化学信息学库
经 AI Skill Hub 精选评估，化学信息学库 获评「强烈推荐」。这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.0 分，适合有一定技术背景的用户使用。
📚 深度解析
通过安装 化学信息学库，你的 AI 助手将获得额外的工具调用能力，可以用自然语言直接操控该工具的功能，无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置，永久增强"——配置完成后，每次与 AI 对话时都可以无缝调用这些工具。
在技术实现上，MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信，工具的功能以"工具列表"的形式暴露给 AI 模型，AI 可以按需调用。化学信息学库 提供了结构化的工具调用接口，使 AI 模型能够精确地理解和使用每个功能点，显著降低 AI 在工具使用上的错误率。
与传统的 API 集成相比，MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON，即可让 AI 获得全新能力。AI Skill Hub 将 化学信息学库 评为 AI 评分 8.0 分，属于同类工具中的优质选择。
📋 工具概览
化学信息学库 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
📖 中文文档
化学信息学库 是一款遵循 MCP（Model Context Protocol）标准协议的 AI 工具扩展。通过 MCP 协议，它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务，实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用，都可以通过自然语言在 AI 对话中直接触发，极大提升生产效率。
- 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
- 提供结构化工具调用接口，显著降低 AI 集成复杂度
- 支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
- 可与其他 MCP 工具组合叠加，构建完整 AI 工作站
- 轻量无侵入设计，不影响现有系统架构
- 在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
- 通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
- 将多个 MCP 工具组合使用，构建个人专属 AI 工作站
# 方式一：通过 Claude Code CLI 一键安装
claude skill install https://github.com/kent-tokyo/chematic
# 方式二：手动配置 claude_desktop_config.json
{
"mcpServers": {
"------": {
"command": "npx",
"args": ["-y", "chematic"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
- 确认已安装 Node.js（v18 或以上版本）
- 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
- 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
- 保存配置文件并重启 Claude 客户端
- 重启后，在对话中即可使用本工具
# 安装后在 Claude 对话中直接使用 # 示例： 用户: 请帮我用 化学信息学库 执行以下任务... Claude: [自动调用 化学信息学库 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入："列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"______": {
"command": "npx",
"args": ["-y", "chematic"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
chematic
A cheminformatics library for Python, Rust, and the browser.
Cheminformatics that's fast by default, safe by design. Pure Rust by default · optional native InChI C FFI · Python · WebAssembly · Website · Live Demo
| chematic | RDKit (Python) | RDKit.js (WASM) | |
|---|---|---|---|
| **Get started** | pip install chematic | pip install rdkit (official prebuilt wheels) or conda | npm install @rdkit/rdkit, no Python bindings |
| **Browser bundle** | **3.73 MB raw / 1.36 MB gzip** | not applicable (Python/C++ library) | 6.91 MB raw* |
| **ECFP4 batch** | **54.7 µs/mol** | 94.3 µs/mol | — |
| **Canonical SMILES** | **24.95 / 18.27 µs/mol** | 25.58 / 26.82 µs/mol | — |
| **SDF graph read / serialization-only write** | **9.48 / 7.62 µs/mol** | 99.96 / 79.54 µs/mol | — |
| **Memory safety** | compiler-enforced (Rust) | C++ | C++ |
| **Build from source** | cargo build only | cmake + clang + Boost | Emscripten SDK |
\* RDKit.js gzip-over-the-wire size was not independently measured; raw figures are compared on a like-for-like basis. RDKit.js is currently in a maintainer transition (see its repo for current status).
The canonical and SDF rows are scoped 2026-09-04 macOS arm64 medians, not cross-platform claims; see the exact corpora and operation boundaries in the benchmark details. The current chematic WASM artifact was measured 2026-09-09 from the v1.0.10 release candidate with wasm-pack 0.13.1 + wasm-opt 130: 3.73 MB raw (1.36 MB gzip). The pinned historical comparators are RDKit.js 6.91 MB (@rdkit/rdkit@2025.3.4-1.0.0's RDKit_minimal.wasm, via unpkg.com) · Indigo (Ketcher build) 11.24 MB (indigo-ketcher@1.45.1's main .wasm, via jsDelivr) — chematic's raw WASM binary is currently about 1.9× smaller than RDKit.js's and about 3.0× smaller than Indigo's Ketcher-oriented build, on a raw-to-raw basis. See the v1.0.10 artifact record, the latest measured artifact record for this release line.
The separate 2026-08-23 benchmark rebuild reports 2.98 MB raw / 1.11 MB gzip; both figures are retained with their measurement dates because build outputs can vary slightly by toolchain and build environment.
Feature maturity at a glance:
| Feature | Status |
|---|---|
| SMILES / SMARTS / fingerprints / descriptors | Stable |
| 3D conformer generation (DG + MMFF94) | Experimental |
| pKa / ADMET | Rule-based screening (not for clinical use) |
| IUPAC name generation | Partial (25+ classes) |
| Pure-Rust InChI | Approximate (enable native-inchi feature for exact) |
Reliability by Feature
Not all features have the same validation depth. This table tells you what to trust.
| Feature | Status | Validation |
|---|---|---|
| SMILES parse / write | **Stable** | 4,999-mol ChEMBL comparison; OpenSMILES corpus (parse *correctness*, not canonical-form self-stability — see Canonical SMILES row) |
| Canonical SMILES (structural correctness) | **Stable** | canonical_smiles(parse(x)) always represents the same molecule as x: **100%** across 5,000-mol ChEMBL worst-of-10 *and* a 33-compound acyclic-polyene corpus (retinoids/carotenoids/prostaglandins/leukotrienes/macrolides), each with a verified positive control — was 4.28% corrupting to a different stereoisomer. **Not yet a dedup/cache key** — see Known Limitations below |
| Molecular weight | **Stable** | 99.82% within ±0.01 Da on 4,999 mol |
| HBA / HBD | **Stable** | 100% RDKit agreement on 4,999 mol |
| TPSA | **Stable** | **100%** on 4,999-mol ChEMBL subset (±0.1 Ų) — see [docs/validation.md](https://kent-tokyo.github.io/chematic/validation/) |
| LogP (Crippen) | **Stable** | **100%** on 4,999-mol corpus (max Δ = 1.1×10⁻¹³, within float64 rounding error) |
| ECFP4 / MACCS fingerprints | **Stable** | RDKit comparison + benchmark |
| Tanimoto similarity | **Stable** | RDKit comparison |
| SDF / MOL V2000/V3000 I/O | **Stable** | round-trip tests |
| Substructure search (SMARTS / VF2) | **Stable** | internal test suite |
| PAINS / Brenk filters | **Stable** | rule matching stable; ring-size SMARTS ([r5]/[r6]) now 0% instability across 5,000-mol worst-of-10 (was ~29–55% before the SSSR fix) |
| Ring perception (SSSR) | **Stable** | Horton algorithm, minimal + deterministic; 0% self-instability across 5,000-mol worst-of-10 (was 50.6%) — see Known Limitations below |
| Murcko scaffold | **Stable** (normalized) | normalized string output **100%** stable across 5,000-mol worst-of-10 (was 0.8% unstable, same root cause as the canonical-SMILES corruption above, now fixed); raw .smiles inherits the still-partially-open direction-normalization gap — normalize before comparing (see Known Limitations) |
| 2D SVG depiction | **Stable** | visual spot-checks; not publication-quality |
| 3D conformer (DG + MMFF94) | **Experimental** | reasonable geometry; not equivalent to RDKit ETKDGv3 quality |
| pKa prediction | **Rule-based screening** | 23 SMARTS rules; early triage only, not clinical |
| ADMET (BBB / Caco-2 / hERG / CYP3A4) | **Rule-based screening** | empirical models; directional, not validated on clinical endpoints |
| IUPAC name generation | **Partial** | common compound classes; complex structures may fail |
| Pure-Rust InChI | **Approximate** | enable native-inchi feature for bit-exact IUPAC InChI |
Full benchmark methodology → validation/ · History → benchmarks/
---
Installation
```bash
Python — no C/C++ compiler required
pip install chematic
Common Use Cases
| Scenario | How chematic helps |
|---|---|
| **HTML report** | chematic.report(mols, output="report.html") — self-contained compound grid, no server needed |
| **Drug screening** | 190+ descriptors, ADMET, PAINS/Brenk, QED — batch over thousands of compounds |
| **Molecule search** | ECFP4/MACCS fingerprints, opt-in RDKit-compatible chiral Morgan fingerprints, Tanimoto, LSH approximate nearest-neighbour |
| **AI agent / MCP** | Built-in MCP server — Claude Desktop can call chemistry tools directly |
| **Browser app** | 1.36 MB gzip WASM bundle, zero backend required, React/Vue/Svelte ready |
| **Jupyter notebook** | mol renders SVG inline; descriptors_df() returns a pandas DataFrame |
| **Batch analysis** | Rayon-parallel descriptor/fingerprint/3D pipelines; SDF/CSV in, CSV out |
| **Rust server** | Pure-Rust crates with no C/C++ toolchain; Axum/Actix compatible |
Full worked examples → Use cases
---
Quick Start
Choose your interface
- Rust
- Python
- WebAssembly / Node.js
- Materials and simulation formats — mmCIF, PQR, QCSchema, ORCA, Gaussian Cube, OpenDX, LAMMPS
- Migrating from RDKit — feature-by-feature Supported/Partial/Not-supported breakdown
- Compatibility scope — v1.0 boundary for RWMol, CDXML, polymer expansion, and RDKit/Morgan compatibility
---
Crate Reference
| Area | Crates |
|---|---|
| Molecular graph and identity | chematic-core, chematic-smiles, chematic-perception, chematic-cip |
| Queries, descriptors, and fingerprints | chematic-smarts, chematic-chem, chematic-fp |
| File and reaction models | chematic-mol, chematic-rxn, chematic-inchi, chematic-iupac |
| 2D, 3D, and materials | chematic-depict, chematic-3d, chematic-ff, chematic-crystal, chematic-ewald |
| User interfaces | chematic, chematic-py, chematic-wasm, chematic-cli, chematic-mcp |
See format capabilities, language bindings, and the individual crate READMEs for supported operations and limitations.
---
Side-by-side comparison
cmp = chematic.compare(aspirin, ibuprofen, names=("Aspirin", "Ibuprofen")) cmp.save("compare.html") ```
---
Descriptor accuracy (benchmark 2026-08-23, v0.18.0 vs RDKit 2026.03.4):
Comparison with Other Cheminformatics Libraries
| Feature | **chematic** | RDKit (rdkit-sys) | OpenBabel FFI | RDKit.js (WASM) |
|---|---|---|---|---|
| **C/C++ dependencies** | **None (default)**† | Extensive C++ | Extensive C++ | C++ via Emscripten |
| **WASM binary size** | **3.73 MB raw** (1.36 MB gzip) | N/A (no WASM) | N/A (no WASM) | 6.91 MB raw |
| **Build requirement** | cargo build only | cmake + clang | cmake + clang | Emscripten SDK |
| **WASM target support** | **Full (native)** | No | No | Yes (Emscripten) |
| **Python bindings** | **Yes** (pip install chematic, PyO3) | Yes (rdkit-sys) | Yes | No |
| **Unsafe Rust** | **None in own crates**‡ | Extensive | Extensive | N/A |
See the format capability matrix and the RDKit migration guide for detailed support differences. The table above is intentionally limited to deployment-level differences; detailed feature claims belong in those maintained pages.
---
高质量的Rust化学信息学库，实现RDKit功能
- 需要 chematic 解决具体问题的开发者与运营人员
- 先在测试环境跑通最小用例，再接入生产数据
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
- 提供结构化工具调用接口，显著降低 AI 集成复杂度
- 支持 Claude Desktop 和 Claude Code 无缝接入，开箱即用
- 可与其他 MCP 工具组合叠加，构建完整 AI 工作站
- 轻量无侵入设计，不影响现有系统架构
- 需要 chematic 解决具体问题的开发者与运营人员
- 先在测试环境跑通最小用例，再接入生产数据
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
👥 适合人群
🎯 使用场景
- 在 Claude Desktop 对话中直接调用本地工具，实现 AI 与系统的深度联动
- 通过自然语言驱动复杂的多步骤自动化任务，代替繁琐手动操作
- 将多个 MCP 工具组合使用，构建个人专属 AI 工作站
⚖️ 优点与不足
- +标准化 MCP 协议，生态互联性强
- +与 Claude 官方生态无缝对接
- +即插即用，配置简单快捷
- −未明确开源协议，商用场景需谨慎评估
- −依赖 Claude 客户端，非 Claude 用户无法使用
- −MCP 协议仍在持续演进，接口可能变更
- −需要一定的配置步骤
该工具未明确声明开源协议，商业使用前请联系原作者确认授权范围，避免侵权风险。
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
🔗 相关工具推荐
❓ 常见问题 FAQ
AI Skill Hub 点评：化学信息学库 的核心功能完整，质量优秀。对于Claude Desktop / Claude Code 用户来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | chematic |
| 原始描述 | 开源MCP工具：A pure-Rust cheminformatics library targeting RDKit feature parity — zero C/C++ 。⭐15 · Rust |
| Topics | cheminformaticschemistrydrug-discovery |
| GitHub | https://github.com/kent-tokyo/chematic |
| 语言 | Rust |
收录时间：2026-06-20 · 更新时间：2026-06-22 · License：未公布 · AI Skill Hub 不对第三方内容的准确性作法律背书。