Accelerating the Hit-To-Lead Optimization of a SARS-CoV-2 Mpro Inhibitor Series by Combining High-Throughput Medicinal Chemistry and Computational Simulations.

In this study, we performed the hit-to-lead optimization of a SARS-CoV-2 Mpro diazepane hit (identified by computational methods in a previous work) by combining computational simulations with high-throughput medicinal chemistry (HTMC). Leveraging the 3D structural information of Mpro, we refined the original hit by targeting the S1 and S2 binding pockets of the protein. Additionally, we identified a novel exit vector pointing toward the S1' pocket, which significantly enhanced the binding affinity. This strategy enabled us to transform, rapidly with a limited number of compounds synthesized, a 14 μM hit into a potent 16 nM lead compound, for which key pharmacological properties were subsequently evaluated. This result demonstrated that combining computational technologies such as machine learning, molecular docking, and molecular dynamics simulation with HTMC can efficiently accelerate hit identification and subsequent lead generation.