Computationally guided side chain optimization of nirmatrelvir for lead discovery against coronavirus main proteases.

To optimize Nirmatrelvir as an inhibitor of the MERS-CoV main protease (Mpro), we developed an automated RDKit-based iterative scaffold optimization framework that integrated fragment replacement, combinatorial library enumeration, and chemical feasibility filtering to generate 15,744 drug-like analogs through systematic substitutions at six positions on the Nirmatrelvir scaffold. Structural similarity to Nirmatrelvir was assessed using complementary 2D fingerprint and 3D shape-based analyses of the 15,953 designed analogs and a separate set of 13,090 molecular weight-matched ChEMBL compounds to examine the binding potency in both the modified and randomly selected compound cohorts. Based on the correlation between structural similarity and docking score of the modified molecules, a binding score (BS) approach was advanced to rapidly identify and rank promising lead compounds. Several top candidates exhibited improved binding characteristics compared to that of Nirmatrelvir. To assess the robustness of the BS approach, multiple computational methods were employed, including K Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s44345-026-00079-3.