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-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.Comment: 15 pages, 7 figures, submitted to PPSN 202

Molecular Docking Reveals Ivermectin and Remdesivir as Potential Repurposed Drugs Against SARS-CoV-2

SARS-CoV-2 is a newly emerged coronavirus that causes a respiratory disease with variable severity and fatal consequences. It was first reported in Wuhan and subsequently caused a global pandemic. The viral spike protein binds with the ACE-2 cell surface receptor for entry, while TMPRSS2 triggers its membrane fusion. In addition, RNA dependent RNA polymerase (RdRp), 3′–5′ exoribonuclease (nsp14), viral proteases, N, and M proteins are important in different stages of viral replication. Accordingly, they are attractive targets for different antiviral therapeutic agents. Although many antiviral agents have been used in different clinical trials and included in different treatment protocols, the mode of action against SARS-CoV-2 is still not fully understood. Different potential repurposed drugs, including, chloroquine, hydroxychloroquine, ivermectin, remdesivir, and favipiravir, were screened in the present study. Molecular docking of these drugs with different SARS-CoV-2 target proteins, including spike and membrane proteins, RdRp, nucleoproteins, viral proteases, and nsp14, was performed. Moreover, the binding affinities of the human ACE-2 receptor and TMPRSS2 to the different drugs were evaluated. Molecular dynamics simulation and MM-PBSA calculation were also conducted. Ivermectin and remdesivir were found to be the most promising drugs. Our results suggest that both these drugs utilize different mechanisms at the entry and post-entry stages and could be considered potential inhibitors of SARS-CoV-2 replication.</jats:p

Versatile clinical movement analysis using statistical parametric mapping in movementRx

202403 bcvcVersion of RecordOthersAgency for Science, Technology and Research; National Health Group; Nanyang Technological UniversityPublishedC

Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration

Abstract “Virtual Screening” is a common step of in silico drug design, where researchers screen a large library of small molecules (ligands) for interesting hits, in a process known as “Docking”. However, docking is a computationally intensive and time-consuming process, usually restricted to small size binding sites (pockets) and small number of interacting residues. When the target site is not known (blind docking), researchers split the docking box into multiple boxes, or repeat the search several times using different seeds, and then merge the results manually. Otherwise, the search time becomes impractically long. In this research, we studied the relation between the search progression and Average Sum of Proximity relative Frequencies (ASoF) of searching threads, which is closely related to the search speed and accuracy. A new inter-process spatio-temporal integration method is employed in Quick Vina 2, resulting in a new docking tool, QuickVina-W, a suitable tool for “blind docking”, (not limited in search space size or number of residues). QuickVina-W is faster than Quick Vina 2, yet better than AutoDock Vina. It should allow researchers to screen huge ligand libraries virtually, in practically short time and with high accuracy without the need to define a target pocket beforehand