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 + Section 4 (WASM, API improvements) + Sprint v0.1.33 (CXSMILES/CXSMARTS + audit) + Sprint v0.1.34 (InChI ring closure + stereo layers) + Sprint v0.1.35 (wasmBridge support) + Sprint v0.1.36 (Issue #1 Audit: BUG-2/3/4 fix) + Sprint v0.1.37 (mol_transforms API + random SMILES) + Sprint v0.1.69–v0.1.74 (RDKit Gap Analysis: 6 feature implementations) + v0.1.88–v0.1.89 (Gap closure 89%: A1–A6, B1–B2 complete) + v0.1.91–v0.1.94 (Gap closure 100%: A1–A5, B3 complete) — 1,521 tests, all passing. Zero C/C++ dependencies.

Latest release: v0.1.94 (2026-06-12) — RDKit gap analysis complete (A1–A5, B3 implemented)

cargo test --workspace   # 1,521 tests, all passing

Quick Start

Installation

# Rust
cargo add chematic --git https://github.com/kent-tokyo/chematic --features "smiles,perception,chem,3d,fp"

# JavaScript/TypeScript
npm install @kent-tokyo/chematic@0.1.94

5-Minute Examples

Parse SMILES & check drug-likeness

use chematic_smiles::parse;
use chematic_chem::*;

let mol = parse("CC(=O)Oc1ccccc1C(=O)O")?;  // aspirin

println!("MW: {:.2}", molecular_weight(&mol));
println!("LogP: {:.2}", logp(&mol));
println!("TPSA: {:.2}", tpsa(&mol));

if lipinski_descriptor_pass(&mol) {
    println!("✓ Passes Lipinski's Rule of Five");
}

Detect rings & aromaticity

use chematic_perception::{find_sssr, assign_aromaticity};

let rings = find_sssr(&mol);
let aromatic = assign_aromaticity(&mol);

println!("Rings: {}", rings.ring_count());
// NEW in v0.1.32: Check for antiaromatic systems
if aromatic.has_antiaromaticity(&mol) {
    println!("⚠ Contains antiaromatic rings (unstable)");
}

Generate 3D coordinates

use chematic_3d::generate_and_minimize_constrained;

let coords_3d = generate_and_minimize_constrained(&mol);
// NEW in v0.1.32: Constraint satisfaction for better geometry

Calculate fingerprint similarity

use chematic_fp::tanimoto_ecfp4;

let benzene = parse("c1ccccc1")?;
let toluene = parse("Cc1ccccc1")?;
let sim = tanimoto_ecfp4(&benzene, &toluene)?;
println!("Similarity: {:.2}", sim);  // ~0.5

Preserve chemical metadata with CXSMILES

use chematic_smiles::parse_cxsmiles;

let cx = parse_cxsmiles("CCO |$ethanol$,atomProp:1.role.acceptor,^2:0|")?;
// cx.atom_labels: ["ethanol"]
// cx.atom_props: [(atom: 1, key: "role", value: "acceptor")]
// cx.atom_radicals: [None, 2, None]

Audit standardization with reports

use chematic_chem::{StandardizationPipeline, StandardizeOptions};

let opts = StandardizeOptions {
    largest_fragment_only: true,
    neutralize_charges: true,
    ..Default::default()
};
let pipeline = StandardizationPipeline::new(opts);
let (standardized, report) = pipeline.run(&mol);

println!("Status: {:?}", report.status);  // Unchanged | Modified | CompletedWithWarnings
for step in &report.steps {
    println!("  {}: changed={}", step.step.as_str(), step.changed);
}

Use from WASM/JavaScript

import init, { molecule_report_json, parse_cxsmiles_json } from 'chematic-wasm';

await init();

// Parse CXSMILES with metadata
const cx = JSON.parse(parse_cxsmiles_json("CCO |$ethanol$|"));
console.log(cx.atomLabels);  // ["ethanol"]

// Standardize with audit report
const report = JSON.parse(
    molecule_report_json("CC(=O)Oc1ccccc1C(=O)O")
);
console.log(`LogP: ${report.descriptors.logp}`);
console.log(`Lipinski: ${report.filters.lipinski_passes ? '✓' : '✗'}`);

Full Example (Rust)

use chematic_smiles::parse;
use chematic_perception::{find_sssr, assign_aromaticity};
use chematic_chem::*;
use chematic_3d::generate_and_minimize_dreiding;
use chematic_fp::tanimoto_ecfp4;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Parse
    let benzene = parse("c1ccccc1")?;
    let toluene = parse("Cc1ccccc1")?;

    // Perception
    let rings = find_sssr(&benzene);
    let arom = assign_aromaticity(&benzene);
    println!("Benzene: {} rings, aromatic: {}", 
        rings.ring_count(), 
        arom.is_aromatic(&benzene));

    // Chemistry
    let mw = molecular_weight(&benzene);
    println!("Benzene MW: {:.2}", mw);

    // 3D
    let coords = generate_and_minimize_dreiding(&benzene);
    println!("3D coordinates generated");

    // Fingerprints
    let sim = tanimoto_ecfp4(&benzene, &toluene)?;
    println!("Benzene-Toluene similarity: {:.2}", sim);

    Ok(())
}

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.

Recent Development (v0.1.89–v0.1.94)

v0.1.91–v0.1.94: RDKit Gap Closure (A1–A5, B3)

• v0.1.91: True MHFP (structural fragment hashing), True ERG (Ertl 2017 functional groups)
• v0.1.92: Path FP with bond type interleaving, InChI stereo layer parsing (/t, /b)
• v0.1.93: Full multi-sphere CIP stereochemistry priority rules (moved to chematic-perception, avoids circular dependency)
• v0.1.94: SA Score corpus expanded (145 → 188 FDA molecules, 1034 → 1415 unique fragments)

v0.1.88–v0.1.90: InChI stereo layers, Brenk SMARTS, reionization, group normalization

v0.1.69–v0.1.87: Initial RDKit gap analysis — SSSR, Kekulization, CIP, 3D geometry, WASM API maturity

For detailed historical roadmap (Phases 1–16, v0.1.14–v0.1.33), see tasks/todo.md.

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.