Machine Learning and Virtual Screening Methods to Discover Potential Cyclin-Dependent Kinase 2 (CDK2) Inhibitors
Abstract
Background: Cyclin-dependent kinase 2 (CDK2) is a key regulator of cell cycle progression and an important therapeutic target in cancer treatment. This study aims to identify novel CDK2 inhibitors using an integrated computational approach combining machine learning and structure-based methods. Methods: A computational pipeline was developed incorporating Lipinski’s Rule of Five filtering, machine learning (ML)-based activity prediction, molecular docking, and molecular dynamics simulations (MDs). A dataset of CDK2 inhibitors with IC50 values was retrieved from ChEMBL, and molecular fingerprints were generated using PaDEL. A 5-fold stratified cross-validation approach was applied to train multiple classifiers, with the random forest model showing the best performance. Predicted active compounds from the InterBioScreen database were subjected to docking against CDK2 (PDB ID: 2FVD) using PyRx, followed by 100 ns MDS for stability analysis. Results: The random forest classifier achieved an AUC-ROC of 0.90 and an accuracy of 0.84. A total of 187 compounds were predicted as active. Among these, two compounds, STOCK4S-00019 and STOCK4S-00025, demonstrated docking scores comparable to the co-crystallized reference ligand. Molecular dynamics simulations confirmed stable binding, consistent interaction patterns, and favorable conformational behavior throughout the simulation period. Conclusions: The identified compounds, STOCK4S-00019 (hit1) and STOCK4S-00025 (hit2), show strong potential as CDK2 inhibitors. These findings support their further investigation through experimental validation and highlight the effectiveness of integrated computational approaches in anticancer drug discovery.
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BibTeX
@article{rampogu2026machine,
title = {{Machine Learning and Virtual Screening Methods to Discover Potential Cyclin-Dependent Kinase 2 (CDK2) Inhibitors}},
author = {Shailima Rampogu and Thananjeyan Balasubramaniyam and Jacek Z. Kubiak and Keun Woo Lee},
journal = {Pharmaceuticals},
year = {2026},
month = jun,
doi = {10.3390/ph19071019},
url = {https://hal.science/hal-05746883v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Machine Learning and Virtual Screening Methods to Discover Potential Cyclin-Dependent Kinase 2 (CDK2) Inhibitors
AU - Shailima Rampogu
AU - Thananjeyan Balasubramaniyam
AU - Jacek Z. Kubiak
AU - Keun Woo Lee
PY - 2026
DA - 2026/06/30
JO - Pharmaceuticals
DO - 10.3390/ph19071019
UR - https://hal.science/hal-05746883v1
AB - Background: Cyclin-dependent kinase 2 (CDK2) is a key regulator of cell cycle progression and an important therapeutic target in cancer treatment. This study aims to identify novel CDK2 inhibitors using an integrated computational approach combining machine learning and structure-based methods. Methods: A computational pipeline was developed incorporating Lipinski’s Rule of Five filtering, machine learning (ML)-based activity prediction, molecular docking, and molecular dynamics simulations (MDs). A dataset of CDK2 inhibitors with IC50 values was retrieved from ChEMBL, and molecular fingerprints were generated using PaDEL. A 5-fold stratified cross-validation approach was applied to train multiple classifiers, with the random forest model showing the best performance. Predicted active compounds from the InterBioScreen database were subjected to docking against CDK2 (PDB ID: 2FVD) using PyRx, followed by 100 ns MDS for stability analysis. Results: The random forest classifier achieved an AUC-ROC of 0.90 and an accuracy of 0.84. A total of 187 compounds were predicted as active. Among these, two compounds, STOCK4S-00019 and STOCK4S-00025, demonstrated docking scores comparable to the co-crystallized reference ligand. Molecular dynamics simulations confirmed stable binding, consistent interaction patterns, and favorable conformational behavior throughout the simulation period. Conclusions: The identified compounds, STOCK4S-00019 (hit1) and STOCK4S-00025 (hit2), show strong potential as CDK2 inhibitors. These findings support their further investigation through experimental validation and highlight the effectiveness of integrated computational approaches in anticancer drug discovery.
SN - 1424-8247
ER -