CDK9 Screen tool allows you to predict the potential inhibitory activity of small molecules against human CDK9. This repository provides necessary Python scripts to perform predictions using a predefined environment and dependencies.
If you use CDK9Screen, please cite:
Piazza, L.; Poles, C.; Bononi, G.; Granchi, C.; Di Stefano, M.; Poli, G.; Giordano, A.; Medugno, A.; Napolitano, G.M.; Tuccinardi, T.; Alfano, L. Machine Learning-Based Virtual Screening for the Identification of Novel CDK-9 Inhibitors. Biomolecules 2026, 16, 12. https://doi.org/10.3390/biom16010012
To set up the required environment, use the provided YAML file:
conda env create -f cdk9_env.ymlThen, activate the environment:
conda activate cdk9_envRun the predictions with Python, specifying the required inputs:
python run.py -in input.csv -out output.csv- The input file must be in CSV format and contain a column named
SMILEScontaining the molecular structures. - An example input CSV file is included in the repository for testing purposes.
- The run.py script will generate a CSV file containing an additional column named
Out_predictionsstoring the predicted class. - If a molecule is assigned to class 1, it is predicted to be active; conversely, class 0 corresponds to an "inactive" prediction.