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chematic

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

The 1.0.27 Python wheel retains the atom-output and source-atom provenance APIs, corrected RDKit-compatible HBA profile, and plain-SMILES ring-closure writer fix introduced in v1.0.25. The new caller-managed atom tags are a Rust API; they are not exposed as a Python feature in this release. Compatibility and performance claims remain operation- and corpus-scoped.

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())

# Mol objects can be cached or sent to worker processes with pickle. Graph
# chemistry, stereo, R-groups, source metadata, and SD properties survive.
import pickle
restored = pickle.loads(pickle.dumps(mol))

# 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

# Reuse prepared MMFF94 terms across iterative energy/gradient calls
field = mol.mmff_force_field(coords)
energy, gradient = field.energy_gradient()
field.set_position_(0, [coords[0][0] + 0.01, *coords[0][1:]])
status, optimized_energy = field.minimize_()

Features

  • Zero C/C++ dependencies — pure Rust, no RDKit or OpenBabel required
  • SMILES / MOL / SDF / ChemicalJSON parsing and writing
  • Indexed SDF datasets — SdfDataset provides cheap record counts, metadata, random access, and bounded range reads without materializing every molecule
  • 190+ descriptor values (70+ functions; several return vectors): MW, LogP, TPSA, QED, Fsp3, SA Score, HBD, vabc, schultz_mti, gutman_mti, and gravitational_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() 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
  • Reusable MMFF94/UFF objects: mol.mmff_force_field(coords) and mol.uff_force_field(coords) retain prepared terms across energy, gradient, coordinate-update, and in-place minimization calls
  • Charge-conserving EEM: mol.eem_charges(coords) uses the published NEEMP model and preserves the formal-charge sum for ions

RDKit compatibility

The operation/profile boundaries and measured oracle lanes are listed in the Compatibility Contract dashboard. For persisted fingerprints, saved indices, canonical identity, stereo, and browser/Worker migration decisions, read the RDKit migration guide before changing a production workflow. Native ECFP and RDKit-compatible Morgan are separate profiles; rebuild stored fingerprints and indices under the chosen profile.

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

# Explain RDKit atom-pair / topological-torsion codes
code = mol.rdkit_atom_code(0)
chematic.explain_rdkit_atom_code(code)  # (symbol, branches, pi_electrons)
path_code = mol.rdkit_topological_torsion_path_code([0, 1, 2, 3])
chematic.explain_rdkit_topological_torsion_code(path_code)

# 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"))

# Large seekable SDF: index once, parse records only when requested
from chematic import SdfDataset
dataset = SdfDataset.open("out.sdf")
print(len(dataset), dataset.metadata(0).title)
print(dataset[-1].smiles)

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 GetProp(..., default=...), Set/Has/ClearProp, typed getters/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

Metadata

Release files for chematic 1.1.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for chematic 1.1.4
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Built distributions (wheels)

Table of built distributions (wheels) for chematic 1.1.4
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chematic-1.1.4-cp313-cp313-win_amd64.whl CPython 3.13 CPython 3.13 Windows x86-64 Details
chematic-1.1.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ x86-64 Details
chematic-1.1.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.13 CPython 3.13 Linux glibc 2.17+ ARM64 Details
chematic-1.1.4-cp313-cp313-macosx_11_0_arm64.whl CPython 3.13 CPython 3.13 macOS 11.0+ ARM64 Details
chematic-1.1.4-cp313-cp313-macosx_10_12_x86_64.whl CPython 3.13 CPython 3.13 macOS 10.12+ x86-64 Details
chematic-1.1.4-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
chematic-1.1.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ x86-64 Details
chematic-1.1.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ ARM64 Details
chematic-1.1.4-cp312-cp312-macosx_11_0_arm64.whl CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details
chematic-1.1.4-cp312-cp312-macosx_10_12_x86_64.whl CPython 3.12 CPython 3.12 macOS 10.12+ x86-64 Details
chematic-1.1.4-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
chematic-1.1.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ x86-64 Details
chematic-1.1.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ ARM64 Details
chematic-1.1.4-cp311-cp311-macosx_11_0_arm64.whl CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
chematic-1.1.4-cp311-cp311-macosx_10_12_x86_64.whl CPython 3.11 CPython 3.11 macOS 10.12+ x86-64 Details
chematic-1.1.4-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details
chematic-1.1.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.17+ x86-64 Details
chematic-1.1.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.10 CPython 3.10 Linux glibc 2.17+ ARM64 Details
chematic-1.1.4-cp310-cp310-macosx_11_0_arm64.whl CPython 3.10 CPython 3.10 macOS 11.0+ ARM64 Details
chematic-1.1.4-cp310-cp310-macosx_10_12_x86_64.whl CPython 3.10 CPython 3.10 macOS 10.12+ x86-64 Details
chematic-1.1.4-cp39-cp39-win_amd64.whl CPython 3.9 CPython 3.9 Windows x86-64 Details
chematic-1.1.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.9 CPython 3.9 Linux glibc 2.17+ x86-64 Details
chematic-1.1.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.9 CPython 3.9 Linux glibc 2.17+ ARM64 Details
chematic-1.1.4-cp39-cp39-macosx_11_0_arm64.whl CPython 3.9 CPython 3.9 macOS 11.0+ ARM64 Details
chematic-1.1.4-cp39-cp39-macosx_10_12_x86_64.whl CPython 3.9 CPython 3.9 macOS 10.12+ x86-64 Details

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Release history Release notifications | RSS feed

1.1.6

26 release files

1.1.5

26 release files

This release

1.1.4 This release

26 release files

1.1.3

26 release files

1.1.2

26 release files

1.1.1

26 release files

1.1.0

26 release files

0.9.0

18 release files

0.8.1

18 release files

0.8.0

18 release files

0.7.0

18 release files

0.6.0

18 release files

0.5.0

18 release files

0.4.9

18 release files

0.4.8

18 release files

0.4.7

18 release files

0.4.6

6 release files

0.4.5

6 release files

0.4.4

6 release files

0.4.0

6 release files

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