cosmolkit 0.2.8

Python bindings for COSMolKit

COSMolKit

COSMolKit is a Python molecular toolkit backed by a Rust core. It provides value-style molecule operations, SMILES/SDF/MOL2/XYZ workflows, 2D depiction, native 3D conformer generation, UFF/MMFF optimization, fingerprints, batch processing, Python pickle round-tripping, and protein-focused structural biology APIs.

The library is built around explicit behavior: supported operations return structured results, unsupported behavior fails visibly, and public molecule transforms are explicit about whether they return new values or mutate in place.

COSMolKit is designed for array-oriented structural data access, keeping molecular data efficient and natural for NumPy, PyTorch, and model-building workflows.

Documentation

• Python documentation: https://kit.cosmol.org/
• Rust crate notes: crates/cosmolkit/README.md

Installation

pip install cosmolkit

Core Concepts

• Value-style molecules: methods such as with_hydrogens(), without_hydrogens(), with_kekulized_bonds(), and with_2d_coordinates() return new molecule values.
• Explicit mutation: in-place Molecule operations always end with _. The trailing underscore has no other public Molecule meaning.
• Explicit errors: invalid input and unsupported behavior are surfaced as errors instead of silent fallbacks.
• Batch-native processing: MoleculeBatch keeps input order, supports structured per-record failures, and can run batch transforms and exports with configurable parallelism.
• Array-friendly data access: coordinates, bounds matrices, fingerprints, and graph features are exposed in forms that fit Python numerical workflows.
• Source-backed 3D workflows: conformer generation and UFF/MMFF optimization are available through the public Python API.

Value-Style Transformations

Normal molecule operations return new objects and do not mutate their inputs. This follows the same explicit-dataflow direction as modern dataframe libraries: users can reason about each transformation as a new value while COSMolKit can share unchanged internal storage efficiently.

from cosmolkit import Molecule

mol = Molecule.from_smiles("CCO")
mol_h = mol.with_hydrogens()

assert mol is not mol_h

Python Quick Start

from cosmolkit import Molecule, MoleculeBatch

mol = Molecule.from_smiles("c1ccccc1O")
mol_2d = mol.with_2d_coordinates()

print(mol_2d.to_smiles())
print(mol_2d.coordinates_2d())

mol_3d = mol.with_hydrogens().with_3d_conformer()
print(mol_3d.coordinates_3d().shape)

svg = mol_2d.to_svg(width=400, height=300)
mol_2d.write_png("phenol.png", width=400, height=300)

fp = mol.fingerprint_morgan(radius=2, n_bits=2048)
print(fp.on_bits())

batch = (
    MoleculeBatch.from_smiles_list(
        ["CCO", "c1ccccc1", "CC(=O)O"],
        sanitize=True,
        errors="keep",
    )
    .with_parallel_jobs(8)
    .with_progress_bar(False)
)

prepared = batch.with_hydrogens(errors="keep").with_2d_coordinates(errors="keep")
print(prepared.valid_mask())
print(prepared.to_smiles_list())

prepared.to_images(
    "molecule_images",
    format="png",
    size=(300, 300),
    errors="keep",
    filenames=["ethanol", "benzene", "acetate"],
)

Protein Structures

Use Protein when the workflow is focused on protein chains rather than the full structural table.

from cosmolkit import Protein

protein = Protein.from_pdb("1crn.pdb")

print(protein.num_chains())
print(protein.num_residues())
print(protein.num_atoms())

for chain in protein.chains():
    print(chain.index(), chain.kind(), len(chain))
    for residue in chain.residues():
        print(residue.name(), residue.kind(), len(residue))

SDF and Dataset Workflows

SdfDataset builds a lightweight index of SDF record byte ranges, so individual records and chunks can be read without loading an entire file into memory. Molfile-only readers such as Molecule.read_mol() follow RDKit MolFromMolBlock boundaries: they stop after the first M END line and leave trailing SDF data fields to the SDF APIs.

from cosmolkit import SdfDataset

dataset = SdfDataset.open("library.sdf")
print(len(dataset))

record = dataset[0]
mol = record.molecule()

for batch in dataset.batches(size=1024, errors="keep", n_jobs=8):
    smiles = batch.to_smiles_list()

Conformer Generation And Optimization

from cosmolkit import EmbedParameters, Molecule

mol = Molecule.from_smiles("CC(=O)NC").with_hydrogens()

params = EmbedParameters.etkdg_v3()
params.random_seed = 0xF00D
params.num_threads = 1
params.track_failures = True

embedded = mol.with_3d_conformer(params)
print(embedded.num_conformers())
print(embedded.coordinates_3d().shape)
print(params.failures)

multi = mol.with_3d_conformers(5, params)
print(multi.num_conformers())

if embedded.has_uff_params():
    uff = embedded.with_uff_optimized(max_iters=200)
    print(uff.energy())

if embedded.has_mmff_params():
    mmff = embedded.with_mmff_optimized(max_iters=200)
    print(mmff.needs_more())

with_3d_conformer() follows RDKit's ETKDG behavior for trusted molecular graphs: molecules without explicit hydrogens are embedded as heavy-atom-only conformers instead of failing or automatically adding hydrogens. Calling with_hydrogens() first is recommended for all-atom geometry, force-field optimization, and hydrogen-bond-sensitive workflows. Coordinate-only inputs such as XYZ blocks do not contain a bond topology and are not valid ETKDG inputs until a trusted graph has been constructed.

Feature Areas

• Molecular graph construction and inspection
• SMILES parsing and writing
• MOL/SDF reading and writing
• MOL2 reading with RDKit-style Mol2ParserParams
• XYZ block reading
• Hydrogen transforms and Kekulization
• Sanitization and chemistry problem detection
• 2D coordinate generation and SVG/PNG depiction
• Native 3D conformer generation with DG/KDG/ETDG/ETKDG parameter presets
• UFF/MMFF optimization of generated or imported 3D conformers
• Morgan and Avalon fingerprints
• Distance-geometry bounds matrices
• Substructure matching and SMARTS parse metadata
• Ordered batch transforms and exports
• Python pickle round-tripping for Molecule
• PDB/mmCIF molecule-block parsing and protein projection APIs
• Support-status metadata for public features

Design Principles

COSMolKit aims to be Python-friendly, batch-friendly, and suitable for model-building workflows.

• Correctness comes before breadth.
• Public transforms use value semantics.
• Mutation-capable workflows are explicit.
• Unsupported chemistry should fail clearly.
• RDKit-parity behavior is the correctness floor for supported cheminformatics features.
• High-throughput APIs should preserve input order and expose per-record failures.

Examples

Python examples live in python/examples/.

Development

Small focused Rust test filters may use the default debug profile while iterating:

cargo test -p cosmolkit-core --features op-contracts-strict <test-filter>

Large local runs, parity suites, and CI tests should use release mode with the same strict feature set:

cargo test -p cosmolkit-core --release --features op-contracts-strict

Release-mode testing keeps operation contracts and runtime invariants enabled through op-contracts-strict; optimized release builds for distribution use default features unless explicit runtime checks are requested.

Roadmap

Status labels:

• ✅ available in the public Python API
• 🧪 implemented or partially available, still being hardened
• 🚧 planned / not yet public

Chemistry Core

Goal: keep the supported molecular core correct before expanding breadth.

• ✅ Molecule, atom, and bond graph model
• ✅ SMILES parsing
• ✅ SMILES writing with RDKit-style writer options for supported branches
• ✅ Ring perception, valence handling, aromaticity, and Kekulization
• ✅ Hydrogen addition and removal
• ✅ Sanitization for supported chemistry workflows
• ✅ Stereochemistry inspection for supported atom and bond states
• ✅ Distance-geometry bounds matrices
• ✅ Native 3D conformer generation and UFF/MMFF post-optimization for supported molecules
• 🧪 Morgan fingerprints and Tanimoto similarity
• 🧪 Avalon fingerprints
• 🧪 Substructure matching and Python SMARTS parse metadata
• 🚧 Broader descriptor APIs such as formula, molecular weight, and ring statistics

File I/O and Depiction

Goal: make common molecule import, export, and visualization workflows usable from Python.

• ✅ MOL/SDF reading
• ✅ MOL2 reading
• ✅ XYZ block reading
• ✅ SDF dataset indexing for large files
• ✅ SDF writing for supported V2000/V3000 branches
• ✅ PDB block to molecule conversion
• ✅ mmCIF block to molecule conversion through the same molecule-conversion profile
• ✅ 2D coordinate generation
• ✅ SVG drawing
• ✅ PNG export
• 🧪 RDKit-style visual parity testing for supported depiction output
• 🚧 Annotation overlays and richer drawing customization
• ✅ 3D conformer generation and embedding APIs

Batch-Native Workflows

Goal: make high-throughput molecule preparation and export a core product identity.

• ✅ Ordered MoleculeBatch.from_smiles_list()
• ✅ Batch transforms for sanitization, hydrogens, Kekulization, and 2D coordinates
• ✅ Configurable parallelism with with_parallel_jobs()
• ✅ Configurable progress display with with_progress_bar()
• ✅ Per-record errors, valid masks, and error reports
• ✅ Batch SMILES, image, and SDF export paths
• 🧪 Golden parity tests for parallel batch behavior
• 🚧 More streaming and chunked dataset workflows

Protein and Structural Biology

Goal: provide practical Biopython-like structure workflows without forcing users through low-level structural tables.

• ✅ Protein.from_pdb() / Protein.from_mmcif() high-level entry points
• ✅ Protein chain, residue, and atom iteration
• ✅ Protein-only projection from broader structural data
• 🧪 PDB/mmCIF structural parsing
• 🚧 Selection utilities for chains, residues, atoms, and neighborhoods
• 🚧 Ligand, nucleic-acid, and mixed-structure ergonomic APIs

Python API and ML Readiness

Goal: expose verified molecular behavior through a practical Python interface.

• ✅ Value-style molecule transformations
• ✅ Graph, coordinate, fingerprint, and bounds-matrix accessors
• ✅ Python examples for drawing, SDF-to-SMILES, pickle round-tripping, batch processing, and proteins
• 🧪 Type stubs and documentation coverage
• 🚧 Stable model-ready graph exports
• 🚧 NumPy / PyTorch oriented adapters
• 🚧 Molecular tokenization and AI-native geometry helpers

Browser and Deployment

Goal: support lightweight chemistry workflows outside native Python processes.

• 🚧 WASM compilation target
• 🚧 JavaScript bindings
• 🚧 Browser-native SMILES/SDF parsing and depiction

Respect for RDKit

COSMolKit is developed with deep respect for RDKit and the broader open-source cheminformatics community. The goal is an independent Rust-native implementation that preserves interoperability and RDKit-parity behavior where appropriate, while offering a deterministic Python API and AI-native extension surface.