Advances in AI-Empowered Small-Molecule Drug Design for Vaccine Development and Immunotherapy

Artificial intelligence (AI) is reshaping drug discovery by shifting molecular design from empirical high-throughput screening toward data- and knowledge-guided workflows. This review summarizes the applications of graph neural networks, variational autoencoders, generative adversarial networks, biology-guided deep learning, and information-theoretic predictive models in vaccine development and cancer immunotherapy. Key tasks include molecular representation, de novo generation, virtual screening, ADMET prediction, antigen structure optimization, small-molecule adjuvant discovery, tumor microenvironment characterization, and precision dosing. Representative applications involving TLR/STING-related adjuvants, lipid nanoparticle delivery systems, and immunotherapy-response prediction are discussed to illustrate the complementarity of these computational approaches. Although AI can accelerate early-stage candidate identification and expand searchable chemical space, its clinical impact remains constrained by heterogeneous datasets, limited labeled samples, insufficient interpretability, weak cross-population generalization, and incomplete regulatory standards. Future research should emphasize standardized multimodal data, mechanism-aware model design, prospective validation, privacy-preserving collaboration, and closed-loop integration of computation, experimentation, and clinical feedback.