Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development - Yuan‐Tao Liu; Le‐Le Zhang; Zi‐Ying Jiang; Xian‐Shu Tian; Peng‐Lin Li; Pei‐Huang Wu; Wen‐Ting Du; Bo‐Yu Yuan; Chu Xie; Guo‐Long Bu; Lan‐Yi Zhong; Yan‐Lin Yang; et al. (MedComm)
ABSTRACT Artificial intelligence (AI) is revolutionizing biotechnology by transforming the landscape of therapeutic development. Traditional drug discovery faces persistent challenges, including high attrition rates, billion‐dollar costs, and timelines exceeding a decade. Recent advances in AI—particularly generative models such as generative adversarial networks, variational autoencoders, and diffusion models—have introduced data‐driven, iterative workflows that dramatically accelerate and enhance pharmaceutical R&D. However, a comprehensive synthesis of how AI technologies reshape each key modality of drug discovery remains lacking. This review systematically examines AI‐enabled breakthroughs across four major therapeutic platforms: small‐molecule drug design, protein binder discovery, antibody engineering, and nanoparticle‐based delivery systems. It highlights AI's ability to achieve >75% hit validation in virtual screening, design protein binders with sub‐Ångström structural fidelity, enhancing antibody binding affinity to the picomolar range, and optimize nanoparticles to achieve over 85% functionalization efficiency. We further discuss the integration of high‐throughput experimentation, closed‐loop validation, and AI‐guided optimization in expanding the druggable proteome and enabling precision medicine. By consolidating cross‐domain advances, this review provides a roadmap for leveraging machine learning to overcome current biopharmaceutical bottlenecks and accelerate next‐generation therapeutic innovation.