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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
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Combining MVD and Ridge Method to Predict CDK2 Inhibition.

Sema Nur Pehlivan1, Amauri Duarte da Silva2, Walter Filgueira de Azevedo3

  • 1Department of Bioengineering, Institute of Science and Technology, Marmara University, Kadıköy, Istanbul, Turkey.

Methods in Molecular Biology (Clifton, N.J.)
|October 11, 2025
PubMed
Summary
This summary is machine-generated.

Molegro Virtual Docker (MVD) combined with Scikit-Learn predicts protein inhibition. This approach enhances binding affinity prediction accuracy for targets like cyclin-dependent kinase 2 (CDK2) compared to standard methods.

Keywords:
Artificial intelligenceCDK2Machine learningMolegro Virtual DockerSAnDReS 2.0Scoring function space

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Molegro Virtual Docker (MVD) is a widely used docking program for protein-ligand interactions.
  • MVD offers flexibility through 16 combinations of search algorithms and scoring functions.
  • Docking results from MVD have been successfully applied to predict protein inhibition.

Purpose of the Study:

  • To integrate MVD with Scikit-Learn's Ridge regression for enhanced predictive modeling.
  • To explore the scoring function space for improved computational drug design.
  • To predict the inhibition of cyclin-dependent kinase 2 (CDK2) using this integrated approach.

Main Methods:

  • Utilized Molegro Virtual Docker (MVD) for docking simulations.
  • Integrated MVD outputs with Scikit-Learn's Ridge regression machine learning model.
  • Applied the combined method to predict cyclin-dependent kinase 2 (CDK2) inhibition.

Main Results:

  • The integrated MVD and Scikit-Learn model demonstrated superior predictive performance.
  • The computational model achieved better binding affinity prediction compared to classical scoring functions.
  • The study explored the concept of scoring function space for model development.

Conclusions:

  • Combining MVD with machine learning, specifically Ridge regression, offers a powerful approach for predicting protein inhibition.
  • This integrated method provides enhanced predictive accuracy for binding affinity.
  • The developed computational model shows potential for drug discovery and development efforts.