MolPipeline is a Python package for processing molecules with RDKit in scikit-learn.
The scikit-learn package provides a large variety of machine
learning algorithms and data processing tools, among which is the Pipeline class, allowing users to
prepend custom data processing steps to the machine learning model.
MolPipeline extends this concept to the field of cheminformatics by
wrapping standard RDKit functionality, such as reading and writing SMILES strings
or calculating molecular descriptors from a molecule-object.
MolPipeline aims to provide:
- Automated end-to-end processing from molecule data sets to deployable machine learning models.
- Scalable parallel processing and low memory usage through instance-based processing.
- Standard pipeline building blocks for flexibly building custom pipelines for various cheminformatics tasks.
- Consistent error handling for tracking, logging, and replacing failed instances (e.g., a SMILES string that could not be parsed correctly).
- Integrated and self-contained pipeline serialization for easy deployment and tracking in version control.
Sieg J, Feldmann CW, Hemmerich J, Stork C, Sandfort F, Eiden P, and Mathea M, MolPipeline: A python package for processing
molecules with RDKit in scikit-learn, J. Chem. Inf. Model., doi:10.1021/acs.jcim.4c00863, 2024
Further links: arXiv
Feldmann CW, Sieg J, and Mathea M, Analysis of uncertainty of neural
fingerprint-based models, 2024
Further links: repository
pip install molpipeline
The notebooks folder contains many basic and advanced examples of how to use Molpipeline.
A nice introduction to the basic usage is in the 01_getting_started_with_molpipeline notebook.
Create a fingerprint-based prediction model:
from molpipeline import Pipeline
from molpipeline.any2mol import AutoToMol
from molpipeline.mol2any import MolToMorganFP
from molpipeline.mol2mol import (
ElementFilter,
SaltRemover,
)
from sklearn.ensemble import RandomForestRegressor
# set up pipeline
pipeline = Pipeline([
("auto2mol", AutoToMol()), # reading molecules
("element_filter", ElementFilter()), # standardization
("salt_remover", SaltRemover()), # standardization
("morgan2_2048", MolToMorganFP(n_bits=2048, radius=2)), # fingerprints and featurization
("RandomForestRegressor", RandomForestRegressor()) # machine learning model
],
n_jobs=4)
# fit the pipeline
pipeline.fit(X=["CCCCCC", "c1ccccc1"], y=[0.2, 0.4])
# make predictions from SMILES strings
pipeline.predict(["CCC"])
# output: array([0.29])Calculating molecular descriptors from SMILES strings is straightforward. For example, physicochemical properties can be calculated like this:
from molpipeline import Pipeline
from molpipeline.any2mol import AutoToMol
from molpipeline.mol2any import MolToRDKitPhysChem
pipeline_physchem = Pipeline(
[
("auto2mol", AutoToMol()),
(
"physchem",
MolToRDKitPhysChem(
standardizer=None,
descriptor_list=["HeavyAtomMolWt", "TPSA", "NumHAcceptors"],
),
),
],
n_jobs=-1,
)
physchem_matrix = pipeline_physchem.transform(["CCCCCC", "c1ccccc1(O)"])
physchem_matrix
# output: array([[72.066, 0. , 0. ],
# [88.065, 20.23 , 1. ]])MolPipeline provides further features and descriptors from RDKit, for example Morgan (binary/count) fingerprints and MACCS keys. See the 04_feature_calculation notebook for more examples.
Molpipeline provides several clustering algorithms as sklearn-like estimators. For example, molecules can be clustered by their Murcko scaffold. See the 02_scaffold_split_with_custom_estimators notebook for scaffolds splits and further examples.
from molpipeline.estimators import MurckoScaffoldClustering
scaffold_smiles = [
"Nc1ccccc1",
"Cc1cc(Oc2nccc(CCC)c2)ccc1",
"c1ccccc1",
]
linear_smiles = ["CC", "CCC", "CCCN"]
# run the scaffold clustering
scaffold_clustering = MurckoScaffoldClustering(
make_generic=False, linear_molecules_strategy="own_cluster", n_jobs=16
)
scaffold_clustering.fit_predict(scaffold_smiles + linear_smiles)
# output: array([1., 0., 1., 2., 2., 2.])This software is licensed under the MIT license. See the LICENSE file for details.