Abstract
Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus replication is via protease inhibition. We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2 ’s main protease. Based on the SELFIES representation the EMOA maximizes the binding of candidate ligands to the protein using the docking tool QuickVina 2, while at the same time taking into account further objectives like drug-likeness or the fulfillment of filter constraints. The experimental part analyzes the evolutionary process and discusses the inhibitor candidates.
Keywords
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PDB: protein data base, https://www.rcsb.org.
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Acknowledgements
We thank Ahmad Reza Mehdipour, Max Planck Institute of Biophysics, Frankfurt, Germany, for useful comments improving this manuscript. Furthermore, we thank the German Research Foundation (DFG) for supporting our work within the Research Training Group SCARE (GRK 1765/2).
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Cofala, T., Elend, L., Mirbach, P., Prellberg, J., Teusch, T., Kramer, O. (2020). Evolutionary Multi-objective Design of SARS-CoV-2 Protease Inhibitor Candidates. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12270. Springer, Cham. https://doi.org/10.1007/978-3-030-58115-2_25
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