Cypstrate

CYPstrate consists of a collection of machine learning classifiers (random forest and support vector machines) for the prediction of substrates and non-substrates of the nine most important human CYP isozymes in the metabolism of xenobiotics (i.e. CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1 and 3A4). The models are trained on a high-quality data set of 1831 substrates and non-substrates compiled from public sources.

Installation

# requires Python 3.8
pip install -U cypstrate

Usage

CYPstrate can be called from the command line. Examples:

# input in SMILES format
cypstrate "CCOC(=O)N1CCN(CC1)C2=C(C(=O)C2=O)N3CCN(CC3)C4=CC=C(C=C4)OC"

# prediction is one of "best_performance" (default) or "full_coverage"
cypstrate --prediction-mode full_coverage "CCN(C)C(=O)OC1=CC=CC(=C1)C(C)N(C)C"

# input can be a file
cypstrate molecules.sdf > result.csv

# output format can be specified
cypstrate --output sdf molecules.smiles > result.sdf

# more information via --help
cypstrate --help

The model can be used in Python. Calling the predict function of the CypstrateModel class results in a pandas DataFrame containing the prediction results for each input molecule.

from cypstrate import CypstrateModel

model = CypstrateModel()

# "predict" method accepts a list of SMILES representations
df_predictions = model.predict(['CCN(C)C(=O)OC1=CC=CC(=C1)C(C)N(C)C'])

# ... or a list of file paths
df_predictions = model.predict(['part1.sdf', 'part2.sdf'])

The result DataFrame contains the columns:

• mol_id: unique number identifying the input molecule
• input: the raw representation provided as input (e.g. OCCCCC)
• input_type: the representation type of the input (e.g. smiles)
• source: the input source (e.g. my_molecules.sdf)
• name: the name of the input molecule (if provided in the input)
• input_mol: the RDKit molecule parsed from the input representation
• preprocessed_mol: the RDKit molecule after preprocessing
• errors: a list of errors that occured during reading or preprocessing the input
• prediction_1a2, prediction_2a6, prediction_2b6, prediction_2c8, prediction_2c9, prediction_2c19, prediction_2d6, prediction_2e1, prediction_3a4: probability (between 0 and 1) of being a substrate of the given CYP isozyme
• neighbor_1a2, neighbor_2a6, neighbor_2b6, neighbor_2c8, neighbor_2c9, neighbor_2c19, neighbor_2d6,neighbor_2e1,neighbor_3a4: similarity to the most similar molecule in the corresponding training set

Contribute

conda env create -f environment.yml
conda activate cypstrate
pip install -e .[dev,test]
ptw

Contributors

• Malte Holmer
• Steffen Hirte
• Axinya Tokareva