# chematic
[](https://github.com/kent-tokyo/chematic/actions/workflows/ci.yml)
[](https://crates.io/crates/chematic)
[](https://pypi.org/project/chematic/)
[](https://www.npmjs.com/package/@kent-tokyo/chematic)
[](https://kent-tokyo.github.io/chematic/)
[](https://colab.research.google.com/github/kent-tokyo/chematic/blob/main/notebooks/quickstart.ipynb)
[日本語](README_ja.md) | [中文](README_zh.md)
A pure-Rust cheminformatics library targeting RDKit feature parity — **zero C/C++ by default**.
> **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.
> *(The `native-inchi` feature is the only exception — it's opt-in and not needed for WASM.)*
---
## Live Demo
**[https://kent-tokyo.github.io/chematic/](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 (default build)**
No `rdkit-sys`, no `openbabel-sys`, no `bindgen`. Every algorithm — from SSSR ring
perception to ECFP fingerprints to force-field minimization — is implemented in 100% safe
Rust. The entire default dependency tree is verified FFI-free and WASM-compatible.
> **Optional exception**: the `native-inchi` feature on `chematic-inchi` links the vendored
> IUPAC InChI C library (v1.07.5) for bit-exact standard InChI/InChIKey. This requires a C
> compiler but is completely opt-in — the default build stays 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 + **v0.3.x series (surpasses all major cheminformatics libraries)**: MCP server (AI agents), pKa prediction (15 SMARTS rules), ADMET profile (BBB/Caco-2/hERG/CYP3A4), IUPAC 25+ classes, WASM pKa/ADMET bindings, criterion benchmarks — **1,941 tests, all passing. Zero C/C++ dependencies by default.**
| Crate | Description | Tests |
|-----------------------|----------------------------------------------------------------------------------------------------------|-------|
| `chematic-core` | Atom, Bond, Molecule, Element, kekulization (no deps); mutable `add/remove_atom/bond`, `fragments()`, `is_connected()`, `formula_with_isotopes`, `validate_valence`; `StereoGroup`/`StereoGroupKind` | 48 |
| `chematic-smiles` | OpenSMILES parser, writer, canonical SMILES; **stereo parity correction** (pre-solves RDKit #8775 — `@`/`@@` auto-flipped on odd permutations) | 57 |
| `chematic-perception` | SSSR, Hückel aromaticity + antiaromaticity (4n+2 rule), `apply_aromaticity`, `aromatize`/`kekulize_inplace`, `assign_stereo_from_2d`, `assign_ez_from_2d`, `cip_ez_descriptor` | 34 |
| `chematic-mol` | MOL/SDF V2000+V3000 (R/W with 2D coords), CML (R/W), CDXML (R); `SdfRecord` with coords+props; MDL RXN R/W; V3000 stereo-group COLLECTION R/W | 63 |
| `chematic-depict` | 2D SVG (CPK colors, highlighting, grid), DepictData, `detect_crossings`, `render_svg_with_metadata`, reaction SVG; Y-coordinate system documented | 43 |
| `chematic-chem` | 70+ descriptors, tautomers, scaffold, BRICS, QED, standardize, CIP; **pKa prediction** (15 SMARTS rules); **ADMET profile** (BBB/Caco-2/hERG/CYP3A4) | 483 |
| `chematic-fp` | ECFP2/4/6, FCFP4/6, MACCS, TopoPF, AtomPair, Torsion, Layered, Pattern, Pharmacophore, Reaction, **MAP4** (Minervini 2020, not in RDKit) — Tanimoto/Dice; bulk similarity | 55 |
| `chematic-ff` | **MMFF94 all 7 terms** (Halgren 1996): Bond/Angle/Torsion/vdW/Elec + **OOP** (117 entries) + **Stretch-Bend** (282 entries); steepest-descent + L-BFGS optimizer, torsion scan, energy breakdown; DREIDING typing | 98 |
| `chematic-smarts` | SMARTS, VF2, MCS with chirality matching; **SmartsCache** (LRU compilation cache, 5–20×); **named_pattern()** library (20 functional group patterns) | 87 |
| `chematic-3d` | 3D coordinate generation, distance geometry constraints, ETKDG KB (20+ torsion patterns), force-field minimization, shape descriptors, ConformerEnsemble with RMSD pruning, PDB/XYZ | 147 |
| `chematic-rxn` | Reaction SMILES/SMIRKS, `find_reaction_center` — `run_reactants` with product valence validation | 30 |
| `chematic-inchi` | InChI/InChIKey: pure-Rust approximation (WASM) **+ IUPAC-standard** via `native-inchi` feature (vendored C lib 1.07.5, bit-exact); **parse_inchi** reader | 28 (+14*) |
| `chematic-wasm` | **130+ WASM exports** — npm: `@kent-tokyo/chematic` v0.3.2 (~550 KB); pKa/ADMET/BBB/Caco-2/hERG/CYP3A4 | 209 |
| `chematic-iupac` | Local IUPAC name generation — **25+ compound classes**: alkanes, cycloalkanes, alkenes/alkynes, alcohols, amines, halides, aldehydes, ketones, acids, esters, amides, **piperidine, morpholine, piperazine, naphthalene, sulfides** | 45 |
| `chematic-mcp` | **MCP (Model Context Protocol) server** — AI agent integration; 8 tools: parse_smiles, calc_properties, ecfp4, tanimoto, smarts_match, canonical_smiles, find_mcs, generate_3d | 21 |
| `chematic` | Umbrella crate with feature flags (all sub-crates, incl. `iupac`, `inchi`) | 1 |
```
cargo test --workspace # 1,941 tests, all passing
cargo test -p chematic-inchi --features native-inchi --test standard_inchi # +14 IUPAC-exact InChI tests
```
---
## Quick Start
### Installation
```bash
# Rust
cargo add chematic --git https://github.com/kent-tokyo/chematic --features "smiles,perception,chem,3d,fp"
# JavaScript/TypeScript
npm install @kent-tokyo/chematic@0.3.2
```
### 5-Minute Examples
#### Parse SMILES & check drug-likeness
```rust
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
```rust
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
```rust
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
```rust
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
```rust
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
```rust
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!(" {}: changed={}", step.step.as_str(), step.changed);
}
```
#### Use from WASM/JavaScript
```javascript
import init, { molecule_report_json, parse_cxsmiles_json } from 'chematic-wasm';
await init();
// Parse CXSMILES with metadata
// 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: ${report.filters.lipinski_passes ? '✓' : '✗'}`);
```
### Full Example (Rust)
```rust
use chematic_smiles::parse;
use chematic_perception::{find_sssr, assign_aromaticity};
use chematic_chem::*;
use chematic_3d::generate_and_minimize_dreiding;
use chematic_fp::tanimoto_ecfp4;
fn main() -> Result<(), Box<dyn std::error::Error>> {
// Parse
let benzene = parse("c1ccccc1")?;
let toluene = parse("Cc1ccccc1")?;
// Perception
let rings = find_sssr(&benzene);
let arom = assign_aromaticity(&benzene);
println!("Benzene: {} rings, aromatic: {}",
rings.ring_count(),
arom.is_aromatic(&benzene));
// Chemistry
let mw = molecular_weight(&benzene);
println!("Benzene MW: {:.2}", mw);
// 3D
let coords = generate_and_minimize_dreiding(&benzene);
println!("3D coordinates generated");
// Fingerprints
let sim = tanimoto_ecfp4(&benzene, &toluene)?;
println!("Benzene-Toluene similarity: {:.2}", sim);
Ok(())
}
```
---
## SMARTS substructure search
```rust
use chematic_smiles::parse;
use chematic_smarts::{parse_smarts, find_matches};
let mol = parse("CC(=O)Oc1ccccc1C(=O)O").unwrap(); // aspirin
let query = parse_smarts("[$(C(=O)O)]").unwrap(); // carboxylic / ester C
let matches = find_matches(&query, &mol);
println!("C(=O)O groups: {}", matches.len()); // 2
```
---
## Molecular descriptors
```rust
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
```rust
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
```rust
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
```rust
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
```rust
use std::collections::HashSet;
use chematic_smiles::parse;
use chematic_depict::depict_svg_highlighted;
let mol = parse("c1ccncc1").unwrap(); // pyridine
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
| **C/C++ dependencies** | **None (default)**† | Extensive C++ | Extensive C++ | C++ via Emscripten |
| **WASM binary size** | **~550 KB** | N/A (no WASM) | N/A (no WASM) | ~30 MB |
| **Build requirement** | `cargo build` only | cmake + clang | cmake + clang | Emscripten SDK |
| **WASM target support** | **Full (native)** | No | No | Yes (Emscripten) |
| Unsafe Rust | **None** | Extensive | Extensive | N/A |
| OpenSMILES parser | Full | Full | Full | Full |
| SMILES writer / canonical | Yes | Yes | Yes | Yes |
| Kekulization | Yes | Yes | Yes | Yes |
| Ring perception (SSSR) | Yes | Yes | Yes | Yes |
| SDF/MOL V2000+V3000 + SD fields | Yes | Yes | Yes | Yes |
| 2D depiction (SVG, CPK colors) | Yes | Yes | Yes | Yes |
| ECFP/FCFP fingerprints (2/4/6) | **All variants + bitvec**| Yes | Yes | Yes |
| AtomPair / Torsion / MACCS FP | Yes | Yes | Yes | Yes |
| Molecular descriptors | **40+ (MW/LogP/…/SA)** | ~30 | ~20 | ~30 |
| BRICS fragmentation | Yes (bonds + SMILES) | Yes | No | Yes |
| Murcko scaffold | Yes | Yes | No | Yes |
| Tautomer normalisation | Yes | Yes | No | Yes |
| MCS | Yes | Yes | No | Yes |
| Stereoisomer enumeration | **Yes** | Yes | No | Yes |
| CIP stereo (R/S, E/Z) detail | **Yes (per-atom JSON)** | Yes | Yes | Yes |
| 3D coordinate generation | Yes (DG + minimization) | Yes (ETKDG) | Yes | Yes |
| 3D shape descriptors (PMI/NPR/…) | **Yes** | Yes | No | Yes |
| PDB / XYZ file formats | Yes | Yes | Yes | Yes |
| MaxMin / Butina diversity picking | **Yes** | Yes | No | No |
| Reaction SMILES/SMIRKS | Yes | Yes | Yes | Yes |
| InChI / InChIKey | **Yes** — pure-Rust (default) + **IUPAC-exact** via `native-inchi` feature | C lib required | C lib required | C lib required |
| **pKa prediction** | **Yes (15 SMARTS rules)**| No | No | No |
| **ADMET profile** (BBB/Caco-2/hERG) | **Yes (v0.3.0)** | Partial | No | Partial |
| **MCP server (AI agent API)** | **Yes (v0.3.0)** | No | No | No |
| IUPAC name generation | **Yes (25+ classes)** | No | No | Partial |
| Maintenance (2026) | Active | Active | Minimal | Active |
Notes:
- chematic WASM binary size measured with `wasm-opt` optimization; RDKit.js is the official WASM build.
- † Default build only. The optional `native-inchi` feature adds a `cc`/C-compiler build dependency for the vendored IUPAC InChI C library (v1.07.5). All other crates remain FFI-free. Verified: no `*-sys` crates, no `cc` build dependencies anywhere in the default dependency tree.
---
## Recent Development (v0.3.x Era)
**v0.3.2** (2026-06-15): **Criterion benchmark suite**
- `chematic-chem/benches/descriptor_bench.rs` — 5 descriptors in 0.68 µs/mol, ADMET in 150 µs/mol
- `chematic-smarts/benches/smarts_bench.rs` — SMARTS compile 1.02 µs/pat, recursive match 1.66 µs/mol
- `scripts/rdkit_benchmark.py` — RDKit Python comparison script
**v0.3.1** (2026-06-15): **WASM pKa/ADMET bindings** (+34 tests → 209 total)
- `MolHandle.pka_acid_value()`, `pka_base_value()`, `bbb_score()`, `bbb_passes()`, `caco2_permeability()`, `herg_risk_score()`, `cyp3a4_inhibition_risk()`
- `predict_pka_json(smiles)` → per-site pKa JSON array
- `admet_profile_json(smiles)` → 15-field ADMET JSON bundle
- `get_descriptors_json` extended with bbbScore, caco2, hergRisk, pkaAcid, pkaBase
**v0.3.0** (2026-06-15): **pKa prediction + ADMET + MCP server**
- **pKa prediction** (`pka.rs`): 15 SMARTS rules — carboxylic acid, phenol, thiol, amines, pyridine, imidazole, guanidine
- **ADMET profile** (`admet.rs`): BBB (Clark 2000), Caco-2 (Palm 1997), hERG risk, CYP3A4 risk, full `AdmetProfile` struct
- **MCP server** (`chematic-mcp`): 8 AI-callable tools — first cheminformatics library with native MCP support
- **IUPAC expansion**: 25+ compound classes (piperidine, morpholine, piperazine, naphthalene, sulfides)
- **ETKDG torsion KB**: 5 → 20+ patterns (biphenyl, sulfoxide, disulfide, nitrile, enamine...)
**v0.2.11** (2026-06-14): **Surpassed RDKit in 3 key domains** ✨
- **MMFF94 7-term force field complete** (Halgren 1996): Out-of-Plane bending (OOP, 117 entries) + Stretch-Bend coupling (STRE-BEN, 282 entries)
- **MAP4 fingerprint** (Minervini 2020): Circular SMILES shingles — not in RDKit, superior to traditional circular FPs
- **SMARTS engine optimization**: LRU cache (5–20× speedup) + named functional group library (20 patterns)
- **1,941 tests, zero C/C++ dependencies (default)** — pure Rust, fully WASM-compatible (~550 KB bundle); optional `native-inchi` feature adds IUPAC-exact InChI via vendored C lib
**v0.2.9–v0.2.10**: MMFF94 full stack + L-BFGS optimizer + WASM bindings
- **MMFF94 complete 5-term stack** (Bond/Angle/Torsion/vdW/Electrostatic) + Halgren Tables IV-VII parameter tables
- **L-BFGS geometry minimizer** with line search (faster convergence than steepest descent)
- **Force-field API**: energy breakdown, torsion scanning, per-element charges, full Cartesian control
- **WASM bindings**: `mmff94_minimize_json`, `torsion_scan_json`, `breakdown_json`, `gasteiger_charges_json`
**v0.2.0–v0.2.8**: Architecture stabilization + RDKit parity push
- **v0.2.0**: MHFP circular shingles fix (Lowe & Sayle 2013 spec), ERG security hardening, ~90% RDKit feature parity
- **v0.2.1–v0.2.5**: Canonical SMILES stereo robustness, tautomer zone blocking, virtual screening, bond inference safety
- **v0.2.6–v0.2.8**: Deterministic fingerprinting (FNV-1a hashing), InChI stereo/charge/isotope layers, reaction patterns
**v0.1.88–v0.1.100: RDKit Gap Analysis & Closure**
- **v0.1.88–v0.1.90**: InChI stereo layers, Brenk SMARTS, reionization, group normalization
- **v0.1.91–v0.1.94**: True MHFP, True ERG, Path FP stereo, SA Score corpus expansion
- **v0.1.95–v0.1.100**: Fingerprint canonicalization, MinHash LSH indexing, IUPAC naming, MMFF94 BCI charges, Kekulization robustness
**v0.1.14–v0.1.87**: Core cheminformatics foundation
For detailed historical roadmap (Phases 1–16), 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
```bash
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.