AlphaFold Kinase Optimizer: Enhancing Virtual Screening Performance Through Automated Refinement of AlphaFold-Based Kinase Structures.

Journal: Proteins
Published Date: Sep 16, 2025

Abstract

AlphaFold (AF) is a valuable tool for generating protein 3D structures, but its application in structure-based drug design is limited. In this study, we introduce AF Optimizer-a new deep learning-assisted approach that refines binding site geometry based on neural network scores and calculated free binding energy. We refined TTK protein geometry using AF Optimizer and performed virtual screening using the optimized version of the AF-generated protein model. The application of the model showed a decrease in steric clashes with ligands from known crystal complexes, more precise results of molecular docking and virtual screening, and hits enrichment during a prospective in vitro study.

Authors

  • Sergei Evteev
    Dukhov Automatics Research Institute (VNIIA), Moscow, Russian Federation.
  • Yan Ivanenkov
    Pharma.AI Department , Insilico Medicine, Inc. , Baltimore , Maryland 21218 , United States.
  • Andrew Aiginin
    Insilico Medicine AI Limited, Abu Dhabi, UAE.
  • Maksim Kuznetsov
    Insilico Medicine Canada Inc., Montreal, Quebec, Canada.
  • Rim Shayakhmetov
    Insilico Medicine , Baltimore , Maryland 21218 , United States.
  • Maksim Knyazev
    Insilico Medicine AI Limited, Abu Dhabi, UAE.
  • Dmitry Bezrukov
    Insilico Medicine Kong Kong Ltd., Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok, Hong Kong.
  • Alex Malyshev
    Insilico Medicine Hong Kong Ltd., Hong Kong, Pak Shek Kok, Hong Kong.
  • Maxim Malkov
    Insilico Medicine AI Limited, Abu Dhabi, UAE.
  • Alex Aliper
    Pharma.AI Department , Insilico Medicine, Inc. , Baltimore , Maryland 21218 , United States.
  • Alex Zhavoronkov
    Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA.

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