AI-assisted fragment-based drug discovery of SARS-CoV-2 macrodomain binders validated by NMR and X-ray crystallography

Fragment-based drug discovery (FBDD) is an effective approach for exploring chemical space using small, low-affinity fragments as starting points to facilitate development of lead compounds. Strategies to improve fragment potency include fragment merging and linking to generate higher-affinity inhibitors. Recently, artificial intelligence (AI) and machine learning (ML) have accelerated this process through structure-based optimization and generative compound design. Here, we present an AI-assisted FBDD workflow applied to the SARS-CoV-2 macrodomain (Mac1), a conserved viral protein involved in immune evasion and ADP-ribose metabolism. Using available structural data and previously identified fragments, we combined deep learning with molecular docking to design novel Mac1 binders. Selected compounds were synthesized and validated by NMR spectroscopy and X-ray crystallography, demonstrating improved binding relative to the original fragment hits with KD values in the range of 299-990 µM. This study demonstrates the advantages of integrating AI with FBDD to streamline molecular design, providing a data-driven framework for discovering new Mac1 inhibitors and guiding future antiviral drug development. Artificial intelligence and machine learning have recently accelerated fragment-based drug discovery (FBDD) processes through structure-based optimization and generative compound design. Here, the authors present an AI-assisted FBDD workflow applied to the SARS-CoV-2 macrodomain, designing and experimentally validating novel Mac1 binders.