chematic-wasm

WebAssembly bindings for chematic, a pure-Rust cheminformatics library.

Published to npm as @kent-tokyo/chematic.

Installation

npm install @kent-tokyo/chematic

Features

• Parse SMILES strings into molecule handles
• Molecular descriptors: MW, TPSA, LogP, Fsp3, QED, exact mass, rotatable bonds, HBD/HBA, aromatic ring count, Labute ASA
• Drug-likeness filters: Lipinski, Veber, Egan, REOS, Ghose
• EState indices (Hall & Kier 1991): per-atom values, sum/max/min
• Gasteiger-Marsili PEOE partial charges: per-heavy-atom charges
• VSA descriptors: SlogP_VSA (×12), SMR_VSA (×10), PEOE_VSA (×14)
• SA score: synthetic accessibility estimate [1, 10]
• Functional group identification (Ertl 2017 IFG)
• Canonical SMILES generation
• ECFP4/6, AtomPair, Torsion, and path fingerprints with Tanimoto similarity
• BRICS fragment count
• SDF/MOL block parsing
• Topological descriptors: Wiener index, Hall-Kier κ, χ connectivity indices, Bertz CT
• Shape descriptors (with 3D coordinates): PMI, NPR, radius of gyration, asphericity
• 2D SVG depiction with CPK colors and atom/bond highlighting
• SVG grid layout for multiple molecules
• Reaction SMILES/SMIRKS parsing and transform
• Add/remove explicit hydrogens
• embed_pipeline_v2_json: torsion-knowledge-aware 3D embedding + stereo verification/repair + policy-gated force field, mirroring the Python Mol.embed_pipeline_v2() binding — opt-in, not a default 3D API (usage)

Usage

import init, {
  parse_smiles,
  tanimoto_ecfp4,
  tanimoto_atom_pair,
  tanimoto_torsion,
  brics_fragment_count,
  gasteiger_charges_json,
  slogp_vsa_json,
  smr_vsa_json,
  peoe_vsa_json,
  identify_functional_groups,
} from '@kent-tokyo/chematic';

await init();

const mol = parse_smiles('CC(=O)Oc1ccccc1C(=O)O'); // aspirin

// Descriptors
console.log(mol.atom_count());          // 13
console.log(mol.molecular_weight());    // ~180.16
console.log(mol.formula());             // "C9H8O4"
console.log(mol.tpsa());               // ~63.6
console.log(mol.logp_crippen());        // ~1.2
console.log(mol.fsp3());               // ~0.111
console.log(mol.qed());                // drug-likeness score [0, 1]
console.log(mol.exact_mass());         // ~180.042
console.log(mol.hbd_count());          // 1
console.log(mol.hba_count());          // 4
console.log(mol.rotatable_bond_count()); // 3
console.log(mol.aromatic_ring_count()); // 1
console.log(mol.lipinski_passes());     // true
console.log(mol.canonical_smiles());    // canonical SMILES string

// BRICS fragmentation
console.log(brics_fragment_count(mol)); // ≥ 2

// Fingerprint similarity
const caffeine = parse_smiles('Cn1cnc2c1c(=O)n(c(=O)n2C)C');
console.log(tanimoto_ecfp4(mol, caffeine));    // ECFP4 Tanimoto
console.log(tanimoto_atom_pair(mol, caffeine)); // AtomPair Tanimoto
console.log(tanimoto_torsion(mol, caffeine));   // Torsion Tanimoto

// Sprint Q: New descriptors (v0.1.15)
console.log(mol.sa_score());                     // synthetic accessibility [1,10]
console.log(mol.labute_asa());                   // Labute approx. surface area (Ų)

// Gasteiger partial charges (per heavy atom)
const charges = JSON.parse(gasteiger_charges_json(mol));
console.log(charges); // [-0.08, 0.12, -0.43, ...]

// VSA descriptor bins
const slogpVsa = JSON.parse(slogp_vsa_json(mol));
const smrVsa   = JSON.parse(smr_vsa_json(mol));
const peoeVsa  = JSON.parse(peoe_vsa_json(mol));

// Functional group identification
const ifg = JSON.parse(identify_functional_groups(mol));
console.log(ifg); // [{"atoms":[1,2,3],"types":"OC=O"}, ...]

3D embedding (embed_pipeline_v2_json)

Opt-in — does not change behavior of any existing 3D API (generate_coords, generate_and_minimize_*, etc.), which remain the defaults.

const response = JSON.parse(embed_pipeline_v2_json(mol, JSON.stringify({
  embedSeed: 7,
  maxAttempts: 8,
  embedTimeoutMs: null,
  useExpTorsions: false,
  useSmallRingTorsions: false,
  useMacrocycleTorsions: false,
  useMacrocycle14Bounds: false,
  includeLegacyTorsionHeuristic: false,
  stereoPolicy: "ignore",
  failOnUnevaluableStereo: false,
  forceFieldPolicy: "none",
  forceFieldMaxIterations: 200,
  gateMmff94TorsionOop: false,
  ringTorsionPolicy: "fail_closed",
  totalTimeoutMs: null,
})));
// response.ok, response.result / response.error — same shape as
// Mol.embed_pipeline_v2() in the Python binding.

Verified working end-to-end under real WASM (both wasm-pack --target nodejs and --target web, the latter being what this package's npm build actually uses) — success, typed-failure, and typed-timeout paths all return real results. embedTimeoutMs/totalTimeoutMs use a monotonic clock that's portable across native and wasm32-unknown-unknown (web-time, backed by Performance.now() in the browser); this does not claim identical wall-clock precision across every JS engine, only that the value is finite, non-negative, and enforced correctly on all of them.

Version History

v0.1.94 (2026-06-12):

• SA Score corpus expanded: 188 FDA molecules (1415 unique fragments)
• Enhanced fingerprints: True MHFP, True ERG, path FP with bond types
• Full multi-sphere CIP stereochemistry for R/S assignment
• InChI stereo layer round-trip support (tetrahedral and E/Z)

v0.1.93 (2026-06-12):

• Full multi-sphere CIP priority rules
• Correct stereochemistry assignment for complex chiral centers

v0.1.92 (2026-06-12):

• InChI stereo layer parsing (tetrahedral /t and E/Z /b)
• Path fingerprint with bond type interleaving

v0.1.91 (2026-06-12):

• True MHFP (structural fragment hashing)
• True ERG (Ertl 2017 functional group detection)

Bundle Size

~500 KB gzip / ~1.3 MB raw (reduced from ~819 KB gzip in v0.4.17, -38.5%).

PNG rasterization (tiny_skia) is excluded from the WASM build — use SVG output instead. All SVG depiction APIs remain fully available.

Building from source

wasm-pack build --target bundler --release