Machine Learning to Predict CDK4 Inhibition.
1Department of Physics, Institute of Exact Sciences, Federal University of Alfenas, Alfenas, MG, Brazil.
Methods in Molecular Biology (Clifton, N.J.)
|October 11, 2025
Summary
This study presents a machine learning workflow to predict Cyclin-dependent kinase 4 (CDK4) inhibition using atomic coordinates. The developed neural network model leverages docking simulations and binding affinity data for anticancer drug discovery.
Area of Science:
- Computational chemistry
- Drug discovery
- Machine learning
Background:
- Cyclin-dependent kinase 4 (CDK4) is a crucial target for anticancer drug development.
- Existing crystallographic structures enable computational docking simulations for CDK4 inhibition.
- Binding affinity data for CDK4 inhibitors facilitates the creation of predictive machine learning models.
Purpose of the Study:
- To describe an integrated workflow for constructing a neural network model to predict CDK4 inhibition.
- To utilize atomic coordinates and docking results for regression modeling of CDK4 inhibition.
- To provide accessible datasets and Jupyter Notebooks for reproducible research.
Main Methods:
- Employing Molegro Data Modeller (MDM) to build a regression model based on docking results.
- Utilizing protein-pose structures generated by Molegro Virtual Docker (MVD).
- Integrating experimental binding data from BindingDB for model training.
Main Results:
- A functional workflow integrating docking simulations and machine learning for CDK4 inhibition prediction was established.
- The workflow successfully builds regression models to calculate binding affinity based on atomic coordinates.
- Associated CDK4 datasets and Jupyter Notebooks are publicly available on GitHub.
Conclusions:
- The described workflow offers a robust method for predicting CDK4 inhibition using computational approaches.
- This integrated approach facilitates the identification and development of novel anticancer drugs targeting CDK4.
- The availability of code and data promotes further research and application in drug discovery.
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