Azulene Studio Quick Start Guide:

• Azulene Studio Quick Start Guide
• Azulene Studio
• Chaining a macrocycle run into Score Pose / Optimize Pose
• Azulene Studio Job Types

This guide walks you through a complete working example of using Azulene Studio to submit and monitor an Absolute Binding Free Energy 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 (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 Aqueous Solvation 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": "Generate Conformers", ...}, ...]}}

---

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 Score Pose / Optimize Pose

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: Score Pose (opal_ml_score) and Optimize Pose (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.
ligand_file	supplied	An SDF or MOL, which is what a Peptide Structure (HELM to 3D) run hands over — pass its structure_sdf_url. Multi-record files are conformers of one molecule and the first record is read. Charges are taken as written.
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.

ligand_file is not a starting conformer, and cannot be made into one: the folder builds its own conformer for a ligand and rejects a non-protein chain from its template machinery. It changes what is folded, never where the fold starts. The one risk it carries is a stale or edited file — the peptide fold uses binder.sequence whatever the file says, so a mismatched file gives you an affinity for one molecule and an interaction energy for another, both individually valid. The job compares the two on the heavy-atom skeleton (a protonation difference is not flagged; a different molecule is) and puts a warning in warnings.

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.

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. absolute_binding takes pdb_file and ligand_smiles; docking takes structure_file and drug_smiles. 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 Pose Prediction	Structure Prediction	Predict ternary PROTAC linker poses (target + E3 ligase) via rigid-body prescan, GPU…
generate_conformers	Generate Conformers	Structure Generation / Sampling	Generate molecular conformers from SMILES notation
aqueous_solvation	Absolute Aqueous Solvation Free Energy	Molecular Property Prediction	Calculates the absolute solvation free energy of a molecule (SMILES) or peptide (HELM) in…
predict_protein_properties	Protein & Peptide Property Profiler	Property Prediction	Predict key developability properties of a protein or peptide directly from its sequence…
relative_fe	Relative Free Energy	Binding Free Energy	Calculates the relative free energy between two similar molecules in water using alchemical…
relative_fe_uaa	Relative Free Energy for (Unnatural) Amino Acids	Binding Free Energy	Calculates the relative free energy between two (unnatural) amino acid structures in water…
solvent_transfer_free_energy	Solvent Transfer Free Energy	Molecular Property Prediction	Calculates the transfer free energy of a molecule (SMILES) or peptide (HELM) between two…
nonaqueous_solvation	Absolute Nonaqueous Solvation Free Energy	Molecular Property Prediction	Calculates the absolute solvation free energy of a molecule (SMILES) or peptide (HELM) in a…
reaction_free_energy	Aqueous Reaction Free Energy	Molecular Property Prediction	Calculates the reaction free energy in aqueous solution.
deprotonation_fe	Deprotonation Free Energy	Molecular Property Prediction	Calculates the deprotonation free energy in aqueous solution (pKa proxy). Accepts SMILES or…
absolute_binding	Absolute Binding Free Energy	Binding Free Energy	Calculates the absolute binding free energy of a ligand to a protein in aqueous solution.
relative_binding	Relative Binding Free Energy	Binding Free Energy	Calculates the relative binding free energy between two ligands to a protein in aqueous…
lipid_permeation	Lipid Permeation Free Energy	Molecular Property Prediction	Calculates the free energy of a molecule (SMILES) or peptide (HELM) permeating through a lipid…
lipid_permeation_rfe	Relative Lipid Permeation Free Energy	Molecular Property Prediction	Calculates the relative free energy of permeation through a lipid bilayer between two…
predict_solubility	Aqueous Solubility ML Prediction	Property Prediction	Predict aqueous solubility (logS) of a molecule from its SMILES string using an XGBoost model…
predict_admet	ADMET ML Prediction	Property Prediction	Predict a multi-endpoint ADMET profile with traffic-light triage, MPO scoring, and…
covalent_docking	Covalent Docking	Structure-Based Drug Design	Covalent docking into a target residue you name — the platform has no pocket finding…
docking	Docking	Structure-Based Drug Design	Docking into a binding site you supply — the platform has no pocket finding and will not…
sequential_docking	Sequential Docking	Structure-Based Drug Design	Multi-stage docking where every non-covalent stage needs its own binding site centre — the…
opal_ml_optimize	Optimize Pose	Structure-Based Drug Design	LBFGS-relax the ligand or binder inside the frozen receptor with the OPAL ML neural potential…
opal_ml_score	Score Pose	Structure-Based Drug Design	Compute protein-ligand interaction energy using OPAL ML single-point energies. Fast scoring on…
peptide_structure	Peptide 3D Structure from HELM	Structure Generation	Generate 3D structures from HELM notation for linear and cyclic peptides, including…
protein_mutation_ddg_fold	Protein Mutation ΔΔG Fold	Free Energy Methods	Predict the change in folding free energy on amino-acid mutation (natural AAs + 14 ncAAs: Aib…
boltz_prediction	Boltz-2 Structure + Affinity Prediction	Structure Predictions	Co-fold up to 12 protein chains with up to 8 cofactors and 1 ligand using Boltz-2. The minimal…
boltz_ppi	Boltz-2 Protein-Protein Binding Surfaces	Structure Predictions	Predict the K most favorable protein-protein binding surfaces (default K=5) from two protein…
boltz_macrocycle	Macrocycle & Cyclic-Peptide affinity scoring	Structure Predictions	Predict how a macrocyclic or stapled peptide binds a protein receptor. The binder is a…
chai_prediction	Chai-1 Structure Prediction	Structure Predictions	All-atom co-folding with Chai-1 (Apache-2.0). Supports protein, RNA, DNA, and small-molecule…
openfold3_prediction	OpenFold3 Structure Prediction	Structure Predictions	AF3-parity all-atom structure prediction with OpenFold3 (Apache-2.0). Within experimental…
openfold2_prediction	OpenFold2 Structure Prediction	Structure Predictions	Single-chain and multimer protein structure prediction with OpenFold2 (Apache-2.0). OpenFold2…
mpnn_design	MPNN Sequence Design (Inverse Folding)	Structure Predictions	Design new amino-acid sequences that fold to a backbone structure you provide (inverse…
mpnn_stability	ThermoMPNN — Mutation Stability (ΔΔG)	Structure Predictions	Predict how mutations change a protein's folding stability (ΔΔG, in kcal/mol) from its…
esm2_embed	ESM-2 Sequence Embeddings	Protein Embeddings	Turn protein sequences into ESM-2 embeddings — numeric vectors that capture each protein's…
esm2_mutation_score	ESM-2 Mutation Scoring (Zero-Shot)	Protein Mutation Scoring	Score how point mutations affect a protein, with no training data needed. ESM-2 rates each…
esmfold_predict	ESMFold — Fast Single-Chain Structure Prediction	Structure Predictions	Predict the 3D structure of a single protein chain straight from its sequence, with no MSA…
crystal_prediction	Crystal Structure Prediction	Structure Generation / Sampling	Predict organic crystal structures from a SMILES string or an uploaded molecular geometry…

---

1. PROTAC Ternary Pose Prediction (protac_pose_prediction)

Description: Predict ternary PROTAC linker poses (target + E3 ligase) via rigid-body prescan, GPU minimization, and cross-PDB GBM scoring. Optional experimental ground-truth comparison reports topological PROTAC and PROTAC+pocket RMSD.

Category: Structure Prediction · Submission modes: single

Input Schema

Field	Type	Required	Default	Description
job_id	string	no	—	Caller-supplied job id (used for the per-job working dir on the shared volume).
target_pdb_content	file	yes	—	Target protein + warhead — upload a PDB file.
e3_pdb_content	file	yes	—	E3 ligase + recruiter — upload a PDB file.
linker_smiles	string	yes	—	Linker SMILES fragment with attachment points ([:1]…[:2]) connecting warhead and recruiter.
warhead_attach_atom_idx	integer	yes	—	Warhead attachment atom index (0-based).
recruiter_attach_atom_idx	integer	yes	—	E3-recruiter attachment atom index (0-based).
target_chain	string	no	A	Target protein chain id(s); single 'A' or comma-separated 'B,C,D'.
e3_chain_label	string	no	B	E3 protein chain id(s); single or comma-separated.
warhead_chain	string	no	X	Chain ID of the warhead ligand within the target PDB (the small molecule bound to the target protein). Single chain id.
recruiter_chain	string	no	Y	Chain ID of the E3-recruiter ligand within the E3 PDB (the small molecule bound to the E3 ligase). Single chain id.
preset	string	no	medium	Angular search preset (dropdown). 'quick' = coarse 20-deg grid (~0.36x compute, fast/low-accuracy demo); 'medium' = 15-deg grid (production reference the GBM ranker was trained on); 'long' = 10-deg grid, theta_max=110 deg to recover high-theta experimental poses (slowest). One of quick, medium, long.
n_prescan_chunks	integer	no	8	Parallel prescan distance-bin chunks. Range 1–16.
warhead_smiles	string	no	—	Warhead SMILES template (may contain an attachment point; correct bond orders).
e3_anchor_smiles	string	no	—	E3-anchor SMILES template (may contain an attachment point; correct bond orders).
gt_complex_pdb_content	file	no	—	Optional experimental ground-truth ternary complex — upload a PDB file for RMSD comparison.
gt_ligand_resname	string	no	—	Ground-truth PROTAC ligand residue name (required if gt_complex_pdb_content given).
gt_target_chain	string	no	A	Ground-truth target chain id.
gt_e3_chain	string	no	B	Ground-truth E3 chain id(s).
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": "OCCOCCOCC",
  "warhead_attach_atom_idx": 0,
  "recruiter_attach_atom_idx": 0,
  "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	Fast coarse-grid demo on the best-resolved VHL system (degrader MS67, RCSB 7JTP).
6sis-brd4-vhl-quick	BRD4-VHL (6SIS) - quick	Fast coarse-grid demo (macroPROTAC-1, RCSB 6SIS).
7jtp-wdr5-vhl	WDR5-VHL (7JTP) - medium	Production-reference run; the blind top-1 pick lands ~2.0 A from the 7JTP crystal pose…
6sis-brd4-vhl	BRD4-VHL (6SIS) - medium	Production-reference run; a sub-3 A pose sits in the top-5 (macroPROTAC-1, RCSB 6SIS).
8g1q-smarca2-vhl	SMARCA2-VHL (8G1Q) - medium	Production-reference run on SMARCA2 (cmpd_3603, RCSB 8G1Q).
8g1q-smarca2-vhl-long	SMARCA2-VHL (8G1Q) - long	Long preset (theta_max=110 deg) recovers 8G1Q's high-theta E3 pose that the medium grid's…

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2. Generate Conformers (generate_conformers)

Description: Generate molecular conformers from SMILES notation

Category: Structure Generation / Sampling · 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	Generate five 3D conformers of aspirin (acetylsalicylic acid) from its SMILES using RDKit ETKDG.
ibuprofen-conformers	Ibuprofen conformers	Generate five 3D conformers of ibuprofen from its SMILES using RDKit ETKDG.
caffeine-conformers	Caffeine conformers	Generate five 3D conformers of caffeine, a small rigid heteroaromatic molecule.

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3. Absolute Aqueous Solvation Free Energy (aqueous_solvation)

Description: Calculates the absolute solvation free energy of a molecule (SMILES) or peptide (HELM) in water using alchemical transformations. Provide exactly one of 'smiles' or 'helm'.

Category: Molecular Property Prediction · Submission modes: single, batch, workflow_node

Input Schema

Field	Type	Required	Default	Description
smiles	string	no	—	SMILES string of the molecule. 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 'smiles'. Full syntax: https://docs.azulenelabs.com/reference/helm/
conformer_method	string	no	etkdg	3D conformer generator for HELM input: 'etkdg' (fast, default) or 'xtb' (GFN2-xTB, slower, better geometries). Ignored when SMILES input is used. One of etkdg, xtb.
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	0.08	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	Absolute hydration free energy of ethanol by alchemical decoupling in explicit water (short…
methanol-aqueous-solvation	Methanol hydration	Hydration free energy of methanol, a classic small FreeSolv reference molecule.
toluene-aqueous-solvation	Toluene hydration	Hydration free energy of toluene, a hydrophobic aromatic FreeSolv reference.
methane-aqueous-solvation	Methane hydration free energy	Hydration free energy of methane — the canonical apolar-solvation benchmark, and the simplest…

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4. Protein & Peptide Property Profiler (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: Property Prediction · 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/reference/helm/
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 -…

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5. Relative Free Energy (relative_fe)

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

Category: Binding Free Energy · 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 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

{
  "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…

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6. Relative Free Energy for (Unnatural) Amino Acids (relative_fe_uaa)

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

Category: Binding Free Energy · Submission modes: single

Input Schema

Field	Type	Required	Default	Description
sequence_a	string	yes	—	Peptide sequence. Uppercase one-letter codes for natural AAs (e.g. 'GAG'). For UAAs: use library names in angle brackets (pF-Phe, oF-Phe, mF-Phe, Sar, N-Me-Ala), SMILES in angle brackets (e.g. '<NC(C)(C)C(=O)O>'), or lowercase placeholders with uaa_map (e.g. 'GxG' + uaa_map {'x':'pF-Phe'})
sequence_b	string	yes	—	Second peptide sequence (same format as first)
uaa_map	string	no	—	Maps a lowercase placeholder in the sequence to an unnatural amino acid, by library name or SMILES. Placeholders are one lowercase letter plus optional digits (e.g. x, a2).
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

{
  "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…

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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: Molecular Property Prediction · 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/reference/helm/
conformer_method	string	no	etkdg	3D conformer generator for HELM input: 'etkdg' (fast, default) or 'xtb' (GFN2-xTB, slower). Ignored when SMILES input is used. 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).

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8. Absolute Nonaqueous Solvation Free Energy (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: Molecular Property Prediction · 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/reference/helm/
conformer_method	string	no	etkdg	3D conformer generator for HELM input: 'etkdg' (fast, default) or 'xtb' (GFN2-xTB, slower). Ignored when SMILES input is used. 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…

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9. Aqueous Reaction Free Energy (reaction_free_energy)

Description: Calculates the reaction free energy in aqueous solution.

Category: Molecular Property Prediction · 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	false	Use xTB for gas phase calculations
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 tautomerisation	Keto-enol tautomerisation free energy for cyclohexanone to cyclohex-1-en-1-ol in aqueous…

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10. Deprotonation Free Energy (deprotonation_fe)

Description: Calculates the deprotonation free energy in aqueous solution (pKa proxy). Accepts SMILES or HELM for each side -- provide exactly one of (smiles_prot, helm_protonated) and exactly one of (smiles_deprot, helm_deprotonated).

Category: Molecular Property Prediction · 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}$$$$V2.0. Full syntax: https://docs.azulenelabs.com/reference/helm/
helm_deprotonated	string	no	—	HELM2 notation for the deprotonated peptide (A-), e.g. PEPTIDE1{A.G.F.K.L}$$$$V2.0. Full syntax: https://docs.azulenelabs.com/reference/helm/
conformer_method	string	no	etkdg	3D conformer generator for HELM inputs: 'etkdg' (fast, default) or 'xtb' (GFN2-xTB). 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	false	Use xTB for gas phase calculations
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 analogue)	Deprotonation free energy of imidazole to imidazolate (neutral N-H to anion). Imidazole is the…

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11. Absolute Binding Free Energy (absolute_binding)

Description: Calculates the absolute binding free energy of a ligand to a protein in aqueous solution.

Category: Binding Free Energy · 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 in the PDB file (only needed when extracting ligand from cocrystal PDB, auto-detected if omitted)
ligand_residue_name	string	no	—	Residue name of the ligand in the PDB file (only needed when extracting ligand from cocrystal PDB, auto-detected if omitted)
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	Solvent equilibration length of the ligand in nanoseconds per replica. Minimum 0.
solvent_prod_length	number	no	0.1	Solvent production length of the ligand in nanoseconds per replica. Minimum 0.
complex_equil_length	number	no	0.02	Complex equilibration length of the ligand-protein complex in nanoseconds per replica. Minimum 0.
complex_prod_length	number	no	0.1	Complex production length of the ligand-protein complex 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": "<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.

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12. Relative Binding Free Energy (relative_binding)

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

Category: Binding Free Energy · 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 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: Molecular Property Prediction · 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/reference/helm/
conformer_method	string	no	etkdg	3D conformer generator for HELM input: 'etkdg' (fast, default) or 'xtb' (GFN2-xTB, slower). 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…

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14. Relative Lipid 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: Molecular Property Prediction · 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. Aqueous Solubility ML Prediction (predict_solubility)

Description: Predict aqueous solubility (logS) of a molecule from its SMILES string using an XGBoost model trained on AqSolDB

Category: Property Prediction · Submission modes: single, batch, workflow_node

Input Schema

Field	Type	Required	Default	Description
smiles	string	yes	—	SMILES notation of the molecule

Example Input

{
  "smiles": "CCO"
}

Featured Examples

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

Example ID	Name	Description
caffeine-solubility	Caffeine solubility	Predict the aqueous solubility (logS) of caffeine, a small, highly soluble stimulant.
aspirin-solubility	Aspirin solubility	Predict the aqueous solubility (logS) of aspirin (acetylsalicylic acid), a moderately soluble…
ibuprofen-solubility	Ibuprofen solubility	Predict the aqueous solubility (logS) of ibuprofen, a lipophilic, poorly soluble drug.

---

16. ADMET ML Prediction (predict_admet)

Description: Predict a multi-endpoint ADMET profile with traffic-light triage, MPO scoring, and advance/optimize/drop bucketing from a SMILES string.

Category: Property Prediction · Submission modes: single, batch, workflow_node

Input Schema

Field	Type	Required	Default	Description
smiles	string	yes	—	SMILES notation of the molecule
endpoints	array	no	[]	Optional subset of ADMET endpoints to predict. Leave empty to run the full profile. Items are any of caco2, hia, pgp, bioavailability, solubility, lipophilicity, bbb, ppbr, vdss, cyp2c9, cyp2d6, cyp3a4, cyp2c9_sub, cyp2d6_sub, cyp3a4_sub, half_life, cl_hepatocyte, cl_microsome, herg, ames, ld50, dili.

Example Input

{
  "smiles": "CCO"
}

Featured Examples

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

Example ID	Name	Description
caffeine-admet	Caffeine ADMET	Run the full ADMET profile on caffeine, a small well-behaved molecule, for a quick…
imatinib-admet	Imatinib ADMET	Run the full ADMET profile on imatinib, a larger marketed kinase-inhibitor drug, to show the…
sulfamethoxazole-admet	Sulfamethoxazole ADMET	Run the full ADMET profile on sulfamethoxazole, a sulfonamide antibiotic, to show a different…

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17. Covalent Docking (covalent_docking)

Description: Covalent docking into a target residue you name — the platform has no pocket finding. target_resname, target_resid and target_atom are all required: you must already know which residue the warhead attacks. Physics-based, supporting boronic acids, acrylamides, nitriles, sulfonyl fluorides and other warhead classes. Three-stage scoring: pose generation, classical rescoring, and composite ranking.

Category: Structure-Based Drug Design · Submission modes: single

Input Schema

Field	Type	Required	Default	Description
structure_file	file	yes	—	Protein structure file (PDB or CIF format)
drug_smiles	string	yes	—	SMILES string of the covalent inhibitor (must contain a reactive warhead)
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. Use :1 atom map to mark the reactive atom. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. The warhead is detected from built-in SMARTS patterns and cannot be overridden.
protonate	boolean	no	true	Assign physiological protonation state at the target pH. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. The receptor and ligand are always protonated.
ph	number	no	7.4	pH for protonation state assignment. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. Protonation always runs at pH 7.4. Range 0–14.
n_conformers	integer	no	1	Number of ligand conformers to sample. Higher values improve results for flexible ligands but increase runtime. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. Range 1–10.
placement	string	no	combined	Ligand placement strategy. 'combined' is most thorough. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. One of combined, directed, tetrahedral.
max_steps	integer	no	700	Maximum optimization steps per orientation trial. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. Range 100–2000.
keep_cofactors	string	no	—	Comma-separated cofactor residue names to retain from the receptor (e.g. 'ZN' or 'ZN,NDP'). Leave empty to strip every non-standard residue. Names are PDB chemical-component IDs; no spaces after the commas. Waters are REMOVED when the pocket is cropped - add HOH to keep them, but that keeps every water in the structure, not a chosen one. Metal ions carry their formal charge into scoring; organic cofactors (NADPH, ATP, FAD...) are retained with correct protonation but scored as NEUTRAL, so absolute dG for an anionic cofactor is offset while ranking within one receptor is unaffected. CA is calcium, not the C-alpha atom.
extra_chains	string	no	—	Comma-separated additional chain IDs to include (e.g. 'B' or 'B,C').
optimize_and_score	boolean	no	false	Run end-to-end: after covalent docking, automatically chain GPU-accelerated geometry optimization (opal_ml_optimize) and ML scoring (opal_ml_score). Results are returned as the final ML-scoring envelope with structures and scores ranked for binding-affinity inspection. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result.
opt_fmax	number	no	0.05	Convergence force threshold (eV/Å) for GPU optimization. Only used when Optimize + Score is enabled. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. Range 0.005–1.
opt_maxiter	integer	no	200	Maximum optimization iterations per pose. Only used when Optimize + Score is enabled. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. Range 10–2000.
top_k	integer	no	5	Number of top-ranked covalent poses to forward into GPU optimization + scoring. Only used when Optimize + Score is enabled. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. Range 1–20.

Example Input

{
  "structure_file": "<local path or storage key>",
  "drug_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…

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18. Docking (docking)

Description: Docking into a binding site you supply — the platform has no pocket finding and will not locate one for you. Non-covalent docking of small molecules, fragments or cofactors; binding_site_center is required. The centre is in Angstroms in the uploaded structure's OWN coordinate frame — a centre taken from a reference crystal, or from a predicted model (which uses its own arbitrary frame and numbers residues from 1), does not carry over, so derive it from the file you are uploading. A centre with no protein atom within 8 A of it is rejected at submission rather than docked into bulk solvent. If you do not know where the ligand binds, boltz_prediction co-folds protein and ligand from sequence and needs no pocket at all; it is not a substitute for physics-based docking, but it is the only route when the site is unknown.

Category: Structure-Based Drug Design · Submission modes: single, batch, workflow_node

Input Schema

Field	Type	Required	Default	Description
structure_file	file	yes	—	Protein structure file (PDB or CIF format)
drug_smiles	string	no	—	SMILES string of the ligand or cofactor to dock. Provide this OR 'helm'.
helm	string	no	—	Cyclic or linear peptide in HELM2 notation, e.g. PEPTIDE1{A.G.F.K.L}$$$$V2.0 — provide this OR 'drug_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/reference/helm/
ligand_file	file	no	—	Ligand as a file (SDF, MOL, MOL2 or PDB) — provide this OR 'drug_smiles' OR 'helm'. Only the molecule is taken from it, not its coordinates: docking builds its own conformer ensemble in the pocket, and for a macrocycle it regenerates the ring conformers regardless. A multi-record SDF docks the first record only. This is the field to use for a structure produced by 'peptide_structure', which returns one directly as 'structure_sdf_url'; results from before 2026-08-19 carry it inside 'results_file' as structure.sdf instead. 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 cyclic-peptide/macrocycle sampling arm. 'auto' (default) turns it on when the ligand has a flexible ring larger than 8 atoms — which is what you want, and what an ordinary small molecule never triggers. The arm uses ETKDGv3 macrocycle torsions, a larger conformer pool, and seeds the generative samples across several distinct ring conformers, because a macrocycle's ring pucker is otherwise frozen at the input seed. It is markedly slower than small-molecule docking. 'off' forces the small-molecule settings even for a macrocycle. 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. Has NO effect when binding_site_center is set, which is required - it neither steers the docking nor changes the reported pose-to-pocket distance, which is measured from the center. Use it to derive a center: the wizard averages these residues' atoms and fills binding_site_center in. Numbers are as written in the uploaded file (author numbering, not a 1-based index). The match ignores chain, so a number present in several chains averages over all of them; insertion codes cannot be addressed - a residue number matches EVERY residue with that number regardless of insertion code, which matters for Kabat/Chothia-numbered antibodies (H100, H100A...). For an Fv, renumber to IMGT or set binding_site_center directly.
placement_radius	number	no	8	Radius (Angstroms) of the random placement sphere around the binding site center. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. Range 3–20.
n_conformers	integer	no	3	Number of ligand 3D conformers to generate. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. Range 1–20.
n_orientations	integer	no	8	Number of random orientations per conformer. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. Range 1–50.
keep_cofactors	string	no	—	Comma-separated cofactor residue names to retain from the receptor (e.g. 'ZN' or 'ZN,NDP'). Leave empty to strip every non-standard residue. Names are PDB chemical-component IDs; no spaces after the commas. Waters are REMOVED when the pocket is cropped - add HOH to keep them, but that keeps every water in the structure, not a chosen one. Metal ions carry their formal charge into scoring; organic cofactors (NADPH, ATP, FAD...) are retained with correct protonation but scored as NEUTRAL, so absolute dG for an anionic cofactor is offset while ranking within one receptor is unaffected. CA is calcium, not the C-alpha atom.
fmax	number	no	0.5	LBFGS force convergence threshold (eV/Ang). NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result.
max_steps	integer	no	700	Maximum optimization steps per trial. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. Range 100–2000.
use_electrostatics	boolean	no	true	Enable Coulomb interactions in classical scoring. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on 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. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. The receptor and ligand are always protonated.
ph	number	no	7.4	pH for ligand protonation state assignment. NOT CURRENTLY IMPLEMENTED — this value is accepted and then ignored; it has no effect on the result. Protonation always runs at pH 7.4. Range 0–14.

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>",
  "drug_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-benzene	Benzene → T4 lysozyme L99A cavity	Non-covalent docking of benzene into the engineered hydrophobic cavity of T4 lysozyme L99A…
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-cyclised 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 cyclised 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…

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19. Sequential Docking (sequential_docking)

Description: Multi-stage docking where every non-covalent stage needs its own binding site centre — the platform will not find one for you. Dock cofactors, fragments or ligands one at a time; each stage uses the previous result as the frozen receptor for the next, and covalent and non-covalent stages can be mixed. A covalent stage derives its centre from anchor_atom_name instead; a non-covalent stage with neither is rejected at submission, because it would otherwise be skipped and contribute nothing to the result. As with docking, centres are in Angstroms in the uploaded structure's own frame.

Category: Structure-Based Drug Design · 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 from the receptor (e.g. 'ZN' or 'ZN,NDP'). Leave empty to strip every non-standard residue. Names are PDB chemical-component IDs; no spaces after the commas. Waters are REMOVED when the pocket is cropped - add HOH to keep them, but that keeps every water in the structure, not a chosen one. Metal ions carry their formal charge into scoring; organic cofactors (NADPH, ATP, FAD...) are retained with correct protonation but scored as NEUTRAL, so absolute dG for an anionic cofactor is offset while ranking within one receptor is unaffected. CA is calcium, not the C-alpha atom.

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…

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20. Optimize Pose (opal_ml_optimize)

Description: LBFGS-relax the ligand or binder inside the frozen receptor with the OPAL ML neural potential, then score the relaxed geometry and return the usual scoring envelope. opt_fmax and opt_maxiter control the relaxation. The relaxed complex is published as relaxed_complex_file, and a fresh opal_ml_inputs block points at it so this job can be chained into Score Pose — which is the point: scoring the pose you started from would report a post-relaxation number computed before the relaxation. If the relaxation fails for a pose, that pose is scored exactly as supplied and the result says so. Peptide, macrocycle and stapled-peptide binders are supported: name the binder chain in binder_chain, which switches the job to the peptide-complex path and makes ligand_smiles unnecessary — a peptide binder has no SMILES. On that peptide path the relaxation is newly enabled and changes the runtime: it previously failed and degraded silently to "not relaxed", returning in about a minute; a real relaxation of a macrocycle complex has been measured at 6-7 minutes. Check pose_relaxed_in_pocket on the result — it is the per-job answer to whether the relaxation actually ran, and it is more reliable than this description. Flexible target residues. By default the whole receptor is held rigid and only the binder moves. Set flexible_radius (or name residues with flexible_residues) to let pocket side chains relax with it — a macrocycle or peptide on a shallow protein-protein surface binds partly by side-chain accommodation, which a rigid pocket cannot represent. The default of 0 keeps the previous behaviour exactly. Read receptor_shift_a, n_flexible_residues and flexible_residues_resolved on the result to see what moved, and note that an induced-fit dG is systematically more negative than a rigid-pocket one and the two must not be compared. Macrocycle and non-canonical binders. A binder whose residues have no AMBER parameters — a macrocycle of non-canonical amino acids, a stapled peptide, or a single-residue ligand from a docking hand-off — is perceived as a MOLECULE instead: bond orders and pH 7.4 protonation come from ligand_smiles, and partial charges from the ligand force field. Supply ligand_smiles for those, or chain from the job that published it in its opal_ml_inputs block; without it the job refuses rather than scoring the binder with no electrostatics. binder_route on the result says which path ran.

Category: Structure-Based Drug Design · 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 this empty: the structure contains the molecule, and bond orders, protonation and charges are read from it — by RDKit's residue templates for a named peptide chain (what boltz_macrocycle and an RFpeptides design after ProteinMPNN emit), or from geometry plus the pose's own hydrogens for a single-residue ligand (what a docking hand-off publishes). Set it only when the perceived molecule is wrong, or when the pose is a bare heavy-atom skeleton with no residue names for a template to match. The result reports which route was used. A SMILES whose atom formula disagrees with the structure is refused rather than applied.
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/reference/helm/
drug_smiles	string	no	—	SMILES for the binder, a synonym of 'ligand_smiles' so a chain from Docking can forward the field under the name that job uses. Supply at most one of 'ligand_smiles', 'helm' and 'drug_smiles'.
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 inside the same structure — the thing being scored, as opposed to chain_id, which names the receptor. Supplying it switches the job to the peptide-complex path: the complex is split by chain, each side is protonated separately, and the receptor is cropped to the pocket. Use it for peptide, macrocycle and stapled-peptide binders; leave it unset for small molecules. Passing a peptide binder as chain_id instead does not raise an error — the job takes the small-molecule path, which perceives the ligand with RDKit, cannot represent a polymer, and returns an interaction energy that is not meaningful.
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 LBFGS steps for the in-pocket relaxation. Reaching this limit without converging is not an error — the geometry reached is scored.
flexible_radius	number	no	0	Let receptor side chains within this many Angstrom of the binder relax along with it, instead of holding the whole target rigid. 0 (the default) keeps every receptor atom frozen, which is what this job has always done — turn it on deliberately. A macrocycle or peptide on a shallow protein-protein surface binds partly BY side-chain accommodation, and a rigid pocket cannot represent that. 4 is a sensible pocket shell. Only side chains move: the backbone stays frozen unless you also set flexible_backbone, so this is a rotamer rearrangement, not a refold. The resulting dG is not comparable with a rigid-pocket run. It is an induced-fit interaction energy: the receptor's own strain cancels between the complex and the protein reference, but the cost of leaving its unbound conformation is not charged for, so the number comes out systematically more negative. Check receptor_shift_a and flexible_residues_resolved on the result to see how far the pocket actually moved and which residues did it. Range 0–12.
flexible_residues	string	no	—	Name the receptor residues to relax explicitly, as CHAIN:RESSEQ — for example A:145,A:41. Takes precedence over flexible_radius when both are given. Use the residue NUMBER, not its name: protonation relabels histidines to HID/HIE/HIP before the relaxation runs. A residue that is not in the receptor being scored (wrong chain, or cropped away with the rest of the protein) is reported in the warnings and stays frozen; if NONE of them match, the job fails and tells you which chains and residue ranges are actually present. The same caveat as flexible_radius applies to the number that comes back.
flexible_backbone	boolean	no	false	Also let the backbone of the flexible residues move (N, CA, C, O and their hydrogens), instead of side chains only. Off by default and worth leaving off: freezing the backbone is what keeps the relaxation a side-chain rearrangement rather than a slow unfolding of the pocket crop. Turn it on for a loop you expect to reorganise on binding. Has no effect unless flexible_radius or flexible_residues selected something.
crop_radius	number	no	10	For a PEPTIDE binder (binder_chain set): keep only receptor residues within this many Angstrom of the binder. 0 disables cropping. This is not an optimization — the interaction energy is a difference of two whole-receptor energies, and on a large receptor that difference is swamped by absolute-energy noise, which destroys ranking. Cropping is applied only above crop_min_atoms receptor atoms, because a fixed radius over-crops a small receptor and makes it worse. Ignored on the small-molecule path, which truncates around the ligand instead. Range 0–30.
interior_dielectric	number	no	1	Solute interior dielectric for the MM-GBSA correction on the PEPTIDE-binder path. 1 is the value the reported composite was benchmarked at, and it is the VACUUM-INTERIOR extreme of the defensible range — it maximises the desolvation penalty, which matters most for a charged binder. The sign of the composite is not robust to this choice. Measured on a cyclic-peptide/PD-L1 complex whose binder carries net +2: at eps_in=1 the MM-GBSA term is +286 and the composite is +21 (positive, i.e. reads as non-binding); at eps_in=2 it is +141 and the composite -124; at 4, +69 and -198. The term scales as 1/eps to within 1%, which is textbook Born behaviour rather than anything numerically wrong, and dg_nnp is flat across all three because the neural potential does not see this parameter. eps_in of 2-4 is the conventional range when charged groups are buried. Nothing here says which value is right — that is a modelling judgement, and 1 is defensible for a rigid-receptor single-structure protocol — but read the composite with the parameter in view, and prefer opal_ml_dg_nnp_kcal_mol if you want a number this knob does not move. Ignored on the small-molecule path. Range 1–20.
keep_cofactors	string	no	—	Comma-separated cofactor residue names to retain from the receptor (e.g. 'ZN' or 'ZN,NDP'). Leave empty to strip every non-standard residue. Names are PDB chemical-component IDs; no spaces after the commas. Waters are REMOVED when the pocket is cropped - add HOH to keep them, but that keeps every water in the structure, not a chosen one. Metal ions carry their formal charge into scoring; organic cofactors (NADPH, ATP, FAD...) are retained with correct protonation but scored as NEUTRAL, so absolute dG for an anionic cofactor is offset while ranking within one receptor is unaffected. CA is calcium, not the C-alpha atom.
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	GPU OPAL-ML geometry optimization of the Paxlovid–Mpro covalent complex (PDB 7RFS; bound…
t4-lysozyme-benzene	Optimize benzene in T4 lysozyme L99A	GPU OPAL-ML geometry optimization of benzene bound in the engineered hydrophobic cavity of T4…
cdk2-roscovitine	Optimize roscovitine in CDK2	GPU OPAL-ML geometry optimization of the clinical CDK inhibitor roscovitine (seliciclib) bound…
spsb2-cyclic-rgd-helm	Optimize cyclo-RGDINNNV in SPSB2 (HELM)	GPU OPAL-ML relaxation of a docked cyclic peptide in the SPSB2 SPRY-domain groove (PDB 5XN3)…
streptavidin-cyclic-hexapeptide-smiles	Optimize Ac-CHPQFC-NH2 in streptavidin (SMILES)	GPU OPAL-ML relaxation of a docked disulfide-cyclised hexapeptide in streptavidin (PDB 1SLD)…

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21. Score Pose (opal_ml_score)

Description: Compute protein-ligand interaction energy using OPAL ML single-point energies. Fast scoring on pre-optimized or raw structures. Peptide, macrocycle and stapled-peptide binders are supported as well: name the binder chain in binder_chain and the complex is scored by the peptide path — split by chain, protonated per side, receptor cropped to the pocket — instead of the small-molecule path, which cannot represent a polymer.

Category: Structure-Based Drug Design · 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 inside the same structure — the thing being scored, as opposed to chain_id, which names the receptor. Supplying it switches the job to the peptide-complex path: the complex is split by chain, each side is protonated separately, and the receptor is cropped to the pocket. Use it for peptide, macrocycle and stapled-peptide binders; leave it unset for small molecules. Passing a peptide binder as chain_id instead does not raise an error — the job takes the small-molecule path, which perceives the ligand with RDKit, cannot represent a polymer, and returns an interaction energy that is not meaningful.
ligand_smiles	string	no	—	OVERRIDE for the binder's chemistry. Normally leave this empty: the structure contains the molecule, and bond orders, protonation and charges are read from it — by RDKit's residue templates for a named peptide chain (what boltz_macrocycle and an RFpeptides design after ProteinMPNN emit), or from geometry plus the pose's own hydrogens for a single-residue ligand (what a docking hand-off publishes). Set it only when the perceived molecule is wrong, or when the pose is a bare heavy-atom skeleton with no residue names for a template to match. The result reports which route was used. A SMILES whose atom formula disagrees with the structure is refused rather than applied.
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/reference/helm/
drug_smiles	string	no	—	SMILES for the binder, a synonym of 'ligand_smiles' so a chain from Docking can forward the field under the name that job uses. Supply at most one of 'ligand_smiles', 'helm' and 'drug_smiles'.
crop_radius	number	no	10	For a PEPTIDE binder (binder_chain set): keep only receptor residues within this many Angstrom of the binder. 0 disables cropping. This is not an optimization — the interaction energy is a difference of two whole-receptor energies, and on a large receptor that difference is swamped by absolute-energy noise, which destroys ranking. Cropping is applied only above crop_min_atoms receptor atoms, because a fixed radius over-crops a small receptor and makes it worse. Ignored on the small-molecule path, which truncates around the ligand instead. Range 0–30.
interior_dielectric	number	no	1	Solute interior dielectric for the MM-GBSA correction on the PEPTIDE-binder path. 1 is the value the reported composite was benchmarked at, and it is the VACUUM-INTERIOR extreme of the defensible range — it maximises the desolvation penalty, which matters most for a charged binder. The sign of the composite is not robust to this choice. Measured on a cyclic-peptide/PD-L1 complex whose binder carries net +2: at eps_in=1 the MM-GBSA term is +286 and the composite is +21 (positive, i.e. reads as non-binding); at eps_in=2 it is +141 and the composite -124; at 4, +69 and -198. The term scales as 1/eps to within 1%, which is textbook Born behaviour rather than anything numerically wrong, and dg_nnp is flat across all three because the neural potential does not see this parameter. eps_in of 2-4 is the conventional range when charged groups are buried. Nothing here says which value is right — that is a modelling judgement, and 1 is defensible for a rigid-receptor single-structure protocol — but read the composite with the parameter in view, and prefer opal_ml_dg_nnp_kcal_mol if you want a number this knob does not move. Ignored on the small-molecule path. Range 1–20.
keep_cofactors	string	no	—	Comma-separated cofactor residue names to retain from the receptor (e.g. 'ZN' or 'ZN,NDP'). Leave empty to strip every non-standard residue. Names are PDB chemical-component IDs; no spaces after the commas. Waters are REMOVED when the pocket is cropped - add HOH to keep them, but that keeps every water in the structure, not a chosen one. Metal ions carry their formal charge into scoring; organic cofactors (NADPH, ATP, FAD...) are retained with correct protonation but scored as NEUTRAL, so absolute dG for an anionic cofactor is offset while ranking within one receptor is unaffected. CA is calcium, not the C-alpha atom.
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	OPAL-ML single-point interaction energy for benzene bound in the T4 lysozyme L99A cavity (PDB…
mpro-paxlovid	Score Paxlovid–Mpro complex	OPAL-ML single-point interaction energy for the Paxlovid–Mpro covalent complex (PDB 7RFS…
cdk2-roscovitine	Score roscovitine in CDK2	OPAL-ML single-point interaction energy for the clinical CDK inhibitor roscovitine…
spsb2-cyclic-rgd-helm	Score cyclo-RGDINNNV in SPSB2 (HELM)	OPAL-ML scoring of a docked cyclic peptide against SPSB2, with the binder's chemistry named in…
streptavidin-cyclic-hexapeptide-smiles	Score Ac-CHPQFC-NH2 in streptavidin (SMILES)	OPAL-ML scoring of a docked disulfide-cyclised hexapeptide against streptavidin, with the…

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22. Peptide 3D Structure from 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 · 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/reference/helm/
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…

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23. Protein Mutation ΔΔG Fold (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 share feflow.NonEquilibriumCyclingProtocol + Amber14SB + tip3p (with per-ncAA OpenMM XML overlays) and BAR for engine + force-field parity. 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 stabilises the fold.

Category: Free Energy Methods · Submission modes: single

Input Schema

Field	Type	Required	Default	Description
protein_pdb	file	yes	—	Input protein structure (PDB). Single-chain only in v1; multi-chain ambiguous mutations are rejected.
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/reference/helm/
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 MD duration (nanoseconds) for each context-flanked capped peptide before the FEP λ ramp. Dephases folded-context backbone torsions into a random-coil ensemble. Default 0.005 ns (=5 ps, matches the smoke equil_length_ns convention; ~1-3 s per peptide on CPU GBSA). Set 0.1 for the Aldeghi 2019 quantitative protocol. 0 runs minimisation only. (All trajectory timings in ns per stakeholder fix #3.) 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 that overrides several fields. 'smoke' (default) keeps user-supplied values. 'aldeghi_2019_quantitative' overrides equil_length_ns=5.0, n_neq_switches_per_direction=50, neq_switch_length_ns=0.05, protocol_repeats=3, unfolded_flank_size=0 (Ace-X-NMe), unfolded_relax_restrained_nvt_ns=0.05, unfolded_relax_unrestrained_nvt_ns=0.05, unfolded_relax_ns=0.1, unfolded_relax_implicit_solvent=false — the verbatim Aldeghi 2019 / Boresch & Karplus 1998 / feflow-test convention. One of smoke, aldeghi_2019_quantitative.
random_seed	integer	no	42	Deterministic seed for reproducible re-runs.
equil_length_ns	number	no	5	Equilibration length per endpoint in nanoseconds. Default 5 ns; 0.005 is a smoke value used for plumbing tests. Minimum 0.005.
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	3	Number of independent FEP repeats for uncertainty estimation. 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-y1g	ddG_fold: CLN025 Tyr1->Gly (reverts stabilization)	Folding free-energy change for a Tyr1->Gly mutation in CLN025 (5AWL), the hyperstable…

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24. 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: Structure Predictions · 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".
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
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…

---

25. Boltz-2 Protein-Protein Binding Surfaces (boltz_ppi)

Description: Predict the K most favorable protein-protein binding surfaces (default K=5) from two protein sequences using Boltz-2. Returns diverse representative interfaces (default 5), ranked by interface confidence, with per-residue interface lists. Combined chain length capped at 1500 residues.

Category: Structure Predictions · 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 add GPU time without improving the interface.
protein_b	object	yes	—	Second protein chain. Whichever of the two chains is shorter is treated as the binder when the binding-affinity estimate is computed.
n_surfaces	integer	no	—	Number of representative binding surfaces to return (1..20). Default 5.
n_diffusion_samples	integer	no	—	Boltz diffusion samples to run before clustering (2..50). Default 15.
runtime	object	no	—	PPI runtime knobs. {use_msa_server: bool=true, use_potentials: bool=false, no_kernels: bool=false}. Structures are always returned as PDB.
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.

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. One of server, empty, upload.

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. One of server, empty, upload.

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 boltz_ppi <example-id>, or fetch its inputs first with azulene examples download.

Example ID	Name	Description
boltz-ppi-barnase-barstar	Barnase-Barstar interface	Boltz-2 protein-protein binding-surface prediction for the classic barnase-barstar complex.
boltz-ppi-pd1-pdl1	PD-1 / PD-L1 immune checkpoint	Boltz-2 protein-protein binding-surface prediction for the PD-1 / PD-L1 immune checkpoint (PDB…
boltz-ppi-kras-raf1-rbd	KRAS / RAF1-RBD oncogenic interface	Boltz-2 protein-protein binding-surface prediction for the oncogenic RAS-effector interaction…

---

26. Macrocycle & Cyclic-Peptide affinity scoring (boltz_macrocycle)

Description: Predict how a macrocyclic or stapled peptide binds a protein receptor. The binder is a peptide chain, not a ligand SMILES — you give its one-letter sequence and declare non-standard residues (modifications) and crosslinks (bonds) on top of it. There is now a ligand_smiles field and it does NOT change that: it names the chemistry of a second, additional fold, and it cannot stand in for the sequence. That sequence plus its modifications and bonds already names every atom, so the same binder is co-folded a second time as a molecule, from a SMILES assembled out of the very same component definitions — ligand_arm turns that off, ligand_smiles or helm say where its chemistry comes from instead. Returns the best-scoring diverse poses (default 5), ranked by interface confidence. Poses are sampled under steric and geometry steering potentials, so the diffusion actively avoids atom clashes and distorted bond geometry. Combined receptor+binder length is capped at 1500 residues. Also returns an experimental binding-affinity estimate affinity_pkd (higher = tighter): use it to separate strong binders from weak ones, not to rank close analogs of one another. Returned poses are also scored with an OPAL-ML interaction energy (opal_ml_dg_nnp_kcal_mol, kcal/mol) — a machine-learned-potential interaction energy, not a calibrated binding free energy. Its implicit-solvent counterpart (opal_ml_dg_mmgbsa_kcal_mol) and the sum of the two (opal_ml_dg_kcal_mol) are returned alongside it. It is reported only where the binder's chemistry allows: a binder carrying non-canonical residues that have no protonation template, hydrocarbon staples among them, reports no energy rather than an unreliable one. The affinity estimate and the interface scores are unaffected either way. Every result is also packaged for hand-off: opal_ml_inputs carries the receptor and the pose set in the form the Score Pose and Optimize Pose tools take, so a macrocycle run can be fed straight into either one without re-uploading anything. That block comes from the ligand fold whenever it ran — from_arm says which — because both tools take a ligand, and a chemistry that was an INPUT is not one that had to be read back out of the coordinates. opal_ml_inputs_by_arm carries both folds' hand-offs if you would rather score the peptide complex the affinity describes. Coming the other way, a Peptide Structure (HELM to 3D) result pipes in here: pass its smiles as ligand_smiles, or the HELM itself as helm, and the ligand fold uses that chemistry instead of assembling its own.

Category: Structure Predictions · Submission modes: single

Input Schema

Field	Type	Required	Default	Description
receptor	object	yes	—	Target protein chain — JSON object (single chain, not an array). Minimal value: {"sequence": "<one-letter AA string>"}. id defaults to "A", msa.mode defaults to "server". Trim the receptor to the binding domain. Pasting a full-length UniProt sequence is the biggest quality lever on this tool and it fails quietly: a full-length receptor folds far worse than a domain-scoped one on the same target, and the interface you are scoring degrades with it. All three featured examples ship domain slices for that reason.
binder	object	yes	—	Macrocyclic / stapled peptide binder — JSON object, expressed as a peptide CHAIN, not a ligand SMILES. Minimal value: {"sequence": "<one-letter AA string>"}. id defaults to "B". Non-standard residues go in modifications; the crosslink itself goes in bonds. The binder MSA is always empty (a designed macrocycle has no homologues to align).
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, as [{kind, atom1: {residue, atom}, atom2: {residue, atom}}]. Residue indices are 1-based into binder.sequence. This is where staples, disulfides, lactams and thioethers go — they are side-chain bridges on a linear backbone, so they belong here and NOT in binder.cyclic. Leave atom empty and the canonical crosslink atom for that kind is used (SG for disulfide, CZ/CJ for the S5/R8 staple olefins).
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, with no affinity head. The peptide fold above is unchanged and still produces the binding affinity, the crosslink report and the poses shown in the viewer. This second fold exists for what comes AFTER prediction: Score Pose and Optimize Pose take a ligand, so a peptide pose has to be re-perceived as a molecule from its own coordinates — and that is where the chain breaks. On a stapled peptide, perception returned an amide as a hemiaminal and a hydrocarbon staple as a bicyclobutane, from a pose whose two staple carbons sat 0.49 A apart. A ring declared in a molecular graph cannot fail to close. Turning this off returns the single-fold result and roughly halves the runtime.
ligand_file	file	no	—	The binder as a 3D molecule file (SDF or MOL), read for its CHEMISTRY. This is what a Peptide Structure (HELM to 3D) result hands over — pass its structure_sdf_url here and the ligand fold uses that molecule instead of assembling one from the sequence. Multi-record files are fine: the records are conformers of one molecule and the first is read. It is NOT a starting conformer — the folder builds its own for a SMILES ligand and offers no way to supply one — so this changes what is folded, never where it starts. Checked against what binder.sequence, modifications and bonds describe, and you get a warning if they are 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 using the same PDB Chemical Component Dictionary entries the peptide fold uses, which is exact for every CCD code. Supply it to override that, for instance to state a protonation state of your own: a SMILES you write 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/reference/helm/
runtime	object	no	—	Runtime knobs. {use_msa_server: bool=true, use_potentials: bool=true, no_kernels: bool=false}. use_potentials steers the diffusion with VDW-overlap, geometry and ring-closure potentials, so poses come out clash-free and covalently sane; it is on by default and costs roughly 3x the sampling time. Set it false only if you need the fastest possible run and will filter poses yourself. Structures are always returned as PDB.
templates	array	no	—	Structural templates for the BINDER chain, as [{"url": <PDB or CIF file>, "chain_id": "B", "force": true, "threshold": 1.5}]. Use this when you already know the binder's bound conformation — a co-crystal, an NMR model, or a close homolog. Set force to true or the template does nothing: without it the structure only conditions the network weakly, and on a confident complex that moves the result by about 0.01 Å. With force, supplying the deposited conformation improved binder accuracy from 1.36 to 1.01 Å on our PD-L1 example. The same lever applied to a computationally generated conformer made it worse (1.36 → 3.38 Å), so template a structure you trust, not one you guessed. PDB and CIF only — never SDF. chain_id must name the binder: templating the receptor would replace the structure you asked to dock against.
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.

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.
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-crystallised macrocycle from PDB 7OUN against the PD-L1 IgV ectodomain. This is the one…
boltz-macrocycle-igg-fc-disulfide	IgG1 Fc + Fc-III disulfide-cyclised peptide	The Fc-III cyclic peptide against the IgG1 Fc CH2–CH3 interface (PDB 5DI8 family). Disulfide…

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27. Chai-1 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.

Category: Structure Predictions · Submission modes: single

Input Schema

Field	Type	Required	Default	Description
chains	array	yes	—	1..12 chains. Each: {id, sequence, type} where type is one of protein | rna | dna | glycan (default protein). id may be a list for a homo-multimer. Flat shape (no request wrapper), matching boltz_prediction.
ligands	array	no	—	0..8 ligands / cofactors. Each: {id, ccd} or {id, smiles} (exactly one of ccd / smiles).
templates	array	no	—	Optional structural templates: {url, chain_id?} referencing a signed .pdb / .cif upload.
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".
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, glycan, cyclic.

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

templates — one entry

Field	Type	Required	Default	Description
url	string	yes	—	Signed .pdb / .cif URL
chain_id	string	no	—	Chain this template applies to

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 (MSA-free)	MSA-free Chai-1 co-fold of the ABL1 kinase domain with imatinib (Gleevec) straight from…
chai-u1a-rna	U1A protein + U1 snRNA hairpin	All-atom protein-RNA co-fold of the U1A spliceosomal protein bound to its U1 snRNA stem-loop…
chai-er-estradiol	ER-α LBD homodimer + estradiol	ER-alpha ligand-binding-domain homodimer (one sequence over two chains) co-folded MSA-free…

---

28. OpenFold3 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.

Category: Structure Predictions · 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".
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…

---

29. OpenFold2 Structure Prediction (openfold2_prediction)

Description: Single-chain and multimer protein structure prediction with OpenFold2 (Apache-2.0). OpenFold2 weights and architecture.

Category: Structure Predictions · Submission modes: single

Input Schema

Field	Type	Required	Default	Description
proteins	array	yes	—	1..12 protein chains. Each: {id, sequence, msa:{mode}} where msa.mode is one of server | empty | upload. id may be a list for a homo-multimer. Flat shape (no request wrapper), matching boltz_prediction.
templates	array	no	—	Optional structural templates: {url, chain_id?}.
runtime	object	no	—	OpenFold2 runtime knobs. Defaults: use_msa_server=true, long_sequence_inference=true, use_deepspeed_evo_attention=true, num_recycles=3, output_format="pdb".
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".
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 MSA is built. upload is the only mode that currently gives OpenFold2 an alignment (supply msa.upload_url). server is NOT YET IMPLEMENTED and currently behaves as empty; it is still accepted so existing payloads keep working. empty is single-sequence inference - substantially less accurate than MSA-based AF2 for most natural proteins, and pLDDT/pTM are correspondingly less meaningful. The default is empty because that is what actually runs - defaulting to server would name the reassuring mode while delivering the worst one. One of server, empty, upload.

templates — one entry

Field	Type	Required	Default	Description
url	string	yes	—	Signed .pdb / .cif URL
chain_id	string	no	—	Chain this template applies to

Example Input

{
  "proteins": [
    {
      "id": "A",
      "sequence": "MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAK",
      "msa": {
        "mode": "server"
      }
    }
  ],
  "runtime": {
    "use_msa_server": true,
    "output_format": "pdb"
  },
  "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. NOTE: runs single-sequence today -…
openfold2-luciferase	Firefly luciferase (~550 aa)	Long single-chain firefly luciferase (~550 residues) exercising OpenFold2's long-sequence path…

---

30. MPNN 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: Structure Predictions · 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 to redesign, by chain ID (e.g. ["A"]). Leave empty to redesign every chain.
fixed_positions	object	no	—	Positions to keep at their original amino acid, listed per chain. A position that is not in the file is SILENTLY IGNORED - it will not be held fixed and the job still reports success. 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.
redesign_positions	object	no	—	Positions to redesign, listed per chain, leaving every other position fixed. A position that is not in the file is SILENTLY IGNORED, leaving it fixed instead of redesigned. 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. For a given chain, use this or fixed_positions, not both.
bias_aa	object	no	—	Nudge the design toward or away from specific amino acids at every designed position. Keys are one-letter amino-acid codes; positive values favour an amino acid, negative values disfavour it.
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 (0.01 to 2.0). Default 0.1. Lower values stay closer to the most likely, native-like sequence; higher values give 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…

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31. ThermoMPNN — Mutation Stability (ΔΔG) (mpnn_stability)

Description: Predict how mutations change a protein's folding stability (ΔΔG, in kcal/mol) from its structure, using ThermoMPNN. 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: Structure Predictions · 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.

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32. ESM-2 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: Protein Embeddings · 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	—	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).
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…

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33. ESM-2 Mutation Scoring (Zero-Shot) (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: Protein Mutation Scoring · 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	—	Scoring method: "masked_marginal" (default, more accurate but slower) or "wt_marginal" (faster but less precise).
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).

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34. ESMFold — Fast Single-Chain Structure Prediction (esmfold_predict)

Description: Predict the 3D structure of a single protein chain straight from its sequence, with no MSA required. Residues are numbered from 1, not from wherever your construct begins - but you do not have to correct that yourself: if you give a downstream job pocket residues or a covalent target residue in your reference numbering, submission renumbers them into this model's frame and tells you it did, provided the residue names make the shift unambiguous. Coordinates are a different matter: they are in the model's own arbitrary frame, so a binding_site_center taken from a reference crystal is meaningless against it and has to be recomputed from this model. ESMFold is fast and a good fit when you have just a sequence and want a quick fold, including for designed or unusual proteins. Returns the structure plus per-residue confidence (pLDDT) and an overall pTM score. Sequences can be up to 1024 residues.

Category: Structure Predictions · 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…

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35. Crystal Structure Prediction (crystal_prediction)

Description: Predict organic crystal structures from a SMILES string or an uploaded molecular geometry. Runs the az_crystals CSP pipeline (CPU orchestrator + GPU fan-out). Provide exactly one of smiles or molecule_filename. Optionally compare against an experimental CIF.

Category: Structure Generation / Sampling · 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	—	Molecular geometry file (.xyz/.cif/.mol/.sdf, any ASE-readable format). Provide exactly one of smiles or molecule_filename.
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. 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…

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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. pdb_file is now structure_file and ligand_smiles is now drug_smiles; chain_id and binding_site_center are required.	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.	—

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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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