A community effort in SARS-CoV-2 drug discovery.

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.

In silico design and computational evaluation of novel 2-arylaminopyrimidine-based compounds as potential multi-targeted protein kinase inhibitors: application for the native and mutant (T315I) Bcr-Abl tyrosine kinase.

An integrated computational approach to drug discovery was used to identify novel potential inhibitors of the native and mutant (T315I) Bcr-Abl tyrosine kinase, the enzyme playing a key role in the pathogenesis of chronic myeloid leukemia (CML). This approach included i) design of chimeric molecules based on the 2-arylaminopyrimidine fragment, the main pharmacophore of the Abl kinase inhibitors imatinib and nilotinib used in the clinic for the CML treatment, ii) molecular docking of these compounds with the ATP-binding site of the native and mutant Abl kinase, iii) refinement of the ligand-binding poses by the quantum chemical method PM7, iv) molecular dynamics simulations of the ligand/Abl complexes, and v) prediction of the ligand/Abl binding affinity in terms of scoring functions of molecular docking, machine learning, quantum chemistry, and molecular dynamics. As a result, five top-ranking compounds able to effectively block the enzyme catalytic site were identified. According to the data obtained, these compounds exhibit close modes of binding to the Abl kinase active site that are mainly provided by hydrogen bonds and multiple van der Waals contacts. The identified compounds show high binding affinity to the native and mutant Abl kinase comparable with the one calculated for the FDA-approved kinase-targeted inhibitors imatinib, nilotinib, and ponatinib used in the calculations as a positive control. The results obtained testify to the predicted drug candidates against CML may serve as good scaffolds for the design of novel anticancer agents able to target the ATP-binding pocket of the native and mutant Abl kinase.Communicated by Ramaswamy H. Sarma.

Computational discovery of small drug-like compounds as potential inhibitors of SARS-CoV-2 main protease.

A computational approach to in silico drug discovery was carried out to identify small drug-like compounds able to show structural and functional mimicry of the high affinity ligand X77, potent non-covalent inhibitor of SARS-COV-2 main protease (MPro). In doing so, the X77-mimetic candidates were predicted based on the crystal X77-MPro structure by a public web-oriented virtual screening platform Pharmit. Models of these candidates bound to SARS-COV-2 MPro were generated by molecular docking, quantum chemical calculations and molecular dynamics simulations. At the final point, analysis of the interaction modes of the identified compounds with MPro and prediction of their binding affinity were carried out. Calculation revealed 5 top-ranking compounds that exhibited a high affinity to the active site of SARS-CoV-2 MPro. Insights into the ligand - MPro models indicate that all identified compounds may effectively block the binding pocket of SARS-CoV-2 MPro, in line with the low values ​​of binding free energy and dissociation constant. Mechanism of binding of these compounds to MPro is mainly provided by van der Waals interactions with the functionally important residues of the enzyme, such as His-41, Met-49, Cys-145, Met-165, and Gln-189 that play a role of the binding hot spots assisting the predicted molecules to effectively interact with the MPro active site. The data obtained show that the identified X77-mimetic candidates may serve as good scaffolds for the design of novel antiviral agents able to target the active site of SARS-CoV-2 MPro.Communicated by Ramaswamy H. Sarma.