Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations

Lars Elend, Luise Jacobsen, Tim Cofala, Jonas Prellberg, Thomas Teusch, Oliver Kramer*, Ilia A. Solov’Yov

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates.

Original languageEnglish
Article number4020
JournalMolecules
Volume27
Issue number13
Number of pages25
ISSN1420-3049
DOIs
Publication statusPublished - Jul 2022

Keywords

  • artificial intelligence
  • drug design
  • evolutionary algorithms
  • molecular dynamics
  • neural networks
  • SARS-CoV-2

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