Currently, there is neither effective antiviral drugs nor vaccine for coronavirus disease 2019 (COVID-19) caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to its high conservativeness and low similarity with human genes, SARS-CoV-2 main protease (Mpro) is one of the most favorable drug targets. However, the current understanding of the molecular mechanism of Mpro inhibition is limited by the lack of reliable binding affinity ranking and prediction of existing structures of Mpro-inhibitor complexes. This work integrates mathematics (i.e., algebraic topology) and deep learning (MathDL) to provide a reliable ranking of the binding affinities of 137 SARS-CoV-2 Mpro inhibitor structures. We reveal that Gly143 residue in Mpro is the most attractive site to form hydrogen bonds, followed by Glu166, Cys145, and His163. We also identify 71 targeted covalent bonding inhibitors. MathDL was validated on the PDBbind v2016 core set benchmark and a carefully curated SARS-CoV-2 inhibitor dataset to ensure the reliability of the present binding affinity prediction. The present binding affinity ranking, interaction analysis, and fragment decomposition offer a foundation for future drug discovery efforts.

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Published in Chemical Science, v. 11, issue 44.

© The Royal Society of Chemistry 2020

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

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This work was supported in part by NIH grant GM126189, NSF Grants DMS-1721024, DMS-1761320, and IIS1900473, Michigan Economic Development Corporation, George Mason University award PD45722, Bristol-Myers Squibb, and Pzer. The authors thank The IBM TJ Watson Research Center, The COVID-19 High Performance Computing Consortium, and NVIDIA for computational assistance.

d0sc04641h1.pdf (151 kB)
Supplementary information: SupportingTables.xls: spreadsheets contain information for all supporting tables from S1 to S8

d0sc04641h2.zip (11667 kB)
Supporting information: FileS1.zip: 3D structures generated by our MathPose for 141 ligands in SARS-CoV 2D set

d0sc04641h3.xlsx (399 kB)
Supporting information: FigS1.pdf: deep learning architecture of MathDL model