Large library docking for novel SARS-CoV-2 main protease non-covalent and covalent inhibitors

Abstract

Antiviral therapeutics to treat SARS-CoV-2 are much desired for the on-going pandemic. A well-precedented viral enzyme is the main protease (MPro), which is now targeted by an approved drug and by several investigational drugs. With the inevitable liabilities of these new drugs, and facing viral resistance, there remains a call for new chemical scaffolds against MPro. We virtually docked 1.2 billion non-covalent and a new library of 6.5 million electrophilic molecules against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC50 of 29 μM and 20 μM, respectively. Several series were optimized, resulting in inhibitors active in the low micromolar range. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. Together, these compounds reveal new chemotypes to aid in further discovery of MPro inhibitors for SARS-CoV-2 and other future coronaviruses.

Structure-based discovery of inhibitors of the SARS-CoV-2 Nsp14N7-methyltransferase

“…In total, 65 compounds were selected for purchasing, 50 from Enamine and 19 from WuXi, and overall, 53 were successfully synthesized for a fulfilment rate of 82%. files were generated as previously described [63][64][65] . Briefly, the electrophiles were converted to their transition state product and a dummy atom was placed indicating to the docking algorithm which atom should be modeled covalently bound to the sulfur of the cysteine.…”

“…The optimized docking setup from the SARS-CoV-2 second noncovalent lead-like screen described above was used. Three different screens were run with different matching spheres 61 -those in the adenine-site, SAM-tail site, or all matching spheres (Figure 4A), with 15,738,235 docked and files were generated as previously described [63][64][65] . Briefly, the electrophiles were converted to their transition state product and a dummy atom was placed indicating to the docking algorithm which atom should be modeled covalently bound to the sulfur of the cysteine.…”

Surely you are joking, Mr Docking!

“…Furthermore, ZINC636416501 was also ranked well by the MMGBSA strategy of Cerón-Carrasco, 2 which effectively contradicts the authors’ conclusion that none of the rescored molecules were active. In this context, it's relevant to mention that most experimentally validated Mpro virtual screening campaigns performed by several leading CADD groups hits with rather modest experimental activities, 36–38 which suggests that the active site of this enzyme represents a challenging target. It eventually became evident that SAR-CoV-2 Mpro assays are very dependent on experimental conditions, 37–39 and that potency evaluations require rigorous standardization.…”

“…Nevertheless, these studies reinforced the idea that docking can indeed identify suitable starting points for medicinal chemistry optimization; many of those initial weak hits were consequently optimized into potent SARS-CoV-2 inhibitors with significant antiviral activity. [36][37][38]…”

Rapid Traversal of Ultralarge Chemical Space using Machine Learning Guided Docking Screens

“…Recently, structure-based virtual screens of ultralarge libraries have identified ligands of important therapeutic targets, demonstrating that expanding the coverage of chemical space can accelerate hit discovery. [6][7][8][9][10] The most recently published docking screens have reached billions of compounds [11][12][13] , but these massive libraries are demanding to evaluate due to the substantial computational resources required. The make-on-demand databases will also continue to grow and likely reach several hundred billion compounds in the near future, which will be unfeasible to screen even with the fastest structure-based docking algorithms.…”