
bond_type string no — Derived as <warhead>-<target element>, recomputed from the covalent inputs. Only the warhead half is read downstream (bond_type.split("-")[0]); the target half is informational.

Example Input

{
  "structure_file": "<local path or storage key>",
  "chain_id": "A",
  "stages": "[{\"smiles\":\"CCO\",\"binding_site_center\":[12.5,8.3,-4.1],\"label\":\"fragment\"},{\"smiles\":\"OB(O)c1ccccc1\",\"is_covalent\":true,\"anchor_resname\":\"SER\",\"anchor_resid\":70,\"anchor_atom_name\":\"OG\",\"bond_type\":\"B-O\"}]",
  "keep_cofactors": "ZN"
}

structure_file takes a local path — the SDK uploads it and substitutes the storage key — or a storage key you already have. Check the Required column above for which of them you have to supply.

Featured Examples

Run one with azulene examples submit sequential_docking <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
carbonic-anhydrase-fragments Sulfonamide + hydrophobic fragment → carbonic anhydrase II (keep Zn²⁺) Two-stage fragment-based co-docking into human carbonic anhydrase II (PDB 3HS4), keeping the…
hsp90-fragment-growing Resorcinol → isoxazole growing in the HSP90 ATP pocket Two-stage fragment growing in the HSP90-alpha N-terminal ATP pocket (PDB 2XJX) — the…
bcl-xl-fragment-codocking Fragment co-docking → Bcl-xL BH3 groove Two-stage fragment-based co-docking into the Bcl-xL BH3 groove (PDB 2YXJ) — the SAR-by-NMR…
t4-lysozyme-fragments Two-stage: benzene then toluene to T4 lysozyme Two-stage sequential docking into the T4 lysozyme L99A cavity (PDB 4W52): dock benzene, then…

18. Protein-Ligand Pose Refinement (opal_ml_optimize)

Description: Refine a supplied protein–ligand or protein–peptide binding pose using the Opal ML model. The tool relaxes the bound ligand or peptide within the receptor, then evaluates the refined pose.

Category: Docking & Pose · Docs: https://docs.azulenelabs.com/tools/opal_ml_optimize/ · Submission modes: single

Input Schema

Field Type Required Default Description
protein_file file no — Protein structure (PDB/CIF).
ligand_file file no — Ligand structure (SDF, positioned in protein frame)
ligand_resname string no — HETATM residue name of ligand in PDB
ligand_smiles string no — Override for the binder's chemistry. Normally leave it empty: bond orders, protonation and charges are read from the structure. Set it only when perception is wrong, or the pose is a bare heavy-atom skeleton. A SMILES whose formula disagrees with the structure is refused, not applied. Docs: https://docs.azulenelabs.com/tools/opal_ml_optimize/
helm string no — Cyclic or linear peptide binder in HELM2 notation, e.g. PEPTIDE1{R.G.D.I.N.N.N.V}$PEPTIDE1,PEPTIDE1,1:R1-8:R2$$$V2.0 — an alternative to 'ligand_smiles' for the same chemistry. Prefer it for a macrocycle: the equivalent 24-membered SMILES is not writable by hand. It names the chemistry, not the pose — this job scores the geometry you supply. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
drug_smiles string no — Legacy name for 'ligand_smiles'. Accepted and rewritten onto 'ligand_smiles' on submission, so existing scripts, saved workflows and chained jobs keep working; new callers should use 'ligand_smiles'. Supply at most one of 'ligand_smiles' and 'helm'.
chain_id string no — Chain ID of the receptor — the target the binder is optimized against. See binder_chain for the other side of a two-chain complex.
binder_chain string no — Chain ID of the binder, as opposed to chain_id, which names the receptor. Supplying it switches to the peptide-complex path. Passing a peptide binder as chain_id instead does not raise an error — it takes the small-molecule path and returns an energy that is not meaningful. Docs: https://docs.azulenelabs.com/tools/opal_ml_optimize/
opt_fmax number no 0.05 Force convergence threshold (eV/Ang) for the in-pocket relaxation. Smaller means a tighter converged geometry and more steps.
opt_maxiter integer no 200 Maximum relaxation steps within the binding pocket. If this limit is reached before convergence, the structure reached at the final step is scored.
flexible_radius number no 0 Let receptor side chains within this many Angstrom of the binder relax; 0, the default, keeps the whole receptor rigid, and 4 is a sensible pocket shell. The resulting dG is not comparable with a rigid-pocket run — it is an induced-fit energy and comes out systematically more negative. Docs: https://docs.azulenelabs.com/tools/opal_ml_optimize/. Range 0–12.
flexible_residues string no — Receptor residues to relax, as CHAIN:RESSEQ — for example A:145,A:41. Takes precedence over flexible_radius. Use the residue number, not its name: protonation relabels histidines to HID/HIE/HIP. Residues that do not match are reported and stay frozen. Docs: https://docs.azulenelabs.com/tools/opal_ml_optimize/
flexible_backbone boolean no false Also let the backbone of the flexible residues move, not the side chains alone. Off by default and worth leaving off — a frozen backbone is what keeps this a rotamer rearrangement rather than a slow unfolding. No effect unless flexible residues were selected. Docs: https://docs.azulenelabs.com/tools/opal_ml_optimize/
crop_radius number no 10 For a peptide binder, keep only receptor residues within this many Angstrom; 0 disables it. Not an optimization: on a large receptor the energy difference is swamped by absolute-energy noise, which destroys ranking. Ignored on the small-molecule path. Docs: https://docs.azulenelabs.com/tools/opal_ml_optimize/. Range 0–30.
crop_receptor boolean no false Crop the receptor to the pocket before scoring. Off (the default) uses the uploaded receptor exactly as given, so a ligand series scores against one identical structure and a pre-cropped receptor is preserved unchanged. On runs the Arm-E iterative charge-balanced crop (as in the docking pipeline), seeded by the ligand pose, to bound the working set on a large receptor; it supersedes crop_radius. Docs: https://docs.azulenelabs.com/tools/opal_ml_optimize/
interior_dielectric number no 1 Solute interior dielectric for the MM-GBSA term on the peptide-binder path. The sign of the composite is not robust to this choice — 1, the default, is the vacuum-interior extreme. Prefer opal_ml_dg_nnp_kcal_mol, which this knob does not move. Ignored on the small-molecule path. Docs: https://docs.azulenelabs.com/tools/opal_ml_optimize/. Range 1–20.
keep_cofactors string no — Comma-separated cofactor residue names to retain, e.g. 'ZN,NDP'; empty strips every non-standard residue. Waters go unless you add HOH. Organic cofactors are scored as neutral, so an anionic cofactor offsets absolute dG while ranking within one receptor is unaffected. Docs: https://docs.azulenelabs.com/tools/opal_ml_optimize/
extra_chains string no — Comma-separated additional chain IDs to include (e.g. 'B' or 'B,C').

Example Input

{
  "ligand_resname": "N3",
  "chain_id": "A"
}

Featured Examples

Run one with azulene examples submit opal_ml_optimize <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
mpro-paxlovid Optimize Paxlovid–Mpro complex Geometry optimization of the Paxlovid–Mpro covalent complex (PDB 7RFS; bound ligand 4WI /…
t4-lysozyme-benzene Optimize benzene in T4 lysozyme L99A Geometry optimization of benzene bound in the engineered hydrophobic cavity of T4 lysozyme…
cdk2-roscovitine Optimize roscovitine in CDK2 Geometry optimization of the clinical CDK inhibitor roscovitine (seliciclib) bound in the ATP…
spsb2-cyclic-rgd-helm Optimize cyclo-RGDINNNV in SPSB2 (HELM) Uses the Opal ML model to relax and evaluate a docked cyclic peptide in the SPSB2 SPRY-domain…
streptavidin-cyclic-hexapeptide-smiles Optimize Ac-CHPQFC-NH2 in streptavidin (SMILES) Uses the Opal ML model to relax and evaluate a docked disulfide-cyclized hexapeptide in…

19. Protein-Ligand Scoring (opal_ml_score)

Description: Evaluate a supplied protein–ligand pose using the Opal ML model, with no relaxation. For peptides, macrocycles, or stapled binders, specify the binder chain in the supplied complex.

Category: Binding Affinity · Docs: https://docs.azulenelabs.com/tools/opal_ml_score/ · Submission modes: single

Input Schema

Field Type Required Default Description
protein_file file no — Protein structure (PDB/CIF).
ligand_file file no — Ligand structure (SDF)
ligand_resname string no — HETATM residue name in PDB
chain_id string no — Chain identifier of the receptor — the target the binder is scored against. See binder_chain for the other side of a two-chain complex.
binder_chain string no — Chain ID of the binder, as opposed to chain_id, which names the receptor. Supplying it switches to the peptide-complex path. Passing a peptide binder as chain_id instead does not raise an error — it takes the small-molecule path and returns an energy that is not meaningful. Docs: https://docs.azulenelabs.com/tools/opal_ml_score/
ligand_smiles string no — Override for the binder's chemistry. Normally leave it empty: bond orders, protonation and charges are read from the structure. Set it only when perception is wrong, or the pose is a bare heavy-atom skeleton. A SMILES whose formula disagrees with the structure is refused, not applied. Docs: https://docs.azulenelabs.com/tools/opal_ml_score/
helm string no — Cyclic or linear peptide binder in HELM2 notation, e.g. PEPTIDE1{R.G.D.I.N.N.N.V}$PEPTIDE1,PEPTIDE1,1:R1-8:R2$$$V2.0 — an alternative to 'ligand_smiles' for the same chemistry. Prefer it for a macrocycle: the equivalent 24-membered SMILES is not writable by hand. It names the chemistry, not the pose — this job scores the geometry you supply. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
drug_smiles string no — Legacy name for 'ligand_smiles'. Accepted and rewritten onto 'ligand_smiles' on submission, so existing scripts, saved workflows and chained jobs keep working; new callers should use 'ligand_smiles'. Supply at most one of 'ligand_smiles' and 'helm'.
crop_radius number no 10 For a peptide binder, keep only receptor residues within this many Angstrom; 0 disables it. Not an optimization: on a large receptor the energy difference is swamped by absolute-energy noise, which destroys ranking. Ignored on the small-molecule path. Docs: https://docs.azulenelabs.com/tools/opal_ml_score/. Range 0–30.
crop_receptor boolean no false Crop the receptor to the pocket before scoring. Off (the default) uses the uploaded receptor exactly as given, so a ligand series scores against one identical structure and a pre-cropped receptor is preserved unchanged. On runs the Arm-E iterative charge-balanced crop (as in the docking pipeline), seeded by the ligand pose, to bound the working set on a large receptor; it supersedes crop_radius. Docs: https://docs.azulenelabs.com/tools/opal_ml_score/
interior_dielectric number no 1 Solute interior dielectric for the MM-GBSA term on the peptide-binder path. The sign of the composite is not robust to this choice — 1, the default, is the vacuum-interior extreme. Prefer opal_ml_dg_nnp_kcal_mol, which this knob does not move. Ignored on the small-molecule path. Docs: https://docs.azulenelabs.com/tools/opal_ml_score/. Range 1–20.
keep_cofactors string no — Comma-separated cofactor residue names to retain, e.g. 'ZN,NDP'; empty strips every non-standard residue. Waters go unless you add HOH. Organic cofactors are scored as neutral, so an anionic cofactor offsets absolute dG while ranking within one receptor is unaffected. Docs: https://docs.azulenelabs.com/tools/opal_ml_score/
extra_chains string no — Comma-separated additional chain IDs to include (e.g. 'B' or 'B,C').

Example Input

{
  "ligand_resname": "PJE",
  "chain_id": "C"
}

Featured Examples

Run one with azulene examples submit opal_ml_score <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
t4-lysozyme-benzene Score benzene in T4 lysozyme L99A Uses the Opal ML model to evaluate benzene bound in the T4 lysozyme L99A cavity (PDB 4W52…
mpro-paxlovid Score Paxlovid–Mpro complex Uses the Opal ML model to evaluate the Paxlovid–Mpro covalent complex (PDB 7RFS; bound ligand…
cdk2-roscovitine Score roscovitine in CDK2 Uses the Opal ML model to evaluate the clinical CDK inhibitor roscovitine (seliciclib) bound…
spsb2-cyclic-rgd-helm Score cyclo-RGDINNNV in SPSB2 (HELM) Uses the Opal ML model to evaluate a docked cyclic peptide against SPSB2, with the binder's…
streptavidin-cyclic-hexapeptide-smiles Score Ac-CHPQFC-NH2 in streptavidin (SMILES) Uses the Opal ML model to evaluate a docked disulfide-cyclized hexapeptide against…

20. Peptide 3D Structure Generation (HELM) (peptide_structure)

Description: Generate 3D structures from HELM notation for linear and cyclic peptides, including non-canonical amino acids. Supports head-to-tail cyclization, disulfide bridges, lactam bridges, and arbitrary HELM2 connections. Two methods: fast (ETKDG+MMFF) or quantum (xTB GFN2 optimization).

Category: Structure Generation · Docs: https://docs.azulenelabs.com/tools/peptide_structure/ · Submission modes: single

Input Schema

Field Type Required Default Description
helm string yes — Peptide in HELM2 notation. Linear: PEPTIDE1{A.G.F.K.L}$$$$V2.0 - single-letter residues, dot-separated, non-canonical in brackets (e.g. [Aib], [dF]). To cyclize, add a bond in the first $-section: head-to-tail is PEPTIDE1{A.G.L.K.F}$PEPTIDE1,PEPTIDE1,5:R2-1:R1$$$V2.0. There are always exactly four $ separators, so a cyclic string ends $$$V2.0, not $$$$V2.0. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
n_conformers integer no 1 Number of conformers to generate and rank by energy. Range 1–100.
conformer_method string no etkdg etkdg (fast, ~3s) or xtb (GFN2-xTB geometry optimization, ~30s). One of etkdg, xtb.
force_field string no MMFF94s Force field for ETKDG optimization (ignored for xtb method). One of MMFF94s, UFF.

Example Input

{
  "helm": "PEPTIDE1{A.G.F.K.L}$$$$V2.0"
}

Featured Examples

Run one with azulene examples submit peptide_structure <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
leu-enkephalin Leu-enkephalin 3D structure Generate a 3D structure for the opioid pentapeptide Leu-enkephalin (YGGFL) from HELM via fast…
oxytocin Oxytocin (disulfide) 3D structure Generate a 3D structure for the hormone oxytocin with its native Cys1-Cys6 disulfide, from…
bradykinin Bradykinin (charged) 3D structure Generate a 3D structure for the vasoactive nonapeptide bradykinin (RPPGFSPFR) from HELM…

21. Protein Mutation Folding Stability (Physics-based ΔΔG) (protein_mutation_ddg_fold)

Description: Predict the change in folding free energy on amino-acid mutation (natural AAs + 14 ncAAs: Aib, Sar, dA/dF/dW/dY, hPhe, Hyp, mePhe, meS, meT, Nle, Orn, Phe_4F) via the Wyman two-step relative FEP cycle. Folded and unfolded legs use the same nonequilibrium alchemical protocol, Amber14SB force field, TIP3P water model, and BAR estimator, with parameters for the supported non-canonical amino acids. The unfolded reference is a capped, context-flanked peptide built from the local sequence around the mutation site (default ±3 residues). Sign convention: ΔΔG_fold < 0 ⇔ mutation stabilizes the fold.

Category: Protein Stability · Docs: https://docs.azulenelabs.com/tools/protein_mutation_ddg_fold/ · Submission modes: single

Input Schema

Field Type Required Default Description
protein_pdb file yes — Input protein structure (PDB). One chain only — a file with more than one chain is rejected, because the mutation site would be ambiguous.
mutations string no — Comma-separated list of mutations of the form CHAIN:RESID[ICODE]:TARGET. Bracket-wrapped HELM2 monomers also accepted (e.g. "A:78:V,A:78:[Aib],A:78:[dF],A:78:[meS]"). Mutually exclusive with mutant_chain_helm — provide one or the other.
mutant_chain_helm string no — The mutated chain in full (N to C) in HELM2 notation, e.g. PEPTIDE1{A.G.F.K.L}$$$$V2.0. Mutations are inferred by aligning it position-by-position against the chosen PDB chain, so it must be the same length as that chain. Alternative to listing mutations; mutually exclusive with it. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
mutant_chain_id string no A Chain id in the input PDB that mutant_chain_helm corresponds to. Default A. Ignored when mutations is used directly.
unfolded_flank_size integer no 3 Number of residues to keep on each side of the mutation site when building the unfolded-reference capped peptide from the input PDB. Default 3 ⇒ 7-residue window. 0 collapses to a capped single-residue (Ace-X-NMe) reference. Range 0–10.
unfolded_engine string no feflow Engine for the unfolded leg. 'feflow' (recommended) shares NonEquilibriumCyclingProtocol with the folded leg. 'rfe_legacy' is the original SMILES + RFECalculator path retained one release as an escape hatch. One of feflow, rfe_legacy.
unfolded_relax_ns number no 0.005 Pre-equilibration molecular dynamics duration (nanoseconds) for each capped peptide representing the unfolded state, before the alchemical transformation. Allows the backbone torsions to relax from the folded context. Default 0.005 ns (5 ps); use 0.1 ns for the Aldeghi 2019 protocol. A value of 0 performs minimization only. Minimum 0.
unfolded_relax_implicit_solvent boolean no true If true (default), use OBC2 GBSA implicit solvent for the unfolded relaxation: ~3-10 s per peptide, no pre-solvation complications. False uses TIP3P explicit + Monte-Carlo barostat (Aldeghi 2019 protocol; required for quantitative comparison against published numbers, slower).
unfolded_relax_restrained_nvt_ns number no 0 Backbone-restrained NVT pre-equilibration duration (nanoseconds). Solvent + side chains relax around a harmonically-fixed backbone. Default 0 (skipped); Aldeghi 2019 uses 0.05 ns. Minimum 0.
unfolded_relax_unrestrained_nvt_ns number no 0 Backbone-free NVT pre-equilibration duration (nanoseconds), runs after the restrained-NVT phase. Default 0 (skipped); Aldeghi 2019 uses 0.05 ns. Minimum 0.
mode_preset string no smoke Single-knob preset. 'smoke' keeps the values you supply — the short demonstration protocol. 'aldeghi_2019_quantitative' overwrites the timing knobs with the published reference-grade protocol (equil_length_ns=5.0, n_neq_switches_per_direction=50, 3 replicates, explicit-solvent unfolded reference) — far slower and many more credits, but the Aldeghi 2019 convention. Runnable now that both its equilibration length and its explicit-solvent reference relaxation are fixed. One of smoke, aldeghi_2019_quantitative.
random_seed integer no 42 Recorded in the result provenance for audit. It does not make a run reproducible: nothing in this pipeline seeds an integrator (setRandomNumberSeed appears nowhere in the worker) and velocities are re-drawn on every run, so two submissions with identical input and the same random_seed return different ddG_fold — a 1.6 kcal/mol spread has been measured on one input. Treat repeat runs as independent samples.
equil_length_ns number no 0.005 Equilibration length per endpoint, in nanoseconds, before each nonequilibrium switch. Default 0.005 ns (5 ps), the short demonstration setting; increase for more thorough equilibration at proportional runtime and credit cost. Any positive value runs exactly, down to a single integration timestep (4 fs) — there is no fixed list of allowed lengths and no upper cap (cost scales with the value you choose). Minimum 4e-06.
n_neq_switches_per_direction integer no 50 Number of non-equilibrium switches per direction (forward + reverse). Default 50 per Aldeghi 2019. Minimum 1.
protocol_repeats integer no 1 Number of independent replicates of the full folded + unfolded protocol. Each replicate draws fresh velocities, so a value above 1 reports a real uncertainty — the standard error of the mean across replicates — instead of a single-shot estimate. Cost scales linearly with the number of replicates; the default of 1 is the quick demonstration setting. Minimum 1.

Example Input

{
  "protein_pdb": "<local path or storage key>",
  "mutations": "A:6:F"
}

protein_pdb takes a local path — the SDK uploads it and substitutes the storage key — or a storage key you already have. Check the Required column above for which of them you have to supply.

Featured Examples

Run one with azulene examples submit protein_mutation_ddg_fold <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
model-peptide-w6f ddG_fold: Trp6->Phe model peptide Folding free-energy change for a Trp6->Phe mutation in a 7-residue model peptide via the…
chignolin-w9f ddG_fold: chignolin Trp9->Phe (destabilizing) Folding free-energy change for a Trp9->Phe mutation in chignolin (1UAO), the 10-residue…
cln025-y2g ddG_fold: CLN025 Tyr2->Gly (destabilizing) Folding free-energy change for a Tyr2->Gly mutation in CLN025 (5AWL), the hyperstable…

22. Boltz-2 Structure + Affinity Prediction (boltz_prediction)

Description: Co-fold up to 12 protein chains with up to 8 cofactors and 1 ligand using Boltz-2. The minimal input is {"proteins": [{"sequence": "..."}], "ligand": {"smiles": "..."}} — chain IDs auto-assign (proteins → A, B, C, …; ligand → Z), MSA defaults to the public Boltz server, affinity head fires when a ligand is present, output is CIF, potentials disabled by default. Override any field explicitly. Homo-multimers are expressed by listing several proteins entries (one per copy) — the legacy id: ["A", "B"] shorthand is also accepted.

Category: Co-folding · Docs: https://docs.azulenelabs.com/tools/boltz_prediction/ · Submission modes: single

Input Schema

Field Type Required Default Description
proteins array yes — JSON array of 1..12 protein chain entries — always wrap in [...] even for a single chain. Minimal value: [{"sequence": "<one-letter AA string>"}]. Per-entry: id auto-assigns to A, B, C, …; msa.mode defaults to "server".
ligand object no — Optional one-ligand JSON object (affinity-head target). Minimal value: {"smiles": "<SMILES>"}. id auto-assigns to Z. Provide exactly one of smiles or ccd.
cofactors array no — JSON array of 0..8 cofactor entries. Minimal value: [{"id": "C", "ccd": "<3-letter code>"}]. Each requires an explicit id plus exactly one of ccd (preferred) or smiles.
templates array no — JSON array of 0..N structural templates. Minimal value: [{"url": "<signed .pdb/.cif URL>"}]. Each entry: {url, chain_id?, template_id?, force?, threshold?}.
constraints object no — Optional {pockets: [...], bonds: [...]} — pocket distance constraints and explicit covalent (e.g. disulfide) bonds.
properties object no — Property-head toggles. {affinity: bool} — defaults to true when a ligand is present, false otherwise.
runtime object no — CLI knobs. Defaults: use_msa_server=true, use_potentials=false, no_kernels=false, diffusion_samples=1, output_format="cif".
mode string no — "json" (default) or "raw_yaml" for the power-user passthrough (also requires raw_yaml_url). One of json, raw_yaml.
raw_yaml_url string no — Signed-URL pointing at a complete Boltz YAML when mode == "raw_yaml". Accepts .yaml,.yml.
keep_dirs boolean no true Keep the full prediction outputs so you can download them afterward (recommended). Turn off only if you don't need the raw files.
proteins — one entry
Field Type Required Default Description
id string no — Chain label, e.g. A
sequence string yes — One-letter sequence
msa.mode string no server How the MSA is built. One of server, empty, upload.
ligand — one entry
Field Type Required Default Description
smiles string no — SMILES, if the ligand has no CCD code
ccd string no — 3-letter PDB chemical component code, e.g. ATP
id string no — Chain label; auto-assigns to Z when omitted
cofactors — one entry
Field Type Required Default Description
id string yes — Chain label, e.g. A
ccd string no — 3-letter PDB chemical component code, e.g. ATP
smiles string no — SMILES, if the ligand has no CCD code
templates — one entry
Field Type Required Default Description
url string yes — Signed .pdb / .cif URL. Accepts .pdb,.cif.
chain_id string no — Chain this template applies to

Example Input

{
  "proteins": [
    {
      "sequence": "MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAK"
    }
  ],
  "ligand": {
    "smiles": "CC(=O)Oc1ccccc1C(=O)O"
  },
  "properties": {
    "affinity": true
  }
}

Featured Examples

Run one with azulene examples submit boltz_prediction <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
boltz-t4-toluene T4 lysozyme L99A + toluene (affinity) T4 lysozyme L99A co-folded de novo with toluene, firing Boltz-2's affinity head on the…
boltz-dhfr-trimethoprim E. coli DHFR + NADPH + trimethoprim E. coli DHFR co-folded with its NADPH cofactor and the inhibitor trimethoprim under explicit…
boltz-t4-template T4 L99A + toluene (template-guided) Template-guided counterpart to t4-toluene: the same L99A + toluene affinity job folded onto…
boltz-hiv-protease-indinavir HIV-1 protease dimer + indinavir HIV-1 protease homodimer co-folded with indinavir, demonstrating C2-symmetric interface…
boltz-fkbp-frb-rapamycin FKBP12–FRB + rapamycin (molecular glue) The rapamycin molecular glue co-folded with the FKBP12 + FRB heterodimer — a compact…
boltz-ubiquitin-ensemble Ubiquitin 20-model ensemble Pure structure prediction of ubiquitin with 20 diffusion samples, exporting a 20-model…

23. Rank protein-protein interfaces using Opal (opal_ppi)

Description: Predict and rank candidate protein-protein interfaces from two protein sequences. Returns representative complex structures, interface-confidence scores, and interface residues for follow-up analysis, plus a trained binding-affinity estimate (pKd). Uses the Opal ML model to evaluate each pose. Either chain may be a cyclic peptide or carry non-canonical residues and covalent crosslinks. Returns up to 5 interfaces by default; combined chain length is capped at 1500 residues.

Category: Co-folding · Docs: https://docs.azulenelabs.com/tools/opal_ppi/ · Submission modes: single

Input Schema

Field Type Required Default Description
protein_a object yes — First protein chain. Prefer the binding domain over the full-length UniProt entry: long disordered tails depress receptor pTM and increase runtime without improving the interface.
protein_b object yes — Second protein chain. By default whichever of the two chains is shorter is treated as the binder for the affinity estimate and Opal ML model evaluation; set binder_chain to choose explicitly.
n_surfaces integer no 5 Number of representative binding surfaces to return (1..20). Default 5. Range 1–20.
n_diffusion_samples integer no 15 Structure-prediction diffusion samples to run before clustering (2..50). Default 15. Range 2–50.
binder_chain string no — Which chain is the binder — the side both scores are computed with respect to. Neither the affinity estimate nor the Opal ML model output is symmetric under swapping the two chains, so this field decides what those numbers describe. Leave it empty and the shorter chain is used, which is right for the common receptor-plus-small-partner case and wrong whenever the biology says otherwise: name your designed binder, your peptide, or whichever partner you are trying to rank. The affinity head crops to ~256 tokens and reserves the whole binder first, so naming a very long chain here spends the budget on it and leaves little for the receptor interface. It must name one of this job's own chains -- the defaults are A and B, but if you rename a chain, use its new id. Whichever way it resolves, the job reports it back as affinity_binder_chain.
bonds array no — Explicit covalent crosslinks, as [{atom1: {chain_id, residue, atom}, atom2: {...}}] — residue indices 1-based into that chain's sequence. Intra-chain for a stapled or bridged binder, inter-chain for a covalent complex. Do not declare a plain disulfide between two ordinary cysteines — the model forms those correctly on its own, and declaring one makes the geometry worse. Use this field for a bond the model cannot infer: a hydrocarbon staple, a lactam, or a thioether to a non-canonical residue.
templates array no — Structural templates, as [{"url", "chain_id", "force", "threshold"}]. Set force to true, or the template has very little effect. PDB or CIF only. A crystal structure of one partner is the common case. Template a conformation you trust: forcing a computationally generated conformer can pull the binder several angstroms away from the true pose, while forcing an experimentally determined one improves it.
runtime object no — PPI runtime knobs. {use_msa_server: bool=true, use_potentials: bool=false, no_kernels: bool=false, step_scale: float|null}. Structures are always returned as PDB. step_scale is the diffusion temperature (0.5-3.0, null keeps the folding model's default): A lower value widens the ensemble, which is the lever to reach for when this tool returns K near-identical surfaces because the complex is confident. use_potentials triples sampling time and, on the shipped folding model, is not safe on a multi-chain system - leave it off unless you know otherwise.
keep_dirs boolean no true The full prediction outputs are always kept and uploaded for download on this tool; this switch is accepted but has no effect. You are not charged extra for the archive.
crop_receptor boolean no true Crop the receptor to the interface before evaluation using the Opal ML model. On (the default) applies the charge-balanced interface crop needed by the Opal ML model; evaluating the uncropped complex is unreliable. Off scores the complex uncropped (expert; e.g. a receptor already cropped upstream).
protein_a — one entry
Field Type Required Default Description
sequence string yes — One-letter amino-acid sequence of the first chain.
id string no A Chain label. Defaults to A.
msa.mode string no server How this chain's MSA is built. Keep server: single-sequence chains fold badly, which corrupts the interface you are trying to score. upload needs the companion URL below. One of server, empty, upload.
msa.upload_url string no — Signed URL of a precomputed MSA for this chain (.a3m or .csv). Required when msa.mode is upload, and rejected otherwise. Accepts .a3m,.csv.
cyclic boolean no false Head-to-tail backbone closure on the first chain — residue 1's N bonded to the last residue's C. For a cyclic peptide partner. Leave false for a stapled, disulfide- or lactam-bridged chain: those have linear backbones with a side-chain bridge, and belong in bonds. Setting it on one of those silently changes the relative position encoding and degrades the prediction.
modifications array no — Non-canonical residues on the first chain, as [{position, ccd}] — 1-based positions, each replaced by the named PDB chemical component. The one-letter sequence cannot express an N-methylation, a D-amino acid or a staple olefin; put a placeholder letter in sequence and the real chemistry here.
protein_b — one entry
Field Type Required Default Description
sequence string yes — One-letter amino-acid sequence of the second chain.
id string no B Chain label. Defaults to B.
msa.mode string no server How this chain's MSA is built. Keep server: single-sequence chains fold badly, which corrupts the interface you are trying to score. upload needs the companion URL below. One of server, empty, upload.
msa.upload_url string no — Signed URL of a precomputed MSA for this chain (.a3m or .csv). Required when msa.mode is upload, and rejected otherwise. Accepts .a3m,.csv.
cyclic boolean no false Head-to-tail backbone closure on the second chain — residue 1's N bonded to the last residue's C. For a cyclic peptide partner. Leave false for a stapled, disulfide- or lactam-bridged chain: those have linear backbones with a side-chain bridge, and belong in bonds. Setting it on one of those silently changes the relative position encoding and degrades the prediction.
modifications array no — Non-canonical residues on the second chain, as [{position, ccd}] — 1-based positions, each replaced by the named PDB chemical component. The one-letter sequence cannot express an N-methylation, a D-amino acid or a staple olefin; put a placeholder letter in sequence and the real chemistry here.
bonds — one entry
Field Type Required Default Description
atom1.chain_id string yes — Chain of the first partner - A or B, matching the chain ids above.
atom1.residue integer yes — 1-based residue index of the first crosslink partner. Minimum 1.
atom1.atom string yes — PDB atom name on residue 1. SG for a Cys sulfur, NZ for a Lys amine, CZ on an MH8 staple, CJ on an L4R staple.
atom2.chain_id string yes — Chain of the second partner. Same chain as atom 1 for a staple or an intra-chain disulfide; the other chain for a covalent complex.
atom2.residue integer yes — 1-based residue index of the second crosslink partner. Minimum 1.
atom2.atom string yes — PDB atom name on residue 2.
templates — one entry
Field Type Required Default Description
url file yes — PDB or CIF structure to template on. Accepts .pdb,.cif.
chain_id string no — Which of this job's chains the template applies to. Omit to let the folding model match.
force boolean no false Enforce the template at inference time rather than treating it as a hint.
threshold number no 1 Distance threshold, in Angstrom, for the forced template restraint.

Example Input

{
  "protein_a": {
    "sequence": "MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAK"
  },
  "protein_b": {
    "sequence": "MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAK"
  },
  "n_surfaces": 5,
  "n_diffusion_samples": 15
}

Featured Examples

Run one with azulene examples submit opal_ppi <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
opal-ppi-barnase-barstar Barnase-Barstar interface Opal protein-protein interface ranking for the classic barnase-barstar complex.
opal-ppi-pd1-pdl1 PD-1 / PD-L1 immune checkpoint Opal protein-protein interface ranking for the PD-1 / PD-L1 immune checkpoint (PDB 4ZQK) — the…
opal-ppi-kras-raf1-rbd KRAS / RAF1-RBD oncogenic interface Opal protein-protein interface ranking for the oncogenic RAS-effector interaction KRAS /…

24. Macrocycle and Cyclic Peptide Binding Prediction (boltz_macrocycle)

Description: Folds a macrocyclic or stapled peptide against a receptor and scores the interface; the binder is a peptide chain, not a ligand SMILES. Receptor plus binder is capped at 1500 residues. Poses are sampled under steering potentials and ranked by interface confidence, with an experimental affinity_pkd (higher = tighter) that separates strong binders from weak rather than ranking close analogs. Where the binder's chemistry allows, the tool uses the Opal ML model to evaluate each pose. The model output is not a calibrated binding free energy. A binder whose residues have no protonation template reports no energy rather than an unreliable one; the affinity estimate and the interface scores are unaffected. opal_ml_inputs feeds Protein-Ligand Scoring and Protein-Ligand Pose Refinement.

Category: Co-folding · Docs: https://docs.azulenelabs.com/tools/macrocycle_affinity/ · Submission modes: single

Input Schema

Field Type Required Default Description
receptor object yes — Target protein chain — a JSON object, e.g. {"sequence": "<one-letter AA string>"}. Trim the receptor to the binding domain. A full-length sequence folds far worse and takes the scored interface down with it, quietly; it is the biggest quality lever on this tool.
binder object yes — Macrocyclic or stapled peptide binder — a JSON object expressed as a peptide CHAIN, not a ligand SMILES. Minimal value: {"sequence": "<one-letter AA string>"}. Non-standard residues go in modifications, the crosslink itself in bonds.
modifications array no — Non-standard residues on the binder, as [{position, ccd}] — 1-based positions into binder.sequence, each replaced by the named PDB chemical component. Leave empty for an all-canonical peptide.
bonds array no — Explicit covalent crosslinks on the binder: [{kind, atom1: {residue, atom}, atom2: {residue, atom}}], residue indices 1-based into binder.sequence. Staples, disulfides, lactams and thioethers belong here and not in binder.cyclic.
n_poses integer no 5 Number of diverse representative poses to return (1..20). Default 5. Range 1–20.
n_diffusion_samples integer no 15 Diffusion samples to generate before ranking and clustering (2..50). Default 15. Wall-clock and cost scale roughly linearly with this. Range 2–50.
ligand_arm boolean no true Also co-fold the binder as a ligand from its own SMILES, so Protein-Ligand Scoring and Protein-Ligand Pose Refinement receive a declared molecular graph instead of one re-perceived from coordinates — perception mangles stapled peptides. Turning it off returns the single fold and roughly halves the runtime.
ligand_file file no — The binder as an SDF or MOL file, read for its chemistry and never as a starting conformer. This is what a Peptide Structure result hands over — pass its structure_sdf_url.download_url. Checked against the sequence, with a warning if they describe different molecules. Accepts .sdf,.mol.
ligand_smiles string no — The binder's chemistry as a SMILES, for the ligand fold. Optional: it is otherwise assembled from binder.sequence, modifications and bonds via the same CCD entries. A SMILES you supply is used verbatim and is not re-charged at pH 7.4.
helm string no — The binder in HELM2 notation, naming its chemistry for the ligand fold — never a starting conformer. Templating a macrocycle on a generated conformer was measured and it hurts: on the PD-L1 example the binder went from 1.36 A to 3.38 A off the deposited structure. Full syntax: https://docs.azulenelabs.com/tools/peptide_structure/
runtime object no — Runtime knobs. {use_msa_server: bool=true, use_potentials: bool=true, no_kernels: bool=false}. use_potentials steers the diffusion so poses come out clash-free and covalently sane; it is on by default and costs roughly 3x the sampling time.
templates array no — Structural templates for the binder chain, as [{"url", "chain_id", "force", "threshold"}]. Set force to true, or the template has very little effect. PDB or CIF only, never SDF. Template a conformation you trust: a computationally generated one makes accuracy worse, not better.
keep_dirs boolean no true The full prediction outputs are always kept and uploaded for download on this tool; this switch is accepted but has no effect. You are not charged extra for the archive.
crop_receptor boolean no true Crop the receptor to the interface before evaluation using the Opal ML model. On (the default) applies the size-gated receptor crop needed by the Opal ML model; evaluating the uncropped complex is unreliable. Off scores the complex uncropped (expert; e.g. a receptor already cropped upstream).
receptor — one entry
Field Type Required Default Description
sequence string yes — One-letter sequence of the target protein
id string no A Chain label; defaults to A
msa.mode string no server How the receptor MSA is built. One of server, empty, upload.
binder — one entry
Field Type Required Default Description
sequence string yes — One-letter sequence of the peptide binder
id string no B Chain label; defaults to B
cyclic boolean no false Head-to-tail backbone closure only — the peptide's own N-terminus amide-bonded to its own C-terminus. Leave this OFF for stapled, disulfide-bridged, lactam-bridged and thioether peptides: those are side-chain crosslinks on a linear backbone and belong in bonds, not here. Most macrocyclic peptides are not head-to-tail. Setting it wrongly is silent: it changes the residue positional encoding (the model wraps position N back to position 1) without any error, so the pose comes back looking plausible and is wrong.
modifications — one entry
Field Type Required Default Description
position integer yes — 1-based residue index into the binder sequence. Minimum 1.
ccd string yes — PDB chemical-component (CCD) code for the replacement residue. CCD codes are not guessable from trivial names — look the code up in the PDB chemical component dictionary before using it. Two codes that read like the obvious answer and are not: CBA is a pyridoxal-phosphate adduct, not cyclobutyl-alanine (that is 2JH); AHX is an AMP conjugate, not 6-aminohexanoic acid (that is ACA). A wrong-but-real code is accepted and folded, so the job succeeds with the wrong chemistry.
bonds — one entry
Field Type Required Default Description
kind string no — Chemistry of the crosslink. Sets the default atom names and the bond order. One of staple, disulfide, lactam, thioether, head_to_tail, sidechain.
atom1.residue integer yes — 1-based residue index of the first crosslink partner. Minimum 1.
atom1.atom string no — PDB atom name on residue 1 (e.g. CZ, CJ, SG). Defaults from kind.
atom2.residue integer yes — 1-based residue index of the second crosslink partner. Minimum 1.
atom2.atom string no — PDB atom name on residue 2 (e.g. CZ, CJ, SG). Defaults from kind.
templates — one entry
Field Type Required Default Description
url file yes — Conformer structure — upload a PDB or CIF file. Never an SDF: structural templates are read as PDB or CIF only. Accepts .pdb,.cif.
chain_id string no — Chain this template applies to. Must be the binder chain — templating the receptor would replace the structure you asked to dock against.
force boolean no false Restrain the binder to this template during sampling. Off by default, and off means the template has almost no effect — leave it off only if you want the structure as a hint rather than a constraint.
threshold number no 1.5 How far each residue may drift from the template, in Ångström, when force is on. 1.0–1.5 is the useful range: tightening to 0.5 bought 0.02 Å and cost interface confidence.

Example Input

{
  "receptor": {
    "sequence": "MCNTNMSVPTDGAVTTSQIPASEQETLVRPKPLLLKLLKSVGAQKDTYTMKEVLFYLGQYIMTKRLYDEKQQHIVYCSNDLLGDLFGVPSFSVKEHRKIYTMIYRNLVVVNQQESSDSGTSVSEN"
  },
  "binder": {
    "sequence": "TSFAHYWALLA",
    "cyclic": false
  },
  "modifications": [
    {
      "position": 4,
      "ccd": "L4R"
    },
    {
      "position": 11,
      "ccd": "MH8"
    }
  ],
  "bonds": [
    {
      "kind": "staple",
      "atom1": {
        "residue": 4,
        "atom": "CJ"
      },
      "atom2": {
        "residue": 11,
        "atom": "CZ"
      }
    }
  ],
  "n_poses": 5,
  "n_diffusion_samples": 15
}

Featured Examples

Run one with azulene examples submit boltz_macrocycle <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
boltz-macrocycle-mdm2-stapled-p53 MDM2 + stapled p53 helix (i,i+7 hydrocarbon staple) An i,i+7 all-hydrocarbon stapled p53 analog against the MDM2 p53-binding domain — the textbook…
boltz-macrocycle-pdl1-cyclic PD-L1 + head-to-tail cyclic peptide (PDB 7OUN) The co-crystallized macrocycle from PDB 7OUN against the PD-L1 IgV ectodomain. This is the one…
boltz-macrocycle-igg-fc-disulfide IgG1 Fc + Fc-III disulfide-cyclized peptide The Fc-III cyclic peptide against the IgG1 Fc CH2–CH3 interface (PDB 5DI8 family). Disulfide…

25. Chai-1 Complex Structure Prediction (chai_prediction)

Description: All-atom co-folding with Chai-1 (Apache-2.0). Supports protein, RNA, DNA, and small-molecule chains; MSA-free via ESM embeddings or MSA-augmented; constraint and template inputs match the AF3 family. Combined chain length is limited to 1000 residues; for a larger single-chain protein use OpenFold2 or OpenFold3.

Category: Co-folding · Docs: https://docs.azulenelabs.com/tools/chai_prediction/ · Submission modes: single

Input Schema

Field Type Required Default Description
chains array yes — Add one row per unique sequence and select the molecule type. Give each chain a unique ID. For identical copies, enter multiple IDs, such as A,B. Add small molecules separately under Ligands.
ligands array no — Add small molecules or cofactors using their SMILES strings. Give each ligand a unique ID, such as L1, different from the chain IDs. Leave this section empty for complexes without small-molecule ligands.
runtime object no — Chai-1 runtime knobs. Defaults: use_esm_embeddings=true, use_msa_server=false, low_memory=true, num_diffn_samples=5, output_format="cif".
mode string no — "json" (default) or "raw_fasta" for the power-user FASTA passthrough (also requires raw_fasta_url). One of json, raw_fasta.
raw_fasta_url string no — Signed-URL pointing at a complete Chai FASTA when mode == "raw_fasta". Accepts .fasta,.fa.
keep_dirs boolean no true Keep the full prediction outputs so you can download them afterward (recommended). Turn off only if you don't need the raw files.
chains — one entry
Field Type Required Default Description
id string yes — Chain label, e.g. A. A list such as ["A","B"] repeats the same sequence as a homo-multimer.
type string no protein Polymer class. For protein/rna/dna, sequence is the one-letter sequence; for glycan it is a Chai glycan string (e.g. NAG(4-1 NAG)), not residues. Small molecules and cyclic peptides go in ligands as SMILES. One of protein, rna, dna, glycan, cyclic.
sequence string yes — The full sequence for this chain. Use one-letter amino-acid or nucleotide codes for proteins, RNA, or DNA.
ligands — one entry
Field Type Required Default Description
id string yes — Chain label, e.g. A
ccd string no — 3-letter PDB chemical component code, e.g. ATP
smiles string no — SMILES, if the ligand has no CCD code

Example Input

{
  "chains": [
    {
      "id": "A",
      "type": "protein",
      "sequence": "MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAK"
    }
  ],
  "runtime": {
    "use_esm_embeddings": true,
    "num_diffn_samples": 5,
    "output_format": "cif"
  },
  "keep_dirs": true
}

Featured Examples

Run one with azulene examples submit chai_prediction <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
chai-abl-imatinib ABL1 kinase + imatinib Model the ABL1 kinase domain with imatinib to examine the predicted drug-binding pose and…
chai-u1a-rna U1A protein + U1 snRNA hairpin Model the U1A spliceosomal protein bound to a U1 snRNA hairpin to examine RNA conformation and…
chai-er-estradiol ER-α homodimer + estradiol Model two estrogen receptor-α ligand-binding domains with estradiol to examine the predicted…

26. OpenFold3 Complex Structure Prediction (openfold3_prediction)

Description: AF3-parity all-atom structure prediction with OpenFold3 (Apache-2.0). Within experimental error of AlphaFold3 on CASP16 monomers and the only open model matching AF3 on RNA. Handles large assemblies — verified on production to at least 1600 residues.

Category: Co-folding · Docs: https://docs.azulenelabs.com/tools/openfold3_prediction/ · Submission modes: single

Input Schema

Field Type Required Default Description
chains array yes — The molecules to fold, given as a JSON list of 1–12 chains. Each chain is an object with: id (a chain label like "A"; use a list such as ["A","B"] to repeat the same sequence as a homo-multimer), sequence (the one-letter sequence), and type — one of "protein", "rna", or "dna" (defaults to "protein"). Edit the example shown in the box. Example: [{"id":"A","type":"protein","sequence":"MNIF..."}].
ligands array no — Optional small molecules / cofactors to co-fold (0–8), as a JSON list. Each is an object with an id plus either ccd (a 3-letter PDB chemical-component code, e.g. "ATP") OR smiles (a SMILES string). Leave empty if there are no ligands. Example: [{"id":"L1","ccd":"ATP"}].
bonded_atom_pairs array no — Explicit covalent / disulfide bonds: [{chain_id_a, residue_a, atom_a, chain_id_b, residue_b, atom_b}].
name string no — Job name written into the AF3 JSON (default openfold3_job).
runtime object no — OpenFold3 runtime knobs. Defaults: use_deepspeed_evo_attention=true, num_diffn_samples=5, num_recycles=3, precision="bf16", output_format="cif".
mode string no — "json" (default) or "raw_af3_json" for the power-user AF3-JSON passthrough (also requires raw_af3_url). One of json, raw_af3_json.
raw_af3_url string no — Signed-URL pointing at a complete AF3 input JSON when mode == "raw_af3_json". Accepts .json.
user_ccd string no — Optional user-supplied CCD entries in mmCIF format (raw text) for non-standard ligands.
keep_dirs boolean no true Keep the full prediction outputs so you can download them afterward (recommended). Turn off only if you don't need the raw files.
chains — one entry
Field Type Required Default Description
id string yes — Chain label, e.g. A. A list such as ["A","B"] repeats the same sequence as a homo-multimer.
sequence string yes — One-letter sequence
type string no protein Polymer class. For protein/rna/dna, sequence is the one-letter sequence; for glycan it is a Chai glycan string (e.g. NAG(4-1 NAG)), not residues. Small molecules and cyclic peptides go in ligands as SMILES. One of protein, rna, dna.
ligands — one entry
Field Type Required Default Description
id string yes — Chain label, e.g. A
ccd string no — 3-letter PDB chemical component code, e.g. ATP
smiles string no — SMILES, if the ligand has no CCD code
bonded_atom_pairs — one entry
Field Type Required Default Description
chain_id_a string yes — First chain
residue_a integer yes — First residue number
atom_a string yes — First atom name, e.g. SG
chain_id_b string yes — Second chain
residue_b integer yes — Second residue number
atom_b string yes — Second atom name, e.g. SG

Example Input

{
  "chains": [
    {
      "id": "A",
      "type": "protein",
      "sequence": "MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAK"
    }
  ],
  "name": "of3-single-protein",
  "runtime": {
    "num_diffn_samples": 5,
    "output_format": "cif"
  },
  "keep_dirs": true
}

Featured Examples

Run one with azulene examples submit openfold3_prediction <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
openfold3-trna-phe Yeast tRNA-Phe (pure RNA) Pure-RNA prediction of the L-shaped yeast tRNA-Phe fold from sequence alone — where…
openfold3-ca2-acetazolamide Carbonic anhydrase II + acetazolamide (Zn) AF3-style all-atom co-fold of human carbonic anhydrase II with its catalytic zinc and the…
openfold3-engrailed-dna Engrailed homeodomain + dsDNA Protein-DNA co-fold of the Drosophila engrailed homeodomain with its double-stranded DNA…

27. OpenFold2 Protein Structure Prediction (openfold2_prediction)

Description: Single-chain and multimer protein structure prediction with OpenFold2 (Apache-2.0). Folds from the sequence alone: this deployment has no MSA-search path, so no evolutionary alignment is built unless you upload one. Expect a mean pLDDT around 40-60 rather than the >90 an MSA-backed AlphaFold2-style run reaches — the fold topology is usable, the confidence score is not a defect. Upload a precomputed alignment (msa.mode = upload) to get MSA-backed accuracy. Handles large proteins — verified on production to at least 1600 residues.

Category: Co-folding · Docs: https://docs.azulenelabs.com/tools/openfold2_prediction/ · Submission modes: single

Input Schema

Field Type Required Default Description
proteins array yes — 1..12 protein chains. Each: {id, sequence, msa:{mode}}. msa.mode is empty for single-sequence folding, or upload with an upload_url pointing at a precomputed alignment (.a3m). server is accepted for schema compatibility but runs exactly like empty — this deployment has no MSA-search path, so only upload supplies an alignment. id may be a list for a homo-multimer.
runtime object no — OpenFold2 runtime knobs. long_sequence_inference and use_deepspeed_evo_attention (both default true) are the two that take effect. Structures are always returned as mmCIF, whatever output_format says. num_recycles, chunk_size, precision and use_msa_server are accepted for schema compatibility with the other co-folding tools and are not passed to the model — setting them changes nothing.
mode string no — "json" (default) or "raw_fasta" for the power-user FASTA passthrough (also requires raw_fasta_url). One of json, raw_fasta.
raw_fasta_url string no — Signed-URL pointing at a complete FASTA when mode == "raw_fasta". Accepts .fasta,.fa.
keep_dirs boolean no true Keep the full prediction outputs so you can download them afterward (recommended). Turn off only if you don't need the raw files.
proteins — one entry
Field Type Required Default Description
id string yes — Chain label, e.g. A. A list such as ["A","B"] repeats the same sequence as a homo-multimer.
sequence string yes — One-letter sequence
msa.mode string no empty How the alignment is supplied. Two modes work: empty (the default) folds from the sequence alone, and upload uses a precomputed alignment you provide via msa.upload_url. There is no MSA search in this deployment, so upload is the only way to get MSA-backed accuracy; without one, expect a mean pLDDT around 40-60 and read pLDDT/pTM accordingly. A third value, server, runs exactly like empty: there is no MSA search here. It stays accepted because the published example specs still send it and submit-time validation rejects unknown enum values — dropping it would turn a working submission into a 400. One of server, empty, upload.
msa.upload_url string no — Signed URL of a precomputed multiple-sequence alignment (.a3m) for this chain. Required when msa.mode is upload; it is the only way to give OpenFold2 an alignment, since this deployment has no MSA search. Accepts .a3m.

Example Input

{
  "proteins": [
    {
      "id": "A",
      "sequence": "MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAK",
      "msa": {
        "mode": "empty"
      }
    }
  ],
  "runtime": {
    "long_sequence_inference": true
  },
  "keep_dirs": true
}

Featured Examples

Run one with azulene examples submit openfold2_prediction <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
openfold2-lysozyme Hen egg-white lysozyme monomer OpenFold2 monomer prediction of hen egg-white lysozyme. This tool folds from the sequence…
openfold2-luciferase Firefly luciferase (~550 aa) Firefly luciferase, a single chain of about 550 residues — an example of how the tool handles…

28. ProteinMPNN Sequence Design (Inverse Folding) (mpnn_design)

Description: Design new amino-acid sequences that fold to a backbone structure you provide (inverse folding). Upload a structure and get back several candidate sequences, each with confidence scores and how much it recovers the native sequence. Pick the model that fits your goal: ProteinMPNN (general purpose, protein-only inputs), LigandMPNN (when the backbone includes ligands, nucleic acids, or metal ions you want the design to respect), or SolubleMPNN (when you want to improve the solubility of a soluble protein).

Category: Mutation & Design · Docs: https://docs.azulenelabs.com/tools/mpnn_design/ · Submission modes: single

Input Schema

Field Type Required Default Description
mode string yes design Selects this design task. Leave as the default. One of design.
model_type string no protein_mpnn Which model to use: "protein_mpnn" (default, general purpose), "ligand_mpnn" (accounts for ligands, nucleic acids, or metal ions in the structure), or "soluble_mpnn" (biased toward more soluble sequences). One of protein_mpnn, ligand_mpnn, soluble_mpnn.
pdb_file file no — The backbone structure to redesign — upload a PDB or CIF file. This is the main input; provide either this or the advanced pdb field, not both. Accepts .pdb,.cif.
pdb object no — Advanced/API alternative to uploading a file: supply the backbone as {"inline": "<raw PDB text>"} or {"url": "<signed https URL>"}. Most users should upload via pdb_file instead. Provide one or the other, not both.
chains_to_design array no — Which chains get a new sequence. Leave empty for every chain. For example ["A"] designs chain A only, and leaves any other chain untouched as context.
fixed_positions object no — Positions to keep at their original amino acid, listed per chain. Use the residue numbers as they appear in your uploaded file, not a count from the start of the chain. For example {"A": [47, 51, 58]} keeps those three residues of chain A unchanged. A number that is not in the file is skipped, and the job still reports success, so check the numbers against your structure first.
redesign_positions object no — Positions to redesign, listed per chain, leaving every other position fixed. Use the residue numbers as they appear in your uploaded file, not a count from the start of the chain. For example {"A": [47, 51, 58]} redesigns only those three residues of chain A. A number that is not in the file is skipped, so it stays fixed instead of being redesigned, and the job still reports success. For a given chain, use this or Fixed positions, not both.
bias_aa object no — Push the design toward or away from an amino acid at every designed position. A negative value avoids it and a positive value favors it. For example {"C": -2.0} discourages cysteine everywhere.
omit_aa array no — Amino acids to never use at any designed position (e.g. exclude cysteine).
num_seq_per_target integer no 8 How many candidate sequences to design (1 to 1024). Default 8. Range 1–1024.
sampling_temp number no 0.1 How adventurous the design is, from 0.01 to 2.0, default 0.1. A lower value stays closer to the most likely, native-like sequence, and a higher value gives more diverse but riskier designs. Range 0.01–2.
batch_size integer no 1 How many sequences are computed together per pass (1 to 128). Default 1. A performance knob that does not change the results. Range 1–128.
seed integer no 37 Random seed. Default 37. Use the same seed to reproduce a run; change it to get a different set of designs. Minimum 0.
checkpoint string no — Advanced: a specific model checkpoint to use instead of the default for the chosen model.
parse_atoms_with_zero_occupancy boolean no false Whether to keep atoms marked with zero occupancy in the input structure. Off by default (they are ignored). Turn on only if your structure stores meaningful atoms at zero occupancy that you want included.
keep_dirs boolean no true Keep the full prediction outputs so you can download them afterward (recommended). Turn off only if you don't need the raw files.

Example Input

{
  "mode": "design",
  "model_type": "protein_mpnn",
  "pdb_file": "<local path or storage key>",
  "chains_to_design": [
    "A"
  ],
  "num_seq_per_target": 4,
  "sampling_temp": 0.1
}

pdb_file takes a local path — the SDK uploads it and substitutes the storage key — or a storage key you already have. Check the Required column above for which of them you have to supply.

Featured Examples

Run one with azulene examples submit mpnn_design <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
mpnn-design-crambin Crambin inverse folding (ProteinMPNN) ProteinMPNN inverse folding of the crambin (1CRN) backbone — 8 sequences at low temperature…
mpnn-design-villin-hp36 Villin HP36 redesign (SolubleMPNN) SolubleMPNN redesign of villin headpiece HP36 (1VII) with the three core phenylalanines fixed…
mpnn-design-t4-l99a-benzene T4 L99A + benzene (LigandMPNN) LigandMPNN design of T4 lysozyme L99A (1L83) conditioned on the bound benzene ligand in the…

29. Protein Stability Prediction (ThermoMPNN) (mpnn_stability)

Description: Predict how mutations change protein folding stability (ΔΔG). ThermoMPNN uses a protein structure to predict stability changes in kcal/mol. Negative values mean the mutation is predicted to stabilize the fold, positive values mean it destabilizes. Either score a specific list of mutations, or scan a stretch of a chain to try every possible amino-acid substitution (site saturation). Use it to find stabilizing mutations or to flag risky ones before testing.

Category: Protein Stability · Docs: https://docs.azulenelabs.com/tools/mpnn_stability/ · Submission modes: single

Input Schema

Field Type Required Default Description
mode string yes stability Selects this stability task. Leave as the default. One of stability.
pdb_file file no — The protein structure to score — upload a PDB or CIF file. This is the main input; provide either this or the advanced pdb field, not both. Accepts .pdb,.cif.
pdb object no — Advanced/API alternative to uploading a file: supply the structure as {"inline": "<raw PDB text>"} or {"url": "<signed https URL>"}. Most users should upload via pdb_file instead. Provide one or the other, not both.
mutations array no — A specific list of mutations to score. Each entry is {chain, position, from_aa, to_aa}. Residue numbers are as written in the uploaded file (author numbering), not a 1-based count from the start of the chain — the two differ whenever a construct starts at something other than 1 or has gaps. from_aa is checked against the residue actually at that position; a mismatch returns no predictions plus a warning naming what was requested. Use this or saturation, not both.
saturation object no — Scan a region instead of a fixed list: give {chain, start, end} (inclusive) and every possible amino-acid substitution at every position in that window is scored. Residue numbers are as written in the uploaded file (author numbering), not a 1-based count from the start of the chain — the two differ whenever a construct starts at something other than 1 or has gaps. Use this or mutations, not both.
checkpoint string no thermompnn_v1 Advanced: a specific model checkpoint to use instead of the default.
batch_size integer no 256 How many mutations are scored together per pass (1 to 4096). Default 256. A performance knob that does not change the results. Range 1–4096.
keep_dirs boolean no true Keep the full prediction outputs so you can download them afterward (recommended). Turn off only if you don't need the raw files.
mutations — one entry
Field Type Required Default Description
chain string yes — Chain the residue is in
position integer yes — 1-based position (the first residue is 1)
from_aa string yes — Wild-type residue. One of A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y.
to_aa string yes — Substituted residue. One of A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y.

Example Input

{
  "mode": "stability",
  "pdb_file": "<local path or storage key>",
  "mutations": [
    {
      "chain": "A",
      "position": 1,
      "from_aa": "M",
      "to_aa": "V"
    }
  ]
}

pdb_file takes a local path — the SDK uploads it and substitutes the storage key — or a storage key you already have. Check the Required column above for which of them you have to supply.

Featured Examples

Run one with azulene examples submit mpnn_stability <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
mpnn-stability-ubiquitin Ubiquitin core mutations (ΔΔG) ThermoMPNN ddG for three explicit ubiquitin (1UBQ) core mutations — I3V, V5A, L8A.
mpnn-stability-crambin Crambin site-saturation sweep (ΔΔG) ThermoMPNN site-saturation sweep over crambin (1CRN) positions 5-9 (every non-native AA, 95…
mpnn-stability-villin-hp36 Villin HP36 single mutation (ΔΔG) ThermoMPNN ddG for the single villin headpiece HP36 (1VII) core mutation F47A.

30. ESM-2 Protein Sequence Embeddings (esm2_embed)

Description: Turn protein sequences into ESM-2 embeddings — numeric vectors that capture each protein's properties for use in downstream machine learning, similarity search, or clustering. Optionally also predict residue-residue contact maps. Submit one or many sequences in a single run.

Category: Mutation & Design · Docs: https://docs.azulenelabs.com/tools/esm2_embed/ · Submission modes: single

Input Schema

Field Type Required Default Description
sequences array yes — The protein sequences to embed, as one-letter amino acids — one per line, or paste FASTA. Up to 64 sequences per job, each up to 2048 residues. Embeddings are returned in the order given. A sequence over 1022 residues still runs, but that is the length ESM-2 was trained to attend over and the rest is extrapolated, so the embedding is less reliable and the job says so in its warnings.
labels array no — Optional name for each sequence, one per line, in the same order. Echoed back on the result so you can tell which embedding is which — without it the output is a positional list and FASTA headers are not kept. Give one per sequence or leave it empty; a count that disagrees with the sequences is rejected rather than lined up short.
model_variant string no 650M Model size to run: "650M" (default, gives a 1280-number vector per protein) or "3B" (larger, gives a 2560-number vector for slightly higher quality at higher cost). One of 650M, 3B.
pool string no mean How to summarize each protein: "mean" (default, one vector per protein, averaged over residues), "cls" (one vector from the model's summary token), or "none" (a separate vector for every residue, delivered as a downloadable attachment). One of mean, cls, none.
return_contacts boolean no — Also predict a residue-residue contact map for each sequence (how likely each pair of residues is to be in contact). Off by default.
fp16 boolean no true Use faster half-precision math on the GPU (default on). Leave on for speed; it has no meaningful effect on the results.

Example Input

{
  "sequences": [
    "MKIEELKKWVEEFDKKLAEIFKFDFGGYRELADKVAEAVGKKVDEKQKKIVEIFEKVEAEA"
  ],
  "model_variant": "650M",
  "pool": "mean"
}

Featured Examples

Run one with azulene examples submit esm2_embed <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
esm2-embed-ubiquitin Ubiquitin mean-pooled embedding Mean-pooled ESM-2 650M embedding for ubiquitin (76 aa) — the canonical fixed-length protein…
esm2-embed-lysozyme Lysozyme CLS embedding + contact map CLS-pooled ESM-2 650M embedding plus the unsupervised residue-residue contact map for hen…
esm2-embed-minibatch Batched mini-proteins (Trp-cage, HP36, GB1) One batched call mean-pooling three classic mini-proteins (Trp-cage, villin HP36, GB1) into…

31. ESM-2 Zero-Shot Mutation Scoring (esm2_mutation_score)

Description: Score how point mutations affect a protein, with no training data needed. ESM-2 rates each mutant relative to the wild-type sequence: higher scores mean the model finds the mutation more favorable, lower scores less favorable. Use it to quickly rank or pre-screen candidate mutations.

Category: Mutation & Design · Docs: https://docs.azulenelabs.com/tools/esm2_mutation_score/ · Submission modes: single

Input Schema

Field Type Required Default Description
sequence string yes — The wild-type protein sequence the mutations are measured against (one-letter amino acids, up to 2048 residues). Past 1022 residues it still runs, but that is ESM-2's trained context and positions beyond it are extrapolated, so scores there are less reliable.
mutants array yes — The point mutations to score, as a list written like "M1A" (original amino acid, 1-based position, new amino acid), up to 4096 of them. Each is checked against the wild-type sequence, so out-of-range positions or a wrong original amino acid are rejected.
model_variant string no 650M Model size to run: "650M" (default) or "3B" (larger, for slightly higher quality at higher cost). One of 650M, 3B.
method string no masked_marginal Scoring method: "masked_marginal" (default, more accurate but slower) or "wt_marginal" (faster but less precise). One of masked_marginal, wt_marginal.
fp16 boolean no true Use faster half-precision math on the GPU (default on). Leave on for speed; it has no meaningful effect on the results.

Example Input

{
  "sequence": "MKIEELKKWVEEFDKKLAEIFKFDFGGYRELADKVAEAVGKKVDEKQKKIVEIFEKVEAEA",
  "mutants": [
    "M1A",
    "K2R",
    "I3V"
  ],
  "method": "masked_marginal"
}

Featured Examples

Run one with azulene examples submit esm2_mutation_score <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
esm2-mutation-lysozyme Hen lysozyme catalytic dyad (zero-shot) Masked-marginal zero-shot scoring of the hen lysozyme catalytic dyad (Glu35, Asp52) plus a…
esm2-mutation-ubiquitin Ubiquitin functional hotspots (zero-shot) Masked-marginal zero-shot scoring of ubiquitin functional hotspots (Ile44 patch, Lys48/Lys63…
esm2-mutation-gb1 GB1 hydrophobic-core alanine scan Wild-type-marginal alanine scan over the GB1 hydrophobic core (Y3, L5, F30, W43, F52).

32. ESMFold Single-Chain Structure Prediction (esmfold_predict)

Description: Single-chain structure prediction from sequence with ESMFold — no MSA, up to 1024 residues. Returns per-residue pLDDT and pTM. Coordinates are in the model's own frame with residues numbered from 1, so a center from another structure will not transfer.

Category: Structure Generation · Docs: https://docs.azulenelabs.com/tools/esmfold_predict/ · Submission modes: single

Input Schema

Field Type Required Default Description
sequence string yes — The protein sequence to fold (one-letter amino acids, up to 1024 residues).
output_format string no — Structure file format to return: "pdb" (default) or "cif" (mmCIF).
chunk_size integer no — Advanced memory knob. Smaller values use less GPU memory but run slower; leave unset to use the model default. Lower this only if a long sequence runs out of memory.
num_recycles integer no — How many refinement passes the model makes (0 to 8). Default 4. More passes can sharpen the structure; designed or unusual sequences may still score low confidence regardless.
keep_dirs boolean no true Keep the full prediction outputs so you can download them afterward (recommended). Turn off only if you don't need the raw files.

Example Input

{
  "sequence": "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG",
  "output_format": "pdb",
  "num_recycles": 4
}

Featured Examples

Run one with azulene examples submit esmfold_predict <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
esmfold-trp-cage Trp-cage TC5b mini-protein (20 aa) MSA-free ESMFold structure of the Trp-cage TC5b mini-protein (20 aa), PDB output, downloadable.
esmfold-villin-hp36 Villin headpiece HP36 (mmCIF) MSA-free ESMFold structure of the villin headpiece HP36 three-helix bundle (36 aa), mmCIF…
esmfold-ubiquitin Ubiquitin β-grasp fold (76 aa) MSA-free ESMFold structure of ubiquitin (76 aa), the textbook beta-grasp fold, PDB output…

33. Organic Crystal Structure Prediction (CSP) (crystal_prediction)

Description: Predict organic crystal structures with Opal from either a SMILES string or an uploaded molecular geometry. Optionally compare predictions against an experimental CIF.

Category: Structure Generation · Docs: https://docs.azulenelabs.com/tools/crystal_prediction/ · Submission modes: single

Input Schema

Field Type Required Default Description
smiles string no — SMILES string of the molecule. Provide exactly one of smiles or molecule_filename.
molecule_filename file no — Upload a molecular geometry file, such as .xyz, .cif, .mol, or .sdf. Provide either this file or SMILES.
preset string yes quick Search preset: test (tiny/debug), quick (standard), full (exhaustive CSP). One of test, quick, full.
exp_cif_file file no — Optional experimental CIF; predictions are compared to it (energy/density + geometric RMSD match).
exp_is_lowest_polymorph boolean no false Assert the experimental structure is the most stable polymorph; report whether the matched prediction is the global energy minimum.

Example Input

{
  "smiles": "CC(=O)Oc1ccccc1C(=O)O",
  "preset": "quick"
}

Featured Examples

Run one with azulene examples submit crystal_prediction <example-id>, or fetch its inputs first with azulene examples download.

Example ID Name Description
aspirin-smiles Aspirin crystal (from SMILES) Predict crystal packings of aspirin (acetylsalicylic acid) from its SMILES string using the…
acetic-acid-xyz Acetic acid crystal (.xyz + experimental CIF) Predict crystal packings of acetic acid from a relaxed .xyz geometry and compare the predicted…
nicotinamide-sdf Nicotinamide crystal (.sdf) Predict crystal packings of nicotinamide (vitamin B3) from a .sdf geometry with explicit…

Retired job types

Earlier revisions of this document described the ids below. They are not in the live catalog, so submitting one fails before anything runs and no credits are consumed. They are kept here so an old script or notebook has somewhere to land.

Retired ID Why Use instead
mp2 Quantum-chemistry tools were withdrawn from the catalog. —
hartree_fock Quantum-chemistry tools were withdrawn from the catalog. —
ccsd_calculation Quantum-chemistry tools were withdrawn from the catalog. —
xtb_calculation Semi-empirical tool withdrawn from the catalog. —
molecular_dynamics Plain MD was withdrawn; the free-energy tools run their own MD. absolute_binding, relative_binding
relative_binding_uaa Not in the catalog. The unnatural-amino-acid protocol is only exposed for solvation-phase relative free energies. relative_fe_uaa
pocket_docking Commented out of job_definitions.ts and never re-enabled, so it is not submittable — and no tool finds a pocket for you: docking REQUIRES binding_site_center, which you must supply. pdb_file is now structure_file; ligand_smiles keeps its name (drug_smiles is a deprecated alias). chain_id is newly required on both. docking, covalent_docking
upload_file Not a job type. Pass a local path to any file field and the SDK uploads it first, then submits the returned storage key. —
predict_admet Temporarily hidden from the public catalog while an ML ADMET water-toxicity result is investigated (SAA-607), not withdrawn. Still registered and runnable internally; it will return here when that is resolved. —
predict_solubility Temporarily hidden from the public catalog while the AqSolDB model is validated (opal-backend #227), not withdrawn. Still registered and submittable by id; it will return here when that is resolved. —

For live job type info directly on Azulene Studio, always refer to:

azulene jobs get-job-types

or see the API_Reference.md for usage patterns and CLI/Python examples.

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Author: Azulene Labs

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  • Released:
Newer release available (0.5.26)

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PyPI data
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Avatar for vlladygin from gravatar.com vlladygin

Credits

Author: Azulene Labs

Requires

Python >=3.8

Provides Extra

dev

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