pycchem 1.1.0

High-performance cheminformatics library - Python bindings for cchem

pycchem

Python bindings for cchem - a high-performance cheminformatics library.

Features

• Fast SMILES canonicalization - 2-3x faster than RDKit
• 1600+ molecular descriptors - Comprehensive descriptor set
• Molecular sanitization - Salt removal, neutralization, aromatization
• Pure C backend - No heavy dependencies

Installation

pip install pycchem

From Source

git clone https://github.com/Vitruves/cchem.git
cd cchem/pycchem
pip install .

Quick Start

Canonicalization

import pycchem

# Canonicalize a SMILES
canon = pycchem.canonicalize("C(C)O")
print(canon)  # CCO

# Check if two SMILES are equivalent
pycchem.are_equivalent("CCO", "OCC")  # True

# Validate SMILES
pycchem.validate("CCO")  # True
pycchem.validate("invalid")  # False

Sanitization

import pycchem

# Remove salts and neutralize
clean = pycchem.sanitize("[Na+].CC(=O)[O-]")
print(clean)  # CC(=O)O

# Specific operations
pycchem.sanitize("C1=CC=CC=C1", operations=["aromatize"])  # c1ccccc1

Molecular Descriptors

import pycchem

# Create a molecule
mol = pycchem.Molecule("CCO")

# Compute all descriptors (1600+)
descriptors = mol.descriptors()
print(descriptors["MolecularWeight"])  # 46.069
print(descriptors["CarbonCount"])  # 2

# Compute specific descriptors
selected = mol.descriptors(["MolecularWeight", "WCLogP", "TPSA"])

# List available descriptors
names = pycchem.list_descriptors()
print(len(names))  # ~1600

Batch Processing

import pycchem

smiles_list = ["CCO", "c1ccccc1", "CC(=O)O"]
results = pycchem.canonicalize_batch(smiles_list)

Command-Line Interface

After installation, the cchem command is available:

# Canonicalize SMILES
cchem canonicalize -S "C(C)O"
# Output: CCO

# Validate SMILES
cchem validate -S "CCO"
# Output: CCO: valid

# Compute descriptors
cchem compute -S "CCO" -d MolecularWeight
# Output: MolecularWeight: 46.069000

# Compute all descriptors
cchem compute -S "CCO" --all -o descriptors.csv

# List available descriptors
cchem compute --list

# Sanitize SMILES
cchem sanitize -S "[Na+].CC(=O)[O-]"
# Output: CC(=O)O

# Compare two SMILES
cchem compare "CCO" "OCC"
# Output: equivalent

# Show version
cchem --version

CLI Commands

Command	Description
canonicalize	Canonicalize SMILES strings
validate	Validate SMILES syntax
compute	Compute molecular descriptors
sanitize	Sanitize SMILES (remove salts, neutralize, etc.)
compare	Check if two SMILES are equivalent

API Reference

Functions

Function	Description
canonicalize(smiles)	Canonicalize SMILES string
canonicalize_batch(list)	Batch canonicalization
sanitize(smiles, operations)	Sanitize molecule
validate(smiles)	Check SMILES validity
are_equivalent(s1, s2)	Check if SMILES are equivalent
compute_descriptor(smiles, name)	Compute single descriptor
compute_descriptors(smiles, names)	Compute multiple descriptors
list_descriptors()	List all descriptor names
version()	Get library version

Molecule Class

mol = pycchem.Molecule(smiles)
mol.canonical_smiles  # Canonical form
mol.descriptors()     # All descriptors
mol.descriptor(name)  # Single descriptor

Sanitization Operations

Operation	Description
complete	Full sanitization (default)
unsalt	Remove salts/counter-ions
aromatize	Perceive aromaticity
kekulize	Convert to Kekule form
neutralize	Neutralize charges
normalize	Normalize functional groups
remove_stereo	Remove stereochemistry
remove_isotopes	Remove isotope labels
remove_h	Remove explicit hydrogens

Performance

Benchmarks on MacBook Air M3 (8-core, 16GB RAM):

Library	Throughput	vs RDKit
pycchem	28,311/s	2.52x
RDKit	11,229/s	1.00x

Requirements

• Python 3.9+
• cffi >= 1.15.0

License

Apache License 2.0