Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.

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 a 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 potentially active 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 activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (Nsp12 domain), and (alpha) spike protein S. Overall, 27 potential inhibitors were experimentally confirmed by binding-, cleavage-, and/or viral suppression assays and are presented here. All results are freely available and can be taken further downstream without IP restrictions. Overall, we show the effectiveness of computational techniques, community efforts, and communication across research fields (i.e., protein expression and crystallography, in silico modeling, synthesis and biological assays) to accelerate the early phases of drug discovery.

A virtual screening of the molecular library of biologically active compounds was carried out to identify potential inhibitors of SARS-CoV-2 main protease (Mpro) which plays an important role in the process of virus replication. Using molecular docking and molecular dynamics, the binding energy of these compounds to the catalytic site of the enzyme was assessed, resulting in six molecules that exhibited high chemical affinity for SARS-CoV-2 Mpro. This is evidenced by the low values of the binding free energy of the ligand/Mpro complexes comparable with those predicted for the potent non-covalent SARS-CoV-2 Mpro inhibitor using the identical computational protocol. Based on the data obtained, it was concluded that the identified compounds have a good therapeutic potential for inhibiting the catalytic activity of the enzyme and form promising basic structures for the development of new effective drugs against COVID-19.

De novo design of 95 775 potential ligands of SARS-CoV-2 main protease (Mpro), playing an important role in the process of virus replication, was carried out using a deep learning generative neural network that was developed previously based on artificial intelligence technologies. Molecular docking and molecular dynamics methods were used to evaluate the binding affinity of these molecules to the catalytic site of the enzyme. As a result, 7 leading compounds exhibiting Gibbs free energy low values comparable with the values obtained using an identical computational protocol for two potent non-covalent SARS-CoV-2 Mpro inhibitors used in calculations as a positive control were selected. The results obtained indicate the promise of applying identified compounds for development of new antiviral drugs able to inhibit the catalytic activity of SARSCoV-2 Mpro.