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.

Derivation and reinterpretation of the Fermi-Amaldi functional.

The Fermi-Amaldi correction to the electrostatic self-repulsion of the particle density is usually regarded as a semi-classical exchange functional that happens to be exact only for one- and closed-shell two-electron systems. We show that this functional can be derived quantum-mechanically and is exact for any number of fermions or bosons of arbitrary spin as long as the particles occupy the same spatial orbital. The Fermi-Amaldi functional is also size-consistent for such systems, provided that the factor N in its expression is understood as an orbital occupation number rather than the total number of particles. These properties of the Fermi-Amaldi functional are ultimately related to the fact that it is a special case of the self-exchange energy formula. Implications of our findings are discussed.

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.

Metabolomic profiling of three Araucaria species, and their possible potential role against COVID-19.

The COVID-19 pandemic in Egypt is a part of the worldwide global crisis of coronavirus 2 (SARS-CoV-2). The contagious life-threatening condition causes acute respiratory syndrome. The present study aimed to assess the compounds identified by LC-MS of the methanolic leaves extracts from three conifers trees cultivated in Egypt (Araucaria bidwillii, Araucaria. cunninghamii and Araucaria heterophylla) via docking technique as potential inhibitor of COVID-19 virus on multiple targets; viral main protease (Mpro, 6LU7), non-structural protein-16 which is a methyl transferase (nsp16, 6W4H) and RNA dependent RNA polymerase (nsp12, 7BV2). Among the three targets, nsp16 was the best target recognized by the tested compounds as can be deduced from docking studies. Moreover, the methanolic extract of A. cunninghamii showed the highest radical-scavenging activity using (DPPH test) with 53.7 µg/mL comparable to ascorbic acid with IC50 = 46 µg/mL The anti-inflammatory potential carried using enzyme linked immunoassay showed the highest activity for A. cunninghamii and A. bidwillii followed by A. heterophylla with IC50 = 23.20 ± 1.17 µg/mL, 82.83 ± 3.21 µg/mL and 221.13 ± 6.7 µg/mL, respectively (Celecoxib was used as a standard drug with IC50 = 141.92 ± 4.52 µg/mL). Moreover, a molecular docking study was carried for the LC-MS annotated metabolites to validate their anti-inflammatory inhibitory effect using Celecoxib as a reference compound and showed a high docking score (-7.7 kcal/mol) for Octadecyl (E) P-coumarate and (-7.3 kcal/mol) for secoisolariciresinol rhamnoside.Communicated by Ramaswamy H. Sarma.

Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors.

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block CD4-binding site of the viral envelope protein gp120 was developed. To do this, the following studies were carried out: (i) an autoencoder architecture was constructed; (ii) a virtual compound library of potential anti-HIV-1 agents for training the neural network was formed by the concept of click chemistry allowing one to generate a large number of drug candidates by their assembly from small modular units; (iii) molecular docking of all compounds from this library with gp120 was made and calculations of the values of binding free energy were performed; (iv) molecular fingerprints of chemical compounds from the training dataset were generated; (v) training of the developed autoencoder was implemented followed by the validation of this neural network using more than 21 million molecules from the ZINC15 database. As a result, three small drug-like compounds that exhibited the high-affinity binding to gp120 were identified. According to the data from molecular docking, machine learning, quantum chemical calculations, and molecular dynamics simulations, these compounds show the low values of binding free energy in the complexes with gp120 similar to those calculated using the same computational protocols for the HIV-1 entry inhibitors NBD-11021 and NBD-14010, highly potent and broad anti-HIV-1 agents presenting a new generation of the viral CD4 antagonists. The identified CD4-mimetic candidates are suggested to present good scaffolds for the design of novel antiviral drugs inhibiting the early stages of HIV-1 infection.

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.