High-Efficiency Discovery and Structure–Activity-Relationship Analysis of Nonsubstrate-Based Covalent Inhibitors of <i>S</i>‑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.近十年来，靶向共价抑制剂（targeted covalent inhibitors, TCIs）再度兴起，催生了多款重磅级共价药物。目前已开发出多种用于TCI发现的计算方法，但由于非共价骨架与反应性弹头之间存在相互干扰，预测TCI反应性仍极具挑战，进而导致筛选效率偏低、实验成本高昂。本研究对SCARdock方案进行了改进，引入基于量子化学的弹头反应性计算模块。将该计算结果与非共价对接打分、排名及成键原子距离相结合后，可准确对S-腺苷甲硫氨酸脱羧酶（S-adenosylmethionine decarboxylase, AdoMetDC）的非共价与共价抑制剂进行分类。随后，我们成功筛选得到12种新型AdoMetDC共价抑制剂，命中率高达70%。最后，我们分析了非共价相互作用与共价键合的贡献，并开展了构效关系（structure–activity relationship, SAR）分析。本研究提出了一种高效的TCI发现方案，为AdoMetDC抑制剂的设计提供了全新视角。该方案可通过提升计算筛选效率、降低实验成本，推动TCI领域的研发进程。