化学信息学库

Installation

```bash

Quick Start

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(())
}

---

Live Demo

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

---

Comparison with Other Cheminformatics Libraries

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 C-compiler dependency for the vendored IUPAC InChI C library (v1.07.5). All other crates remain FFI-free.

---

Aromaticity model (Hückel vs RDKit)

chematic uses the Hückel 4n+2 rule applied independently to each SSSR ring, while RDKit uses a more sophisticated fused-ring electron-delocalization model. Differences are most visible in N-heterocycles (pyridone, quinolone, indolizine).

Cascade effects on a 5,000-molecule corpus (issue #12), current status:

All three metrics are now at or near RDKit parity on the 5 000-molecule benchmark.

Aromatic ring count (~100%) improved from 95.6% via a fix to the XOR size guard in augmented_ring_set: changing min → max ensures that a recovered ring equal in size to the smaller SSSR parent (but smaller than the large macro-ring) is correctly added to the ring set. All 222 previously failing bench5k cases now match RDKit. The envelope-ring stripper in count_aromatic_rings was also extended to handle 4-ring GF(2) sums (coronene-class PAHs).

---