Drug repurposing—the strategy of finding new therapeutic uses for existing drugs—has emerged as a pragmatic complement to traditional drug discovery. Advances in computational modeling and artificial intelligence are transforming this field from serendipitous observation into systematic discovery. Physicochemical simulations provide atomistic insights into drug–target interactions, while similarity-based methods leverage chemoinformatics, omics signatures, and network medicine to infer mechanistic analogies across diseases. In parallel, learning-based models leverage deep learning to predict or even generate new therapeutic hypotheses. Together, these paradigms define a continuum from mechanistic to data-driven inference, enabling scalable, interpretable, and increasingly precise drug repositioning. The convergence of physics-grounded simulation, network-level reasoning, and foundation-model intelligence heralds a new era of computational pharmacology, accelerating the identification of safe, effective, and affordable treatments.
Xu et al. (Thu,) studied this question.