SMSD 7.2.2 for Python

Substructure search, maximum common substructure (MCS), fingerprints and similarity screening. Core matching does not require RDKit, CDK or Java. RDKit is optional for molecule conversion and drawing.

Install

The 7.2.2 wheels use CPython 3.14 and include CPU/OpenMP support:

Install 7.2.2 from PyPI when listed:

python -m pip install smsd==7.2.2

You can also download and install the matching wheel from the GitHub release. Intel macOS, Linux arm64 and other Python versions require a source build.

Quick start

import smsd

query = "c1ccccc1"       # benzene
target = "c1ccc(O)cc1"   # phenol

assert smsd.is_substructure(query, target)
mapping = smsd.find_mcs(query, target, timeout_ms=1000)
print(f"MCS: {len(mapping)} atoms")  # MCS: 6 atoms

Mappings link query atom indices to target atom indices. An empty mapping means no match was found. A search timeout can leave a smaller MCS.

Fingerprints

Radius 2 gives ECFP4; use mode="fcfp" for functional-class fingerprints (Rogers and Hahn, 2010). similarity() is a screening upper bound. Use fingerprint metrics for fingerprint similarity:

import smsd

query_fp = smsd.fingerprint_from_smiles("c1ccccc1", radius=2, fp_size=2048)
target_fp = smsd.fingerprint_from_smiles("c1ccc(O)cc1", radius=2, fp_size=2048)
score = smsd.tanimoto_coefficient(query_fp, target_fp)
assert 0.0 <= score <= 1.0

For count fingerprints and other metrics, see the fingerprint examples.

Batch search

Parse molecules once when reusing them. Batch results follow target order:

import smsd

query = smsd.parse_smiles("c1ccccc1")
targets = [smsd.parse_smiles(s) for s in ["c1ccc(O)cc1", "CCO"]]
assert smsd.batch_substructure(query, targets, num_threads=2) == [True, False]
assert [len(m) for m in smsd.batch_find_substructure(query, targets)] == [6, 0]
sizes = smsd.batch_mcs_size(query, targets, timeout_ms=1000)
assert sizes == [6, 2]

Use batch_mcs() for mappings and batch_mcs_size() for atom counts. TargetCorpus supports repeated queries against one collection.

Using RDKit

Install RDKit separately to pass its molecules directly to SMSD. Returned mappings use the original RDKit atom indices:

from rdkit import Chem
import smsd

query = Chem.MolFromSmiles("c1ccccc1")
target = Chem.MolFromSmiles("c1ccc(O)cc1")
mapping = smsd.find_mcs(query, target, timeout_ms=1000)
assert len(mapping) == 6

More examples

The Python guide covers chemistry options, SMARTS, stereo, tautomer matching, fingerprints, MOL/SDF I/O, drawing and batch operations. See the examples for complete workflows.

Build from source

Run these commands at the repository root with a C++17 compiler and CMake 3.18 or later. Source metadata allows Python 3.9 or later:

python -m pip install build
python -m pip install -e ".[dev]"
python -m build

Release wheels use CPU/OpenMP. Metal and CUDA are optional source-build features that need compatible tools and hardware. gpu_device_info() reports the active backend; batch matching uses the CPU.

Tests and benchmarks

Version 7.2.2 passes 691 Python tests with 8 optional skips per platform; see the test report. The 7.2.0 benchmark report contains measured comparisons for its recorded versions and molecules.

Other languages

Java and C++ are also available. Java 7.2.2 is on GitHub; Maven Central remains at com.bioinceptionlabs:smsd:7.1.1 until 7.2.2 is published there.

Citation

If you use SMSD Pro in your research, please cite:

Rahman SA. SMSD Pro: Coverage-Driven, Tautomer-Aware Maximum Common Substructure Search. ChemRxiv, 2026. DOI: 10.26434/chemrxiv.15001534/v1

For the original SMSD toolkit, please also cite:

Rahman SA, Bashton M, Holliday GL, Schrader R, Thornton JM. Small Molecule Subgraph Detector (SMSD) toolkit. Journal of Cheminformatics, 1:12, 2009. DOI: 10.1186/1758-2946-1-12

A machine-readable CITATION.cff is available for automated citation tools.

Licence

Apache 2.0 — Copyright (c) 2018-2026 Syed Asad Rahman, BioInception PVT LTD. See LICENSE and NOTICE for licensing and attribution.