Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds

Abstract

The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structure of SARS-CoV-2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates. Recently, our group has developed a novel deep learning platform - Deep Docking (DD) which provides fast prediction of docking scores of Glide (or any other docking program) and, hence, enables structure-based virtual screening of billions of purchasable molecules in a short time. In the current study we applied DD to all 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS-CoV-2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community.

Keywords: COVID-19; SARS-CoV-2; deep learning; protease inhibitors; virtual screening.

Figure 1

Evaluation of Glide SP docking protocol on SARS Mpro inhibitors. a) Redocking of ligand 7 to the SARS Mpro active site (PDB 4MDS) resulted in 0.86 Å of r.m.s.d (root mean square deviation) between computational (pink) and x‐ray (cyan) poses. b) ROC curves and AUC obtained by docking 81 inhibitors and ∼4,000 decoys to the Mpro active site with Glide SP and XP protocols.

Figure 2

P1, P2, P3 and P1′ groups in the substrate‐binding subsites of SARS MPro (PDB 4MDS). The compound, SID 24808289, is represented as orange sticks. The surface of the protease binding site is partitioned into different sections depending on the pocket occupancy of the compound.

Figure 3

a) Predicted binding pose of our top hit compound (shown in magenta sticks) in the SARS‐CoV‐2 pocket with the annotated pocket occupancy b) Representation of the interactions of ZINC000541677852 in the SARS‐CoV‐2 pocket. Blue dotted lines represent hydrogen bonds with protein backbone atoms, and green dotted lines represent hydrogen bonds with sidechain atoms.

Figure 4

Score probability of top 1,000 ranked compounds extracted from docking of a set of protease inhibitors (7,800 compounds), a random sample of ZINC15 (1 million molecules) and top 1 million molecules from DD.