Work

In silico tuning of binding selectivity for new SARS-CoV-2 main protease inhibitors

Year: 2025

Type: article

Abstract: Extracting quantitative structure-activity relationships (QSAR) using augmented intelligence in drug discovery has been a significant challenge in medicinal and chemical sciences. One of the primary bottlenecks in drug discovery is the robust computer-aided pre-screen for potent drug candidates based on existing high-performance inhibitors approved by the US Food and Drug Administration (FDA). Adrafinil (PDB Code 7ANS) and Baicalein (PDB Code 6M2N) are such inhibitors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protases (MPro). The process involves collaborations between researchers and machines, leveraging their respective strengths for optimal outcomes. Building on our previously development of conformational analysis program PerQMConf, we have enhanced our computational toolkit in this direction. PerQMConf samples potent conformers of an existing inhibitor without altering its composition. In addition to this, we have now developed a new computational method/program called MDBinding for design of lead compounds. MDBinding enables pre-screening of new inhibitors with better binding through functional group substitution of an FDA approved drug, using molecular dynamic (MD) simulations. In this study, we demonstrate the capability of MDBinding to identify new and promising SARS-CoV-2 inhibitor candidates by substituting functional groups of the established SARS-CoV-2 inhibitors Adrafinil and Baicalein. The new compounds were obtained through docking into the SARS-CoV-2 Mpro protein, followed by MD simulations to achieve better free binding energies than the unsubstituted reference ligand-Mpro complexes. Using the MDBinding program, we demonstrate that inhibitor pre-screen processes can be significantly and systematically improved the efficiency and cost with robust.

Source: Computer Methods and Programs in Biomedicine

DOI: 10.1016/j.cmpb.2025.108678

Authors: Feng Wang, Vladislav Vasilyev

Institutions: Swinburne University of Technology, Australian National University, National Computational Infrastructure

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,350