Yu Dong, Xianglu Xiao, Xu‐Xu Zhuang, Wenfan Wu, Zi-Ying Wang, Shuang Zhang, Jin-Tao Li, Ke Zhang, Wen-Yu Fu, Junming Chen, Shi Hang Xiong, Shenglong Deng, Krinos Li, CHAO MA, Wangzhen Jin, Xurui Jin, Qiwei Cai, Han-Ming Shen, Min Li, Huanxing Su, Jian‐Bo Wan, Hua Yu, Defang Ouyang, Keqiang Ye, Evandro F. Fang, Chris Soon Heng Tan, Guang Yang, Zhangming Niu, Jiahong Lu
Dysfunctional autophagy, a key cellular cleaning process, is a key driver of brain ageing and neurodegenerative diseases such as Alzheimer's disease (AD). However, developing effective treatments by enhancing autophagy has been challenging, as most known compounds act through the broad mTOR pathway, risking side effects, and few can effectively penetrate the brain. To address this, we developed DeepDrugDiscovery-a mechanism-aware, AI-powered screening platform incorporating ADMET and blood-brain barrier penetrability predictions. Here we show that this platform successfully identified novel, mTOR-independent autophagy enhancers, with two lead compounds demonstrating an ability to cross the blood-brain barrier, clear AD-related protein aggregates and restore memory function in worm and mouse AD models. By releasing DeepDrugDiscovery as an open-source, modular tool, we offer a user-friendly AI platform that enables customized therapeutic screening. Our work establishes a scalable, AI-driven pipeline that integrates cross-species validation to rapidly discover mechanism-based therapeutics for diseases with high unmet medical need.