Molecule Activity Cliff Estimation (MoleculeACE) is a tool for evaluating the predictive performance on activity cliff compounds of machine learning models.
MoleculeACE can be used to:
- Analyze and compare the performance on activity cliffs of machine learning methods typically employed in QSAR.
- Identify best practices to enhance a model’s predictivity in the presence of activity cliffs.
- Design guidelines to consider when developing novel QSAR approaches.
In a benchmark study we collected and curated bioactivity data on 30 macromolecular targets, which were used to evaluate the performance of many machine learning algorithms on activity cliffs. We used classical machine learning methods combined with common molecular descriptors and neural networks based on unstructured molecular data like molecular graphs or SMILES strings.
Activity cliffs are molecules with small differences in structure but large differences in potency. Activity cliffs play an important role in drug discovery, but the bioactivity of activity cliff compounds are notoriously difficult to predict.
Any regression model can be evaluated on activity cliff performance using MoleculeACE on third party data or the 30 included molecular bioactivity data sets. All 23 machine learning strategies covered in our benchmark study can be used out of the box.
MoleculeACE currently supports Python 3.8
MoleculeACE can be installed as
pip install MoleculeACE
git clone https://github.com/derekvantilborg/MoleculeACE
pip install rdkit-pypi pandas numpy pandas chembl_webresource_client scikit-learn matplotlib tqdm progress python-Levenshtein
Run an out-of-the-box model on one of the many included datasets
from MoleculeACE.benchmark import load_data, models, evaluation, utils # Define which dataaset, descriptor, and algorithm to use dataset = 'CHEMBL287_Ki' descriptor = utils.Descriptors.CANONICAL_GRAPH algorithm = utils.Algorithms.MPNN # Load data data = load_data(dataset, descriptor=descriptor) # Train and a model, if config_file = None, hyperparameter optimization is performed model = models.train_model(data, algorithm=algorithm, config_file=None) predictions = model.test_predict() # Evaluate your model on activity cliff compounds results = evaluation.evaluate(data=data, predictions=predictions)
Evaluate your own data or model
from MoleculeACE.benchmark import load_data, models, evaluation, utils, process_data # Setup some variables dataset = 'path/to/your_own_data.csv' descriptor = utils.Descriptors.ECFP algorithm = utils.Algorithms.GBM # Process your data data process_data(dataset, smiles_colname='smiles', y_colname='exp_mean [nM]', test_size=0.2, fold_threshold=10, similarity_threshold=0.9) # Load data data = load_data(dataset, descriptor=descriptor, tolog10=True) # Train and optimize a model. You can also implement your own model here model = models.train_model(data, algorithm=algorithm, config_file=None) predictions = model.test_predict() # Evaluate your model on activity cliff compounds results = evaluation.evaluate(data=data, predictions=predictions)
How to cite
MoleculeACE is under MIT license. For use of specific models, please refer to the model licenses found in the original packages.