Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study.

Journal: Chemistry (Weinheim an der Bergstrasse, Germany)
Published Date: Sep 28, 2023

Abstract

In the context of drug discovery, computational methods were able to accelerate the challenging process of designing and optimizing a new drug candidate. Amongst the possible atomistic simulation approaches, metadynamics (metaD) has proven very powerful. However, the choice of collective variables (CVs) is not trivial for complex systems. To automate the process of CVs identification, two different machine learning algorithms were applied in this study, namely DeepLDA and Autoencoder, to the metaD simulation of a well-researched drug/target complex, consisting in a pharmacologically relevant non-canonical DNA secondary structure (G-quadruplex) and a metallodrug acting as its stabilizer, as well as solvent molecules.

Authors

  • Gohar Ali Siddiqui
    Professorship of Simulation of Nanosystems for Energy Conversion Department of Electrical and Computer Engineering School of Computation, Information and Technology, Technical University of Munich (TUM), Hans-Piloty-Str. 1, 85748, Garching b. München, Germany.
  • Julia A Stebani
    Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany.
  • Darren Wragg
    Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany.
  • Phaedon-Stelios Koutsourelakis
    Professorship for Data-driven Materials Modeling School of Engineering and Design, Technical University of Munich (TUM), Boltzmannstr. 15, 85748, Garching b. München, Germany.
  • Angela Casini
    Chair of Medicinal and Bioinorganic Chemistry Department of Chemistry, School of Natural Sciences, Technical University of Munich (TUM), Lichtenbergstr. 4, 85748, Garching b. München, Germany.
  • Alessio Gagliardi
    Technische Universität München, Karlstr. 45, 80333 Munich, Germany.

Keywords

External Resources

Popular Topics
Recent Journals