Structure-Based Discovery of Thiosemicarbazones As SARS-CoV-2 Main Protease Inhibitors

Abstract

Aim: Discovery of novel SARS-CoV-2 main protease (Mpro) inhibitors using a structure-based drug discovery strategy. Materials & methods: Virtual screening employing covalent and noncovalent docking was performed to discover Mpro inhibitors, which were subsequently evaluated in biochemical and cellular assays. Results: 91 virtual hits were selected for biochemical assays, and four were confirmed as reversible inhibitors of SARS CoV-2 Mpro with IC50 values of 0.4–3 μM. They were also shown to inhibit SARS-CoV-1 Mpro and human cathepsin L. Molecular dynamics simulations indicated the stability of the Mpro inhibitor complexes and the interaction of ligands at the subsites. Conclusion: This approach led to the discovery of novel thiosemicarbazones as potent SARS-CoV-2 Mpro inhibitors.

Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus

“…Despite the true positive rate of MSSA and MRSA models being lower in comparison to their true negative rates, our virtual screening performed equivalent or even better than other virtual screening campaigns reported in the literature. 67,68 Furthermore, we analyzed the influence of the average value of probabilities on the confusion matrix metrics (true negative rate, accuracy, and balanced accuracy). As high as the cutoff is, the predictability of models (Supporting Figure S6) suggested that this parameter should be considered in future studies as a mandatory threshold for decision making.…”