chematic

Pure-Rust cheminformatics library for Python — SMILES parsing, 190+ descriptor values (71 functions), fingerprints, pKa prediction, ADMET profiling, and template-based retrosynthesis.

Installation

pip install chematic

Quick Start

import chematic

mol = chematic.from_smiles("CC(=O)Oc1ccccc1C(=O)O")  # aspirin

print(mol.mw)      # 180.16
print(mol.logp)    # 1.31
print(mol.tpsa)    # 63.6
print(mol.qed)     # 0.55

# New descriptors
print(mol.vabc)              # van der Waals volume (no 3D needed)
print(mol.schultz_mti)       # Schultz MTI
print(mol.gutman_mti)        # Gutman MTI*
print(mol.gravitational_index)  # gravitational index

# pKa prediction
print(mol.pka())   # {"most_acidic": 3.49, "most_basic": None}

# ADMET profile
print(mol.admet())
# {"bbb": False, "bbb_score": ..., "caco2": ..., "herg_risk": ..., "cyp3a4_risk": ...}

# Fingerprints (bytes, 2048-bit ECFP4)
fp = mol.ecfp4()

# Tanimoto similarity
mol2 = chematic.from_smiles("c1ccccc1")
sim = chematic.tanimoto(mol.ecfp4(), mol2.ecfp4())

# Natural-language property summary (for LLM / MCP agents)
print(mol.describe())

# Structural diff between two molecules
ibuprofen = chematic.from_smiles("CC(C)Cc1ccc(CC(C)C(=O)O)cc1")
d = mol.diff(ibuprofen)  # {"summary": "...", "delta_mw": 66.1, "delta_logp": 2.75, ...}

# SVG / PDF / EPS depiction
svg = mol.to_svg()
pdf_bytes = mol.to_pdf()   # bytes; requires pdf feature
eps_str   = mol.to_eps()   # PostScript string

# ChemicalJSON (Avogadro 2 / MolSSI)
cjson_str = mol.to_cjson(coords=[])   # coords: list of (x,y,z) tuples, optional
mol2, coords = chematic.from_cjson(cjson_str)

# Template-based retrosynthesis (60 retro-SMIRKS templates)
mol3 = chematic.from_smiles("CC(=O)Nc1ccccc1")  # acetanilide
results = mol3.retro_disconnect(max_results=5)
for r in results:
    print(r["template"], "→", r["precursors"])
# amide_secondary → ['CC(=O)O', 'Nc1ccccc1']

# Filter by reaction class
amides = mol3.retro_disconnect(reaction_class="AmideBond")

# Bulk substructure match against a pre-parsed Mol list (returns indices)
mols = [chematic.from_smiles(s) for s in ["CCO", "c1ccccc1O", "CC(=O)O"]]
hits = chematic.bulk.substructure_match("[OH]", mols)  # → [0, 1, 2]

# All descriptors as a dict (for Pandas)
import pandas as pd
smiles = ["CCO", "c1ccccc1", "CC(=O)O"]
df = pd.DataFrame([chematic.from_smiles(s).descriptors() for s in smiles])

# Opt-in v2 embedding pipeline: torsion-knowledge-aware distance geometry +
# stereo verification/repair + policy-gated force field, with full per-stage
# evidence (never just final coordinates)
config = chematic.PipelineV2Config.safe(
    force_field="mmff94_with_uff_fallback",
    stereo_policy="repair_and_verify",
    ring_torsion_policy="fail_closed",
)
try:
    result = mol.embed_pipeline_v2(config)
    coords = result["coords"]                       # same atom order as mol
    print(result["force_field"]["actual_force_field_used"])  # fallback if MMFF94 lacked params
    print(result["final_validation"]["sound"])
except chematic.PipelineV2Error as e:
    print(e.diagnostics["stage"], e.diagnostics["cause"])   # structured, not just a message

Features

  • Zero C/C++ dependencies — pure Rust, no RDKit or OpenBabel required
  • SMILES / MOL / SDF / ChemicalJSON parsing and writing
  • 190+ descriptor values (71 functions; MQN returns 42 values, BCUT2D / autocorr2d / geary / moran return multi-value arrays): MW, LogP (±0.01, 96.5% of 4,999-mol ChEMBL subset), TPSA (±0.1 Ų, 98.1%), QED, Fsp3, SA Score, HBD (100% vs RDKit, incl. S-H), vabc, schultz_mti, gutman_mti, gravitational_index
  • 14 fingerprint algorithms: ECFP2/4/6, FCFP4/6, MACCS, AtomPair, Torsion, …
  • pKa prediction (15 SMARTS rules — unique to chematic)
  • ADMET profile: BBB, Caco-2, hERG, CYP3A4
  • Template-based retrosynthesis: mol.retro_disconnect() — 60 retro-SMIRKS templates, SA Score ranked
  • SMARTS substructure search — chematic.smarts_match() and bulk.substructure_match(smarts, mols) (pre-parsed Mol list, returns indices)
  • SVG / PDF / EPS depiction: mol.to_svg(), mol.to_pdf(), mol.to_eps()
  • ChemicalJSON: mol.to_cjson(coords=[]), chematic.from_cjson(s) — Avogadro 2 / MolSSI compatible
  • Opt-in v2 embedding pipeline: mol.embed_pipeline_v2(config) — torsion-knowledge-aware distance geometry, stereo verify/repair, and policy-gated force field (PipelineV2Config), returning full per-stage evidence (embed stats, torsion knowledge/optimization reports, stereo before/after, force-field actual policy and fallback, final geometry validation, stage timings) instead of just coordinates; raises chematic.PipelineV2Error with structured .diagnostics on failure

RDKit compatibility

chematic.rdkit_compat provides a lightweight RDKit-compatible subset for environments where RDKit is unavailable (WASM, serverless, conda-free CI):

from chematic import rdkit_compat as Chem
from chematic.rdkit_compat import Descriptors, rdMolDescriptors, DataStructs

mol = Chem.MolFromSmiles("CC(=O)Oc1ccccc1C(=O)O")

# Descriptors
Descriptors.MolWt(mol)          # 180.16
rdMolDescriptors.CalcTPSA(mol)  # 63.6

# Fingerprint (ExplicitBitVect) with bitInfo
bitInfo = {}
fp = rdMolDescriptors.GetMorganFingerprintAsBitVect(mol, 2, nBits=2048, bitInfo=bitInfo)
fp.GetNumBits()                         # 2048
bitInfo                                 # {bit: ((atom_idx, radius), ...)}
DataStructs.TanimotoSimilarity(fp, fp)  # 1.0
DataStructs.BulkTanimotoSimilarity(fp, [fp])  # [1.0]

import numpy as np
arr = DataStructs.ConvertToNumpyArray(fp)  # (2048,) int8 for sklearn / PyTorch

# Atom / Bond traversal
for atom in mol.GetAtoms():
    atom.GetSymbol(), atom.GetAtomicNum(), atom.IsInRing()
for bond in mol.GetBonds():
    bond.GetBondType(), bond.GetBondTypeAsDouble(), bond.IsInRing()

# Ring information
ri = mol.GetRingInfo()
ri.NumRings()       # 1
ri.AtomRings()      # tuple of tuples of atom indices
ri.NumAtomRings(0)  # rings containing atom 0

# SDF I/O with SD properties
with Chem.SDWriter("out.sdf") as w:
    mol.SetProp("ID", "aspirin")
    w.write(mol)
for m in Chem.SDMolSupplier("out.sdf"):
    print(m.GetProp("ID"))

Unsupported options raise NotImplementedError or TypeError — they are never silently ignored.

Compatibility matrix

Area Status Notes
SMILES I/O ✅ Supported MolFromSmiles (aromaticity perceived when sanitize=True) / MolToSmiles
SDF I/O ✅ Supported SDMolSupplier / SDWriter + SD properties
Mol properties ✅ Supported Get/Set/Has/ClearProp, typed setters, GetPropsAsDict
Mol / Atom / Bond ✅ Supported read-only traversal (GetAtoms/GetBonds/GetAtomWithIdx/…)
RingInfo ✅ Supported SSSR-based; NumRings/AtomRings/BondRings/NumAtomRings/NumBondRings
Substructure 🟡 Partial SMARTS via chematic; match order may differ from RDKit (use set comparison)
Descriptors ✅ Supported MW/HBA/HBD exact, TPSA ±1.0, LogP ±0.5 vs RDKit (differential-tested)
Morgan fingerprint 🟡 Partial nBits folding + bitInfo shape-/origin-consistent, not RDKit bit-identical (FNV-1a vs MurmurHash)
DataStructs ✅ Supported TanimotoSimilarity/DiceSimilarity/BulkTanimotoSimilarity/ConvertToNumpyArray
RWMol / editing ❌ Unsupported read-only layer
useFeatures, useBondTypes=False 🔊 Fails loudly raise NotImplementedError instead of silently ignoring

A live differential suite (tests/test_rdkit_diff.py, auto-skipped when RDKit is absent) compares chematic against RDKit across descriptors, ring counts, SMARTS match counts, SDF round-trips, and Morgan self-similarity, writing an explainable diff to validation/results/rdkit_diff.jsonl.

chematic.rdkit_compat is not a full RDKit clone — it is a lightweight RDKit-compatible subset for common 2D cheminformatics workflows. See the full RDKit migration guide (compatibility matrix, differential-validation results, known divergences, and runnable examples).

License

MIT OR Apache-2.0

Project links

Data verified by PyPI on Sep 3, 2026
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Key dates

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  • Released:
Newer release available (1.0.5)

1 maintainer

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Avatar for kent.tokyo from gravatar.com kent.tokyo

Credits

Author: Kentaro Tanabe (kent-tokyo)

GitHub Statistics

Data verified by PyPI on Sep 3, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License, MIT License (MIT OR Apache-2.0)

Requires

Python >=3.9

Tags

cheminformatics smiles chemistry rdkit fingerprints drug-discovery molecular-descriptors wasm admet qed

Classifiers

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Release history Release notifications | RSS feed

1.0.5

18 files

1.0.4

18 files

1.0.3

18 files

1.0.2

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1.0.1

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1.0.0

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This release

0.89.0 This release

18 files

0.49.0

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0.48.0

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0.47.0

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0.46.0

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0.45.0

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0.44.0

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0.43.0

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0.42.0

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0.41.0

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0.40.0

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0.39.0

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0.38.0

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0.37.0

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0.36.0

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0.35.0

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0.34.0

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0.33.0

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0.31.0

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0.30.0

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0.29.0

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0.28.0

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0.27.0

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0.26.0

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0.25.0

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0.24.0

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0.23.0

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0.22.0

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0.21.0

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0.20.1

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0.20.0

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0.19.0

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0.18.0

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0.17.0

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0.16.0

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0.15.0

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0.14.1

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0.14.0

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0.13.0

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0.12.0

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0.11.0

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0.10.0

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0.9.0

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0.8.1

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0.8.0

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0.7.0

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0.6.0

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0.5.0

18 files

0.4.30

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0.4.29

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0.4.28

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0.4.22

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0.4.21

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0.4.20

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0.4.19

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0.4.18

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0.4.17

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0.4.16

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0.4.15

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0.4.14

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0.4.9

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0.4.8

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0.4.7

18 files

0.4.6

6 files

0.4.5

6 files

0.4.4

6 files

0.4.0

6 files

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