High-Efficiency Discovery and Structure–Activity-Relationship Analysis of Nonsubstrate-Based Covalent Inhibitors of S‑Adenosylmethionine Decarboxylase

The resurgence of targeted covalent inhibitors (TCIs) in the past decade has resulted in several blockbuster covalent drugs. Various computational methods have been developed for TCI discovery, but predicting TCI reactivity remains challenging due to interferences between noncovalent scaffolds and reactive warheads, leading to low screening efficiency and high experimental costs. Here, we improved our SCARdock protocol by incorporating quantum chemistry-based warhead reactivity calculation. Integrating this calculation with noncovalent docking scores, ranks, and bonding-atom distances, noncovalent and covalent inhibitors of S-adenosylmethionine decarboxylase (AdoMetDC) were correctly classified. Then we successfully identified 12 new AdoMetDC covalent inhibitors, achieving a 70% hit ratio. Finally, we analyzed the contributions of noncovalent interactions and covalent bonding and performed a structure–activity relationship (SAR) analysis. This work presents an efficient protocol for TCI discovery and offers new insights into AdoMetDC inhibitor design. This protocol will stimulate TCI development by improving computational screening efficiency and reducing experimental costs.