Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates
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-obj...
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| description | 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-likeliness or the fulfillment of filter constraints. The experimental part analyzes the evolutionary process and discusses the inhibitor candidates. |
| doi_str_mv | 10.48550/arxiv.2005.02666 |
| format | Article |
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| subjects | Antiretroviral drugs Artificial intelligence Computer Science - Learning Computer Science - Neural and Evolutionary Computing Evolutionary algorithms Multiple objective analysis Protease Protease inhibitors Quantitative Biology - Biomolecules Severe acute respiratory syndrome coronavirus 2 Viral diseases Viruses |
| title | Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates |
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