cosmolkit 0.1.2

Python bindings for COSMolKit

COSMolKit Python

COSMolKit is a Python package for molecule graph workflows, SMILES/SDF IO, coordinate access, Morgan fingerprints, molecule depiction, and high-throughput batch processing.

Current Python documentation: https://kit.cosmol.org/

API Model: Copy-On-Write (COW) Molecule Values

COSMolKit's Python API uses copy-on-write (COW) value semantics for molecule transforms. Methods such as with_hydrogens(), without_hydrogens(), with_kekulized_bonds(), and with_2d_coords() return a new Molecule; they do not mutate the original object in place.

This is intentionally different from common RDKit Python workflows, where many operations mutate an RWMol or update a molecule object directly. In COSMolKit, keep the returned value:

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

assert mol is not mol_h
print(mol.to_smiles())
print(mol_h.to_smiles())

Installation

pip install cosmolkit

Quick Start

from cosmolkit import Molecule

mol = Molecule.from_smiles("c1ccccc1O")
drawn = mol.with_2d_coords()

print(mol.to_smiles())
print(drawn.atoms()[0])

drawn.write_png("phenol.png", width=400, height=300)

Morgan fingerprints are exposed as RDKit-style sparse bit vectors. The on_bits() output is a list of bit indexes set to 1 inside a fixed-length binary vector, not a dense neural embedding:

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

print(fp.n_bits())
print(fp.on_bits())
print(fp.tanimoto(Molecule.from_smiles("c1ccccc1").fingerprint_morgan()))

Additional output mirrors RDKit's Morgan provenance helpers:

result = mol.fingerprint_morgan_with_output(radius=2, n_bits=2048)
info = result.additional_output()

print(result.fingerprint().on_bits())
print(info.atom_counts())
print(info.bit_info_map())

Chiral tags and bond orders are Python IntEnum values, so code can compare against typed constants instead of strings:

from cosmolkit import BondOrder, ChiralTag, Molecule

chiral = Molecule.from_smiles("F[C@H](Cl)Br")
print(chiral.to_smiles())
print(chiral.to_smiles(isomeric_smiles=False))

for atom in chiral.atoms():
    if atom.chiral_tag() != ChiralTag.CHI_UNSPECIFIED:
        print(atom.idx(), atom.chiral_tag().name)

for bond in Molecule.from_smiles("C=C").bonds():
    if bond.bond_type() == BondOrder.DOUBLE:
        print("double bond:", bond.begin_atom_idx(), bond.end_atom_idx())

Read-only maps such as BOND_ORDER_MAP and CHIRAL_TAG_MAP convert external string names to the enum members when needed.

Use Molecule.edit() when you want an explicit editing workflow:

editor = mol.edit()
cl = editor.add_atom("Cl")
editor.add_bond(0, cl, order="single")
mol2 = editor.commit()

Batch Workflows

from cosmolkit import BatchErrorMode, BatchErrorType, MoleculeBatch

smiles = ["CCO", "c1ccccc1", "not-smiles"]
batch = MoleculeBatch.from_smiles_list(
    smiles,
    errors=BatchErrorMode.KEEP,
).with_parallel_jobs(8)

for error in batch.errors():
    if error.error_type() == BatchErrorType.SMILES_PARSE:
        print(error.index(), error.message())

prepared = batch.add_hydrogens(errors=BatchErrorMode.KEEP).compute_2d_coords(
    errors=BatchErrorMode.KEEP,
)
report = prepared.to_images(
    "molecule_images",
    format="png",
    errors=BatchErrorMode.SKIP,
    filenames=["ethanol", "benzene", "invalid"],
)
sdf_files = prepared.to_sdf_files(
    "molecule_sdf_records",
    format="v2000",
    errors=BatchErrorMode.SKIP,
    filenames=["ethanol", "benzene", "invalid"],
)
fingerprints = prepared.fingerprint_morgan_list(n_bits=2048)

print(prepared.valid_mask())
print(prepared.errors())
print([fp.on_bits() if fp is not None else None for fp in fingerprints])
print([mol.to_smiles() if mol is not None else None for mol in prepared])
print(report)
print(sdf_files)

The errors option controls invalid records:

• errors="raise" raises on the first batch validation failure.
• errors="keep" preserves failed records and exposes structured errors.
• errors="skip" omits failed records from the returned result or export.

Batch SMILES output accepts formatting options:

canonical = prepared.to_smiles_list(canonical=True)
explicit = prepared.to_smiles_list(
    all_bonds_explicit=True,
    all_hs_explicit=True,
)
without_maps = prepared.to_smiles_list(ignore_atom_map_numbers=True)

Single-molecule SMILES output accepts the same writer options:

benzene = Molecule.from_smiles("c1ccccc1")
ethanol = Molecule.from_smiles("CCO")

print(benzene.to_smiles(kekule=True))
print(ethanol.to_smiles(all_bonds_explicit=True))
print(ethanol.to_smiles(canonical=False, rooted_at_atom=2))

with_parallel_jobs() configures the default worker count for later batch operations while keeping the batch value-style. Method-level n_jobs can still override it for one call, and prepared.to_list() returns list[Molecule | None] when a Python list is more convenient than iteration.

Directory exports accept optional filenames lists. Entries are aligned with the batch records, None keeps the default numbered filename, and missing extensions are filled from the selected output format.

SDF and Arrays

mol = Molecule.from_smiles("CCO").with_2d_coords()

sdf_text = mol.to_sdf_string(format="v2000")
restored = Molecule.read_sdf_record_from_str(sdf_text, coordinate_dim="2d")

coords = restored.coords_2d()
bounds = restored.dg_bounds_matrix()

print(coords.shape)
print(bounds.shape)

Main Features

• SMILES parsing and writing with Molecule.from_smiles() and to_smiles()
• RDKit-style SMILES writer options on single molecules and batches
• copy-on-write molecule value semantics for transforms
• SDF file and string IO
• atom and bond feature inspection
• hydrogen add/remove transforms
• Kekule bond representation
• CW/CCW chiral tags, chiral centers, and tetrahedral stereo inspection
• 2D coordinate generation and NumPy coordinate arrays
• distance-geometry bounds matrix export
• Morgan fingerprint bit vectors, Tanimoto similarity, and AdditionalOutput
• SVG and PNG molecule depictions
• ordered batch construction, transformation, filtering, and export
• batch Morgan fingerprint generation with Rust-side parallel scheduling
• batch-level default parallelism with with_parallel_jobs()
• Python-style batch iteration, slicing, integer-list selection, and boolean-mask selection
• custom per-record filenames for batch image and SDF directory exports
• explicit molecule editing with Molecule.edit()