chematic vs RDKit — Detailed Comparison

This page gives a direct comparison between chematic and RDKit for teams evaluating which library to use.

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TL;DR

	chematic	RDKit
Install	pip install chematic	conda / cmake required
Browser / WASM	Yes — 504 KB	No
C++ dependency	None (default)	Required
Batch fingerprint speed	3.6 µs/mol	20–50 µs/mol
AI agent integration	MCP server built-in	None
Ecosystem maturity	Growing (2024–)	Established (2006–)

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1. Infrastructure

Installation

	chematic	RDKit
Python	pip install chematic	conda install -c conda-forge rdkit
C++ compiler	Not required	Required (Boost, CMake)
Docker image delta	~4 MB	~200 MB+
GitHub Actions	pip install chematic	Separate conda setup step
Cloudflare Workers	Yes	No
AWS Lambda	Yes	Difficult (binary size)
Embedded / no-std	Partial	No

WASM deployment

Library	Bundle size	Build toolchain
chematic	504 KB gzip	wasm-pack build only
RDKit.js	~30 MB	Emscripten SDK + cmake
Indigo WASM	~40 MB	Emscripten SDK + cmake

chematic compiles to wasm32-unknown-unknown natively — no Emscripten, no cmake, no clang.

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2. Performance

All measurements: Python 3.12, Apple M-series, chematic v0.4.22, RDKit 2026.03.3.

Import time (cold process)

	chematic	RDKit
import only	~35 ms	~400 ms
import + first parse	~38 ms	~430 ms
Speedup	~11×	baseline

Measured by spawning a fresh subprocess per sample (module cache excluded).

Reproduce:

python scripts/bench_startup.py --runs 5 --rdkit

SMILES parse throughput

N molecules	chematic	RDKit	Speedup
1,000	~1 ms	~10 ms	~10×
5,000	~5 ms	~50 ms	~10×
10,000	~10 ms	~100 ms	~10×

Per-molecule: ~1 µs/mol (chematic) vs ~10 µs/mol (RDKit).

Reproduce:

python scripts/bench_smiles_parse.py --n 5000 --rdkit

ECFP4 fingerprint generation (batch)

N molecules	chematic (bulk.ecfp4)	RDKit (Python loop)	Speedup
100	0.36 ms	2 ms	5×
1,000	3.6 ms	20 ms	5×
10,000	36 ms	~500 ms	~14×

chematic uses Rayon for parallel batch processing. Speedup grows with batch size.

Reproduce:

python scripts/bench_smiles_parse.py --n 10000 --rdkit

Where RDKit is faster or better

Task	RDKit advantage
Publication-quality 3D structures	ETKDGv3 with ML torsion corrections (chematic: good for screening)
Exotic molecule handling	20 years of edge-case fixes
Large SDF file streaming	Optimized C++ reader

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3. Feature coverage

chematic has, RDKit does not

Feature	chematic	RDKit
Native WASM (no Emscripten)	Yes	No
MCP server (AI agent API)	15 tools	None
pKa prediction (built-in)	15 SMARTS rules	External tool required
ADMET profile (built-in)	BBB / Caco-2 / hERG / CYP3A4	External tool required
MAP4 fingerprint	Yes (Minervini 2020)	No (external package)
UFF force field for metals	Yes (Zn, Fe, Cu, …)	No
IUPAC name generation (offline)	25+ compound classes	No
Retrosynthesis (template-based)	60 retro-SMIRKS built-in	External tool required
pip install anywhere	Yes	No (conda/cmake)

RDKit has, chematic does not (or is weaker)

Feature	RDKit advantage
Publication-quality 3D conformers	ETKDGv3 with ML torsion corrections; chematic uses chair/envelope + MMFF94 (good for virtual screening)
File format support	100+ formats (chematic: ~20)
Validated production docking	Years of benchmarking
Community plugins	Large ecosystem
Exact InChI (default)	C lib bundled by default

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4. API comparison (Python)

Most common operations map directly:

Operation	chematic	RDKit
Parse SMILES	chematic.from_smiles(s)	Chem.MolFromSmiles(s)
Molecular weight	mol.mw	Descriptors.MolWt(mol)
LogP	mol.logp	Descriptors.MolLogP(mol)
TPSA	mol.tpsa	Descriptors.TPSA(mol)
ECFP4	chematic.bulk.ecfp4(smiles)	AllChem.GetMorganFingerprintAsBitVect(mol, 2)
Substructure	mol.has_substructure("[OH]")	mol.HasSubstructMatch(Chem.MolFromSmarts("[OH]"))
Batch descriptors	chematic.descriptors_df(smiles)	PandasTools + manual loop
Drug-likeness	mol.lipinski_passes	Descriptors.rdMolDescriptors.CalcNumHBD(mol) <= 5 and …
Canonical SMILES	mol.smiles	Chem.MolToSmiles(mol)

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5. When to choose

Choose chematic if:

• You want chemistry in the browser (WASM, 504 KB, no server)
• You need a pure Rust stack with no C++ toolchain
• You deploy to Lambda, Cloudflare Workers, or other constrained environments
• You build AI agents and want native MCP tool integration
• You need fast batch processing (ECFP4: 5–14× faster)
• You want pip install to just work — anywhere

Choose RDKit if:

• You need maximum ecosystem compatibility and 20+ years of production validation
• You need publication-quality 3D structures with ML-assisted torsion corrections (ETKDGv3)
• You rely on community plugins written against the RDKit Python API
• You need bit-exact standard InChI without an extra feature flag
• You work with exotic file formats or unusual molecule types

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6. Migration quick-reference

If you have existing RDKit code, these are the most common substitutions:

# RDKit
from rdkit import Chem
from rdkit.Chem import Descriptors, rdMolDescriptors, AllChem

mol = Chem.MolFromSmiles("CC(=O)Oc1ccccc1C(=O)O")
mw  = Descriptors.MolWt(mol)
fp  = AllChem.GetMorganFingerprintAsBitVect(mol, 2, 2048)

# chematic
import chematic

mol = chematic.from_smiles("CC(=O)Oc1ccccc1C(=O)O")
mw  = mol.mw
fp  = chematic.bulk.ecfp4(["CC(=O)Oc1ccccc1C(=O)O"])[0]  # numpy uint8 array

For large-scale migration, chematic's Python API is designed to be familiar to RDKit users while adding batch-first and browser-native capabilities.

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Benchmark data: Apple M-series, Python 3.12, chematic v0.4.22, RDKit 2026.03.3.
Reproduce all benchmarks: see benchmark details.