smsd 5.9.0

SMSD -- Substructure & MCS search for chemical graphs

SMSD Python Bindings

Python interface for SMSD (Small Molecule Substructure Detector) -- a high-performance library for substructure search, Maximum Common Substructure (MCS), and molecular similarity.

Features

• SMILES parsing and writing -- built-in OpenSMILES parser, no RDKit or CDK required
• Substructure search -- VF2++ subgraph isomorphism
• MCS search -- McSplit with seed-and-extend, orbit pruning, coverage-driven termination
• Tautomer-aware matching -- keto/enol, amide, imidazole tautomer equivalence
• RASCAL screening -- O(V+E) Tanimoto-like similarity upper bound
• Fingerprints -- path-based and MCS-aware fingerprints for pre-screening

Requirements

• Python >= 3.8
• C++17 compiler (GCC 7+, Clang 5+, MSVC 2019+)
• CMake >= 3.15
• pybind11 >= 2.12

Installation

From source (recommended)

cd python/
pip install .

Development install

pip install -e ".[dev]"

Build with specific compiler

CMAKE_ARGS="-DCMAKE_CXX_COMPILER=g++-13" pip install .

Quick Start

from smsd import parse_smiles, find_mcs, is_substructure, similarity

# Parse SMILES strings
benzene = parse_smiles("c1ccccc1")
phenol  = parse_smiles("c1ccc(O)cc1")

# Substructure search
assert is_substructure(benzene, phenol)  # benzene is in phenol

# Maximum Common Substructure
mcs = find_mcs(benzene, phenol)
print(f"MCS size: {len(mcs)} atoms")  # 6

# Similarity
sim = similarity(benzene, phenol)
print(f"Similarity: {sim:.3f}")  # ~0.857

API Reference

SMILES Parsing

from smsd import parse_smiles, to_smiles

mol = parse_smiles("c1ccccc1")
print(mol.n)              # 6 atoms
print(mol.atomic_num)     # [6, 6, 6, 6, 6, 6]
print(mol.aromatic)       # [True, True, True, True, True, True]

smi = to_smiles(mol)      # canonical SMILES string

Substructure Search

from smsd import is_substructure, find_substructure, ChemOptions

query  = parse_smiles("c1ccccc1")
target = parse_smiles("c1ccc(O)cc1")

# Boolean check
if is_substructure(query, target):
    print("Query is a substructure of target")

# Get atom mapping
mapping = find_substructure(query, target)
# Returns list of (query_atom, target_atom) pairs
for qi, ti in mapping:
    print(f"  query atom {qi} -> target atom {ti}")

# Custom options
opts = ChemOptions()
opts.ring_matches_ring_only = True
is_substructure(query, target, opts=opts, timeout_ms=5000)

MCS Search

from smsd import find_mcs, ChemOptions, McsOptions

g1 = parse_smiles("CC(=O)Oc1ccccc1C(=O)O")   # aspirin
g2 = parse_smiles("CC(=O)Nc1ccc(O)cc1")       # acetaminophen

# Default MCS
mapping = find_mcs(g1, g2)
print(f"MCS size: {len(mapping)}")

# Tautomer-aware MCS
taut = ChemOptions.tautomer_profile()
mapping = find_mcs(g1, g2, chem=taut)

# With MCS options
mcs_opts = McsOptions()
mcs_opts.timeout_ms = 5000
mcs_opts.connected_only = True
mapping = find_mcs(g1, g2, opts=mcs_opts)

# Convenience wrapper (accepts SMILES strings directly)
from smsd import mcs
mapping = mcs("c1ccccc1", "Cc1ccccc1", tautomer_aware=True)

Similarity and Screening

from smsd import similarity_upper_bound, screen_targets, similarity

g1 = parse_smiles("c1ccccc1")
g2 = parse_smiles("Cc1ccccc1")

# Single pair
sim = similarity_upper_bound(g1, g2)
print(f"Similarity: {sim:.3f}")

# Convenience wrapper (accepts SMILES)
sim = similarity("c1ccccc1", "Cc1ccccc1")

# Batch screening
library = [parse_smiles(s) for s in smiles_list]
query = parse_smiles("c1ccccc1")
hits = screen_targets(query, library, threshold=0.5)
# Returns indices of molecules with similarity >= 0.5

Fingerprints

from smsd import (
    path_fingerprint, mcs_fingerprint,
    fingerprint_subset, analyze_fp_quality,
    fingerprint, tanimoto,
)

mol = parse_smiles("c1ccccc1")

# Path fingerprint (returns set bit positions)
fp = path_fingerprint(mol, path_length=7, fp_size=2048)

# MCS-aware fingerprint
fp_mcs = mcs_fingerprint(mol, path_length=7, fp_size=2048)

# Subset check (for substructure pre-screening)
query_fp = path_fingerprint(parse_smiles("c1ccccc1"))
target_fp = path_fingerprint(parse_smiles("c1ccc(O)cc1"))
assert fingerprint_subset(query_fp, target_fp)

# Quality analysis
quality = analyze_fp_quality(fp)
print(quality)  # {'set_bits': 12, 'density': 0.006, ...}

# Convenience wrappers
fp = fingerprint("CCO", kind="mcs")
sim = tanimoto(fp, fingerprint("CCCO"))

MolGraph Builder

Build molecules directly without SMILES:

from smsd import MolGraphBuilder

builder = MolGraphBuilder(6)  # 6 atoms
for i in range(6):
    builder.atom(i, 6, charge=0, aromatic=True, in_ring=True)
for i in range(6):
    builder.bond(i, (i + 1) % 6, order=1, in_ring=True, aromatic=True)
benzene = builder.build()

Configuration

from smsd import ChemOptions, McsOptions, BondOrderMode, RingFusionMode

# ChemOptions controls atom/bond matching
chem = ChemOptions()
chem.match_atom_type = True
chem.match_formal_charge = True
chem.tautomer_aware = True
chem.complete_rings_only = True
chem.match_bond_order = BondOrderMode.LOOSE
chem.ring_fusion_mode = RingFusionMode.STRICT

# Named profiles
chem = ChemOptions.tautomer_profile()   # tautomer-aware defaults
chem = ChemOptions.profile("strict")    # strict matching

# McsOptions controls MCS algorithm behavior
opts = McsOptions()
opts.connected_only = True
opts.timeout_ms = 10000
opts.maximize_bonds = True  # MCES mode

Running Tests

cd python/
pip install -e ".[dev]"
pytest tests/ -v

License

Apache 2.0. Copyright (c) 2018-2026 BioInception PVT LTD. Algorithm Copyright (c) 2009-2026 Syed Asad Rahman.