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.