chematic

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A pure-Rust cheminformatics library targeting RDKit feature parity — with zero C/C++ dependencies.

Why does zero C/C++ matter? RDKit.js, Indigo WASM, and OpenBabel all ship C++ code compiled via Emscripten. That means 30–50 MB WASM binaries, complex build toolchains, and platform-specific build failures. chematic compiles to a ~550 KB WASM bundle with a single wasm-pack build — no cmake, no clang, no -sys crates, no build.rs C compilation anywhere in the dependency tree.

Live Demo

https://kent-tokyo.github.io/chematic/ — Interactive descriptor calculator, drug-likeness rules, fingerprint similarity, 3D viewer, and reaction schemes running entirely in your browser via WebAssembly.

Design Goals

Pure Rust, zero C/C++ FFI — guaranteed No rdkit-sys, no openbabel-sys, no cc build dependencies, no bindgen. Every algorithm — from SSSR ring perception to ECFP fingerprints to force-field minimization — is implemented in 100% safe Rust. The entire dependency tree is verified FFI-free.

WASM-compatible and lightweight All crates compile to wasm32-unknown-unknown without modification. The npm package @kent-tokyo/chematic is ~550 KB versus 30–50 MB for C++ FFI alternatives. No cmake, no emcc, no Emscripten toolchain required.

80+ WebAssembly API endpoints The WASM layer exposes 80 functions covering descriptors, fingerprints, scaffold analysis, stereoisomer enumeration, 3D geometry, diversity selection, and more — all callable from JavaScript/TypeScript with full TypeScript type definitions.

Domain-specific algorithms Rather than wrapping a generic graph library, chematic implements chemistry-specific algorithms directly: Kekulization, Hückel aromaticity, CIP stereochemistry, SSSR ring perception, Gasteiger charges, MaxMin/Butina diversity picking.

Reproducible and deterministic Fingerprints use FNV-1a hashing with a fixed invariant ordering. Given the same SMILES input, the same bits are always produced. No RNG, no platform-specific behavior.

Current Status

All phases complete. 877 tests, all passing. Zero C/C++ dependencies.

cargo test --workspace   # 877 tests, all passing

Quick Start

Using the umbrella crate

# Cargo.toml
[dependencies]
chematic = { git = "https://github.com/kent-tokyo/chematic", features = ["smiles", "fp"] }

use chematic::smiles::{parse, canonical_smiles};
use chematic::fp::ecfp4;

Using individual crates

# Cargo.toml
[dependencies]
chematic-smiles     = { git = "https://github.com/kent-tokyo/chematic" }
chematic-perception = { git = "https://github.com/kent-tokyo/chematic" }
chematic-fp         = { git = "https://github.com/kent-tokyo/chematic" }

use chematic_smiles::{parse, canonical_smiles};
use chematic_perception::{find_sssr, assign_aromaticity};
use chematic_fp::{ecfp4, tanimoto_ecfp4};

fn main() {
    let benzene = parse("c1ccccc1").unwrap();
    let toluene = parse("Cc1ccccc1").unwrap();

    // Ring and aromaticity perception
    let rings = find_sssr(&benzene);
    println!("rings: {}", rings.ring_count()); // 1

    // Fingerprint similarity
    let sim = tanimoto_ecfp4(&benzene, &toluene);
    println!("Tanimoto(benzene, toluene): {sim:.3}"); // ~0.5

    // Canonical SMILES
    println!("{}", canonical_smiles(&benzene)); // c1ccccc1
}

SMARTS substructure search

use chematic_smiles::parse;
use chematic_smarts::{parse_smarts, find_matches};

let mol = parse("CC(=O)Oc1ccccc1C(=O)O").unwrap(); // aspirin
let query = parse_smarts("[$(C(=O)O)]").unwrap();   // carboxylic / ester C
let matches = find_matches(&query, &mol);
println!("C(=O)O groups: {}", matches.len()); // 2

Molecular descriptors

use chematic_smiles::parse;
use chematic_chem::{molecular_weight, tpsa, logp_crippen, fsp3, qed, lipinski_passes};

let aspirin = parse("CC(=O)Oc1ccccc1C(=O)O").unwrap();
println!("MW:       {:.2}", molecular_weight(&aspirin)); // ~180.16
println!("TPSA:     {:.2}", tpsa(&aspirin));             // ~63.6
println!("LogP:     {:.2}", logp_crippen(&aspirin));     // ~1.2
println!("Fsp3:     {:.3}", fsp3(&aspirin));             // ~0.111
println!("QED:      {:.3}", qed(&aspirin));              // drug-likeness score
println!("Lipinski: {}", lipinski_passes(&aspirin));     // true

BRICS fragmentation

use chematic_smiles::parse;
use chematic_chem::brics_fragments;

let aspirin = parse("CC(=O)Oc1ccccc1C(=O)O").unwrap();
let frags = brics_fragments(&aspirin);
println!("fragments: {}", frags.len()); // ≥ 2

Fingerprints

use chematic_smiles::parse;
use chematic_fp::{ecfp4, atom_pair_fp, torsion_fp};

let aspirin = parse("CC(=O)Oc1ccccc1C(=O)O").unwrap();
let caffeine = parse("Cn1cnc2c1c(=O)n(c(=O)n2C)C").unwrap();

let sim_ecfp4    = ecfp4(&aspirin).tanimoto(&ecfp4(&caffeine));
let sim_atompair = atom_pair_fp(&aspirin).tanimoto(&atom_pair_fp(&caffeine));
let sim_torsion  = torsion_fp(&aspirin).tanimoto(&torsion_fp(&caffeine));

2D depiction

use chematic_smiles::parse;
use chematic_depict::depict_svg;

let caffeine = parse("Cn1cnc2c1c(=O)n(c(=O)n2C)C").unwrap();
let svg = depict_svg(&caffeine);
std::fs::write("caffeine.svg", svg).unwrap();

Highlighted depiction

use std::collections::HashSet;
use chematic_smiles::parse;
use chematic_depict::depict_svg_highlighted;

let mol = parse("c1ccncc1").unwrap(); // pyridine
let n_idx = mol.atoms().find(|(_, a)| a.element.atomic_number() == 7)
               .map(|(i, _)| i).unwrap();
let svg = depict_svg_highlighted(&mol, &HashSet::from([n_idx]), &HashSet::new());

JavaScript / TypeScript (WebAssembly)

~550 KB, zero C/C++ dependencies. Drop-in for browser or Node.js. Compare with RDKit.js at ~30 MB built via Emscripten.

npm install @kent-tokyo/chematic

import init, {
  parse_smiles, canonical_tautomer, murcko_scaffold,
  largest_fragment, neutralize_charges,
  tanimoto_ecfp4, tanimoto_ecfp6, tanimoto_maccs,
  brics_fragments_json, mcs_smiles_json,
  get_descriptors_json, sssr_rings_json,
  enumerate_stereo_isomers_json,
  sdf_to_records_json, sdf_from_records_json,
  maxmin_picks_ecfp4_json, butina_cluster_ecfp4_json,
  shape_descriptors_json, generate_3d_minimized_pdb,
} from '@kent-tokyo/chematic';

await init();

// ── Parsing & descriptors ─────────────────────────────────────────
const mol = parse_smiles('CC(=O)Oc1ccccc1C(=O)O'); // aspirin
console.log(mol.molecular_weight()); // ~180.16
console.log(mol.qed());              // drug-likeness [0,1]
console.log(mol.sa_score());         // synthetic accessibility [1,10]
console.log(mol.lipinski_passes());  // true

// All descriptors at once (JSON object)
const desc = JSON.parse(get_descriptors_json(mol));
console.log(desc.mw, desc.tpsa, desc.logP, desc.fsp3);

// ── Molecule processing ───────────────────────────────────────────
const salt = parse_smiles('CC(=O)[O-].[Na+]');
const clean = largest_fragment(salt);        // remove Na+
const neutral = neutralize_charges(clean);   // neutralize [O-]

const tautomer = canonical_tautomer(parse_smiles('Oc1cccc2ccccc12'));
const scaffold = murcko_scaffold(parse_smiles('c1ccc(CC(=O)O)cc1'));

// ── Fingerprints & similarity ─────────────────────────────────────
const caffeine = parse_smiles('Cn1cnc2c1c(=O)n(c(=O)n2C)C');
console.log(tanimoto_ecfp4(mol, caffeine));  // ECFP4 Tanimoto
console.log(tanimoto_ecfp6(mol, caffeine));  // ECFP6 Tanimoto
console.log(tanimoto_maccs(mol, caffeine));  // MACCS Tanimoto

// ── Scaffold / fragmentation / MCS ───────────────────────────────
const frags = JSON.parse(brics_fragments_json(mol));
const mcs = mcs_smiles_json('["CC(=O)O","CC(=O)N"]');

// ── Stereochemistry ───────────────────────────────────────────────
const isomers = JSON.parse(enumerate_stereo_isomers_json(parse_smiles('C(F)(Cl)Br')));
// ["[C@@H](F)(Cl)Br","[C@H](F)(Cl)Br"]

// ── 3D geometry ───────────────────────────────────────────────────
const pdb = generate_3d_minimized_pdb(mol);
const shape = JSON.parse(shape_descriptors_json(mol));
console.log(shape.pmi1, shape.npr1, shape.asphericity);

// ── Diversity selection ───────────────────────────────────────────
const library = '["CC","c1ccccc1","CCO","CCCC","c1ccncc1"]';
const picks = JSON.parse(maxmin_picks_ecfp4_json(library, 3));
const clusters = JSON.parse(butina_cluster_ecfp4_json(library, 0.4));

// ── SDF round-trip with properties ───────────────────────────────
const records = JSON.parse(sdf_to_records_json(sdfString));
// records[0].smiles, records[0].name, records[0].properties.MW

const sdf = sdf_from_records_json(
  '["CC(=O)O"]',
  '["aspirin"]',
  '["MW\t180.16\nSource\tChEMBL"]'
);

Comparison with Other Cheminformatics Libraries

Notes:

• chematic WASM binary size measured with wasm-opt optimization; RDKit.js is the official WASM build.
• "None" for C/C++ means verified: no *-sys crates, no cc build dependencies, no build.rs C compilation in the entire dependency tree.

Roadmap

Phase 1 — Foundation (complete)

Core types, OpenSMILES parse/write, Kekulization, canonical SMILES.

Phase 2 — Molecular Perception (complete)

SSSR, Huckel aromaticity, SDF/MOL V2000+V3000, 2D SVG depiction.

Phase 3 — Chemical Intelligence (complete)

Descriptors (MW, LogP, TPSA, Fsp3, Lipinski), QED, BRICS fragmentation, ECFP4/6 fingerprints, SMARTS+VF2 (recursive SMARTS, valence, hybridization), molecular standardization, Murcko scaffold, CIP R/S and E/Z.

Phase 4 — Similarity and Search (complete)

MACCS 166-bit keys, topological path FP, AtomPair FP, Topological Torsion FP, MCS, tautomer normalization.

Phase 5 — 3D Chemistry (complete)

Rule-based 3D coordinate generation, PDB/XYZ formats, UFF-like minimization.

Phase 6 — RDKit Parity (complete)

Reaction SMILES/SMIRKS ✓, umbrella crate with feature flags ✓, WASM npm package @kent-tokyo/chematic ✓, CPK coloring + highlighted depiction ✓, ChEMBL 37 full-set validation (2,897,819 molecules, 100.000%) ✓.

Phase 7 — Extended Descriptors and Diversity (v0.1.14–v0.1.15, complete)

EState indices (Hall & Kier 1991), path fingerprint (DFS path FP, 2048-bit), SDF/MOL WASM bindings, functional group identification (Ertl 2017 IFG), Gasteiger-Marsili PEOE partial charges, VSA descriptors (SlogP_VSA × 12, SMR_VSA × 10, PEOE_VSA × 14), SA score (complexity-based), MaxMin diversity picking, Butina clustering.

See tasks/todo.md for the detailed per-task breakdown.

Repository Structure

chematic/
├── Cargo.toml               workspace root
├── CHANGELOG.md             version history
├── crates/
│   ├── chematic-core/       Atom, Bond, Molecule, Element, kekulization
│   ├── chematic-smiles/     OpenSMILES parser, writer, canonical SMILES
│   ├── chematic-perception/ SSSR ring perception, Huckel aromaticity
│   ├── chematic-mol/        MOL/SDF V2000+V3000 parser and writer
│   ├── chematic-depict/     2D SVG depiction engine (CPK colors, highlighting)
│   ├── chematic-chem/       Descriptors, BRICS, QED, standardization, scaffold
│   ├── chematic-fp/         ECFP4/6, MACCS, path, AtomPair, Torsion FP
│   ├── chematic-smarts/     SMARTS parser + VF2 subgraph isomorphism, MCS
│   ├── chematic-3d/         3D coordinate generation, PDB/XYZ formats
│   ├── chematic-rxn/        Reaction SMILES parser and writer
│   └── chematic/            Umbrella crate with feature flags
└── tasks/
    ├── todo.md              full roadmap checklist (Japanese)
    └── lessons.md           development lessons learned

Development Commands

cargo build --workspace      # build all crates
cargo test --workspace       # run all tests (736)
cargo check --workspace      # type-check without building
cargo clippy --workspace     # lints

License

Licensed under either of Apache License 2.0 or MIT License, at your option.