chematic 0.4.7

Pure-Rust cheminformatics — SMILES, fingerprints, 70+ descriptors, pKa, ADMET

chematic

Pure-Rust cheminformatics library for Python — SMILES parsing, 70+ molecular descriptors, fingerprints, pKa prediction, and ADMET profiling.

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

# 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())

# 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])

Features

• Zero C/C++ dependencies — pure Rust, no RDKit or OpenBabel required
• SMILES / MOL / SDF parsing and writing
• 70+ descriptors: MW, LogP, TPSA, QED, Fsp3, SA Score, and more
• 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
• SMARTS substructure search
• SVG depiction for Jupyter notebooks

License

MIT OR Apache-2.0