Potential Non-Covalent SARS-CoV-2 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches and Reviewed by Human Medicinal Chemist in Virtual Reality

作者：Alex Zhavoronkov, Bogdan Zagribelnyy, Alexander Zhebrak, Vladimir Aladinskiy, Victor A. Terentiev, Quentin Vanhaelen, Dmitry S. Bezrukov, Daniil Polykovskiy, Rim Shayakhmetov, Andrey Filimonov, Michael R. Bishop, Steve McCloskey, Edgardo Leija, Deborah Bright, Keita Funakawa, Yen‐Chu Lin, Shih-Hsien Huang, Hsuan‐Jen Liao, Alex Aliper, Yan A. Ivanenkov · 发表于：ChemRxiv · 年份：2020 · DOI：10.26434/chemrxiv.12301457 · 被引用次数：29 · 研究领域：Computational Drug Discovery Methods、Machine Learning in Materials Science、vaccines and immunoinformatics approaches

<div> <div> <div> <div> <p>One of the most important SARS-CoV-2 protein targets for therapeutics is the 3C-like protease (main protease, Mpro). In our previous work1we used the first Mpro crystal structure to become available, 6LU7. On February 4, 2020 Insilico Medicine released the first potential novel protease inhibitors designed using a de novo,AI-driven generative chemistry approach. Nearly 100 X-ray structures of Mpro co-crystallized both with covalent and non-covalent ligands have been published since then. Here we utilize the recently published 6W63 crystal structure of Mpro complexed with a non-covalent inhibitor and combined two approaches used in our previous study: ligand-based and crystal structure-based. We published 10 representative structures for potential development with 3D representation in PDB format and welcome medicinal chemists for broad discussion and generated output analysis. The molecules in SDF format and PDB-models for generated protein-ligand complexes are available here and at https://insilico.com/ncov-sprint/.Medicinal chemistry VR analysis was provided by Nanome team and the video of VR session is available at https://bit.ly/ncov-vr. </p> </div> </div> </div> </div>