CADD and AI technologies are reshaping the landscape of modern drug discovery and development. A recent review article addresses the fundamental challenges of traditional drug discovery, such as long development cycles, high costs, and high failure rates, and explains how CADD has evolved from a supporting tool into a core driver. It also focuses on how the introduction of AI has propelled this process to new heights. The article highlights the significant advantages of integrating CADD and AI in drug discovery, including improved screening efficiency, reduced R&D costs, and shorter development cycles. It also outlines a new model for drug discovery that integrates computational prediction, automated experimentation, and AI optimization.
This review systematically describes the profound structural transformation of the field of drug discovery, driven by breakthroughs in computational technology. CADD has become an indispensable core tool in both academia and industry, significantly improving R&D efficiency and resource utilization. Increasing computational power enables the exploration of vast chemical spaces, the construction of massive compound libraries, and the efficient prediction of molecular physicochemical properties and biological activity. Meanwhile, AI has permeated every stage of drug R&D. As a cutting-edge branch of CADD, AIDD has accelerated critical processes such as target identification, candidate molecule screening, and pharmacodynamic evaluation. This significantly shortens the R&D cycle and effectively reduces investment and risk. However, translating computational predictions into experimental validation remains challenging, and CADD development faces numerous obstacles. As AI methods continue to evolve and cross-disciplinary technologies mature, CADD is poised to lead drug discovery into a new era of intelligence and precision.
The work titled “Convergence of Computer-Aided Drug Discovery and Artificial Intelligence: Towards Next-Generation Therapeutics” was published on Pharmaceutical Science Advances (published on November 14, 2025).
DOI：10.1016/j.pscia.2025.100100