AI-powered medicinal chemistry and translational drug development
U Kaicheng, Sophia Meixuan Zhang, Ziyu Yu, Zechuan Zhang, Jianwei Zhang, Chang He, Anbang Liu, Rui Chen, Stella Wang, Lijie Yan, Shichao Ding, Lavonda Li, Zongxin Yang, Gao Xiao, Xushuai Zhang, Kaige Bao, Haohan Wang, Athanasios V. Vasilakos, Junhan Zhao, Siwei Chen · Chemical Society Reviews · 2026
predictions and experimentally validated drug candidates. While a small but growing number of AI-guided molecules have entered clinical development, systematic evidence on whether AI-driven approaches ultimately deliver better drugs or faster timelines than traditional methods is still accruing. We discuss emerging opportunities at the intersection of AI with automation, robotics, multimodal biology, protein structure prediction, and autonomous discovery. With rigorous validation, high-quality datasets, and appropriate regulatory frameworks, AI can become a dependable tool for discovering safer, more effective, and more personalized medicines.