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
Affiliations
Lars Elend
Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
Luise Jacobsen
Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
Tim Cofala
Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
Jonas Prellberg
Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
Thomas Teusch
Department of Physics, Carl von Ossietzky University, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany
Oliver Kramer
Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
Ilia A. Solov’yov
Department of Physics, Carl von Ossietzky University, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany
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.
