chematic
Pure-Rust cheminformatics library for Python — SMILES parsing, 190+ descriptor values (71 functions), fingerprints, pKa prediction, ADMET profiling, and template-based retrosynthesis.
The current 1.0.14 release line also includes bounded batch descriptor
output through bulk.descriptors_array(smiles, columns). Requested columns
are computed selectively and returned as typed NumPy arrays; invalid SMILES
remain excluded as documented.
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 (70+ functions; several return vectors): MW,
LogP, TPSA, QED, Fsp3, SA Score, HBD,
vabc,schultz_mti,gutman_mti, andgravitational_index. RDKit agreement is metric-specific; see the validation report. - 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()andbulk.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; raiseschematic.PipelineV2Errorwith structured.diagnosticson 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
1 maintainer
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