Periodicals
Journal of Medicinal Chemistry
Authors
Huimin Cheng; Yuliang Wu; Xiaowen Niu; Yuhang Wu; Tian Zhou; Junjie Zou; Huobin Wang; Chunwang Peng; Zhiqiang Liu; Rui He; Lijie Peng; Yongpan Chen; Zhang Zhang * ; Mingjun Yang * ; Jian Ma *
Abstract
Traditional drug discovery faces challenges from fragmented data, tacit knowledge dependence, and inefficient design-test cycles. We present an integrated AI workflow combining multimodal data curation, encoded medicinal chemistry rules, and physics-based simulations to establish a closed-loop from patent analysis to candidate validation. Application across six patents for four targets successfully deciphered SAR and identified representative molecules. A retrospective case study on RET kinase inhibitors demonstrated the workflow’s efficiency: focusing on patent-derived SAR enabled rapid discovery of Cpd-31 (RET IC50 = 0.48 nM, 95.88% tumor inhibition at 10 mg/kg) with only three initial compounds synthesized. This data-centric approach streamlines early drug discovery by providing objective molecular design foundations, significantly reducing synthetic efforts and hypothesis-driven exploration.
