An integrated deep learning model accelerates luciferase based high throughput drug screening
Xiaonan Zhang, Xinxin Zhang, Shuang Wang, Qiaoling Song, Hang Xu, Minghui Zhang, Xudong Zhang, Hao Xie, Jing Xu, Ying Zhang, Jiayi Yin, Qingyu Tian, Xiaochun Liu, Yue Zhong, Wei He, Changming Dong, Mingming Zhou, Wenting Wang, Xiaohan Xu, Lewei Wang · European Journal of Pharmaceutical Sciences · 2025
High-throughput screening presents clear advantages in accelerating drug development efficiency, but also faces challenges such as high costs, time-consuming processes, and labor-intensive procedures. To address these issues, we developed an integrated deep learning model to find patterns between the structural and molecular characteristics of compounds and our well-established luciferase based HTS values. We utilized about 100,000 HTS values from 18,840 compounds in five luciferase assays including STAT&NFκB system, PPAR system, P53 system, WNT system, and HIF system. Following AI-prediction for putative targeted hit compounds from 8713 compounds, the in vitro and in vivo experimental validation was performed, and drug candidates (inhibitors or activators) with anti-inflammatory, anti-tumor or anti-metabolic syndrome were identified. T4230 exerts its anti-inflammatory effects by inhibiting the expression of inflammatory factors. The classification performance of the compounds after the joint screening exceeded the performance of the respective sub-models when screened independently and the screening accuracy and efficiency improved 7.08 to 32.04-fold across these five systems compared to our conventional HTS. The integrated AI-conducted HTS model we have developed could reduce R&D costs and accelerate the drug development process, making it a valuable referential pipeline for the artificial intelligence accelerated specific signaling pathway-luciferase HTS.