Virtual Screening of Small Molecules Targeting BCL2 with Machine Learning, Molecular Docking, and MD Simulation.

Journal: Biomolecules
PMID: 38785951

Abstract

This study aimed to identify potential BCL-2 small molecule inhibitors using deep neural networks (DNN) and random forest (RF), algorithms as well as molecular docking and molecular dynamics (MD) simulations to screen a library of small molecules. The RF model classified 61% (2355/3867) of molecules as 'Active'. Further analysis through molecular docking with Vina identified CHEMBL3940231, CHEMBL3938023, and CHEMBL3947358 as top-scored small molecules with docking scores of -11, -10.9, and 10.8 kcal/mol, respectively. MD simulations validated these compounds' stability and binding affinity to the BCL2 protein.

Authors

  • Abtin Tondar
    Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya (UOC), 08018 Barcelona, Spain.
  • Sergio Sánchez-Herrero
    Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya (UOC), 08018 Barcelona, Spain.
  • Asim Kumar Bepari
    Department of Pharmaceutical Sciences, North South University (NSU), Dhaka 1229, Bangladesh.
  • Amir Bahmani
    Stanford Deep Data Research Center, Department of Genetics, Stanford University, Stanford, CA 94305, USA.
  • Laura Calvet Liñán
    Telecommunications and Systems Engineering Department, Universitat Autònoma de Barcelona (UAB), Carrer Emprius, 2, 08202 Sabadell, Spain.
  • David Hervás-Marín
    Department of Applied Statistics, Operational Research, and Quality, Universitat Politècnica de València (UPV), 03801 Alcoy, Spain.

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