Synthetically Feasible De Novo Molecular Design of Leads Based on a Reinforcement Learning Model: AI-Assisted Discovery of an Anti-IBD Lead Targeting CXCR4
Xiaoying Jiang, Liuxin Lu, Junjie Li, Jing Jiang, Jiapeng Zhang, Shengbin Zhou, Hao Wen, Hong Cai, Xinyu Luo, Zhen Li, Jiahui Wang, Bin Ju, Renren Bai
Abstract
Artificial intelligence (AI) de novo molecular generation provides leads with novel structures for drug discovery. However, the target affinity and synthesizability of the generated molecules present critical challenges for the successful application of AI technology. Therefore, we developed an advanced reinforcement learning model to bridge the gap between the theory of de novo molecular generation and the practical aspects of drug discovery. This model utilizes chemical reaction templates and commercially available building blocks as a starting point and employs forward reaction prediction to generate molecules, while real-time docking and drug-likeness predictions are conducted to ensure synthesizability and drug-likeness. We applied this model to design active molecules targeting the inflammation-related receptor CXCR4 and successfully prepared them according to the AI-proposed synthetic routes. Several molecules exhibited potent anti-CXCR4 and anti-inflammatory activity in subsequent in vitro and in vivo assays. The top-performing compound XVI alleviated symptoms related to inflammatory bowel disease and showed reasonable pharmacokinetic properties.