Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen.

SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC50 values less than 12 µM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC50 of 1.4 µM. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors.

Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen

SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC50 values less than 12 μM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC50 of 1.4 μM. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors. Graphical Image 1

Discovery of 2-(furan-2-ylmethylene)hydrazine-1-carbothioamide derivatives as novel inhibitors of SARS-CoV-2 main protease.

The COVID-19 posed a serious threat to human life and health, and SARS-CoV-2 Mpro has been considered as an attractive drug target for the treatment of COVID-19. Herein, we report 2-(furan-2-ylmethylene)hydrazine-1-carbothioamide derivatives as novel inhibitors of SARS-CoV-2 Mpro developed by in-house library screening and biological evaluation. Similarity search led to the identification of compound F8-S43 with the enzymatic IC50 value of 10.76 µM. Further structure-based drug design and synthetic optimization uncovered compounds F8-B6 and F8-B22 as novel non-peptidomimetic inhibitors of Mpro with IC50 values of 1.57 µM and 1.55 µM, respectively. Moreover, enzymatic kinetic assay and mass spectrometry demonstrated that F8-B6 was a reversible covalent inhibitor of Mpro. Besides, F8-B6 showed low cytotoxicity with CC50 values of more than 100 µM in Vero and MDCK cells. Overall, these novel SARS-CoV-2 Mpro non-peptidomimetic inhibitors provide a useful starting point for further structural optimization.

Structure-based de novo drug design using 3D deep generative models.

Deep generative models are attracting much attention in the field of de novo molecule design. Compared to traditional methods, deep generative models can be trained in a fully data-driven way with little requirement for expert knowledge. Although many models have been developed to generate 1D and 2D molecular structures, 3D molecule generation is less explored, and the direct design of drug-like molecules inside target binding sites remains challenging. In this work, we introduce DeepLigBuilder, a novel deep learning-based method for de novo drug design that generates 3D molecular structures in the binding sites of target proteins. We first developed Ligand Neural Network (L-Net), a novel graph generative model for the end-to-end design of chemically and conformationally valid 3D molecules with high drug-likeness. Then, we combined L-Net with Monte Carlo tree search to perform structure-based de novo drug design tasks. In the case study of inhibitor design for the main protease of SARS-CoV-2, DeepLigBuilder suggested a list of drug-like compounds with novel chemical structures, high predicted affinity, and similar binding features to those of known inhibitors. The current version of L-Net was trained on drug-like compounds from ChEMBL, which could be easily extended to other molecular datasets with desired properties based on users' demands and applied in functional molecule generation. Merging deep generative models with atomic-level interaction evaluation, DeepLigBuilder provides a state-of-the-art model for structure-based de novo drug design and lead optimization.

A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2.

The COVID-19 pandemic calls for rapid development of effective treatments. Although various drug repurpose approaches have been used to screen the FDA-approved drugs and drug candidates in clinical phases against SARS-CoV-2, the coronavirus that causes this disease, no magic bullets have been found until now. In this study, we used directed message passing neural network to first build a broad-spectrum anti-beta-coronavirus compound prediction model, which gave satisfactory predictions on newly reported active compounds against SARS-CoV-2. Then, we applied transfer learning to fine-tune the model with the recently reported anti-SARS-CoV-2 compounds and derived a SARS-CoV-2 specific prediction model COVIDVS-3. We used COVIDVS-3 to screen a large compound library with 4.9 million drug-like molecules from ZINC15 database and recommended a list of potential anti-SARS-CoV-2 compounds for further experimental testing. As a proof-of-concept, we experimentally tested seven high-scored compounds that also demonstrated good binding strength in docking studies against the 3C-like protease of SARS-CoV-2 and found one novel compound that can inhibit the enzyme. Our model is highly efficient and can be used to screen large compound databases with millions or more compounds to accelerate the drug discovery process for the treatment of COVID-19.

Scutellaria baicalensis extract and baicalein inhibit replication of SARS-CoV-2 and its 3C-like protease in vitro.

COVID-19 has become a global pandemic and there is an urgent call for developing drugs against the virus (SARS-CoV-2). The 3C-like protease (3CLpro) of SARS-CoV-2 is a preferred target for broad spectrum anti-coronavirus drug discovery. We studied the anti-SARS-CoV-2 activity of S. baicalensis and its ingredients. We found that the ethanol extract of S. baicalensis and its major component, baicalein, inhibit SARS-CoV-2 3CLpro activity in vitro with IC50's of 8.52 µg/ml and 0.39 µM, respectively. Both of them inhibit the replication of SARS-CoV-2 in Vero cells with EC50's of 0.74 µg/ml and 2.9 µM, respectively. While baicalein is mainly active at the viral post-entry stage, the ethanol extract also inhibits viral entry. We further identified four baicalein analogues from other herbs that inhibit SARS-CoV-2 3CLpro activity at µM concentration. All the active compounds and the S. baicalensis extract also inhibit the SARS-CoV 3CLpro, demonstrating their potential as broad-spectrum anti-coronavirus drugs.

Potent inhibitors of SARS-CoV-2 3C-like protease derived from N-substituted isatin compounds.

SARS-CoV-2 3C-like protease is the main protease of SARS-CoV-2 and has been considered as one of the key targets for drug discovery against COVID-19. We identified several N-substituted isatin compounds as potent SARS-CoV-2 3C-like protease inhibitors. The three most potent compounds inhibit SARS-CoV-2 3C-like protease with IC50's of 45 nM, 47 nM and 53 nM, respectively. Our study indicates that N-substituted isatin compounds have the potential to be developed as broad-spectrum anti-coronavirus drugs.