Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates
This work addresses the urgent need for drug candidates against SARS-CoV-2, but it is incremental as it applies existing evolutionary methods to a new viral target.
The authors tackled the problem of designing protease inhibitors for SARS-CoV-2 by proposing an evolutionary multi-objective algorithm to generate candidate ligands, resulting in the identification of potential inhibitor candidates through computational docking and analysis.
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.