Artificial intelligence (AI) is profoundly transforming the paradigm of solid tumor drug development. By integrating multi-omics data, spatial transcriptomics, and advanced computational models, AI has significantly accelerated the discovery and validation of new targets, compressing the traditional ten-year research and development cycle to two to three years. Generative AI platforms have optimized small molecule inhibitors, biologics, and messenger RNA vaccines, achieving breakthroughs in overcoming tumor heterogeneity, improving efficacy, and predicting drug resistance. However, clinical translation still faces challenges such as data bias, algorithm transparency, and the validation gap between models and real-world human experience. This review aims to systematically elaborate on the transformative role of AI in solid tumor drug development and to promote interdisciplinary cooperation as well as the construction of ethical frameworks to enable the full realization of precision oncology.

Through cutting-edge methods and advanced artificial intelligence analysis, UNC Charlotte researchers leading a multidisciplinary team across four universities have successfully resolved the entire genome of “Phage G,” the largest bacterial virus (aka bacteriophages or phages) ever cultivated in a physical lab environment. 

Insilico used its generative biologics engine to quickly design over 5,000 novel peptides targeting GLP1R, a key receptor for diabetes drugs like Ozempic, without relying on existing binders. After selecting 20 top candidates based on predicted affinity, lab tests showed that 14 had biological activity and 3 achieved single-digit nanomolar potency—a high level of effectiveness usually reached only after several rounds of design.