Deep Learning for Anticancer Drug Discovery Targeting Non-Apoptotic Regulated Cell Death Mechanisms

Targeting non-apoptotic regulated cell death (RCD) modalities, such as ferroptosis and cuproptosis, offers a new avenue for overcoming resistance to conventional antitumor therapies, while deep learning provides a powerful tool for discovering bioactive molecules from multi-source data. This review delineates the core methodologies and application advances of deep learning in this domain, covering end-to-end molecular representations, multimodal fusion strategies, dataset partitioning criteria, and deep learning frameworks, thereby establishing a preliminary technical framework tailored to the study of non-apoptotic RCD mechanisms. Subsequently, the applications of deep learning in non-apoptotic RCD are discussed along three dimensions: direct applications, adjacent applications, and speculative outlooks. Future directions should focus on constructing high-quality annotated databases capable of distinguishing multiple cell death modalities and establishing standardized blind test benchmarks, developing explainable AI methods, designing mechanism-oriented few-shot learning algorithms, and building dynamic context-aware models. Advances along these directions may help propel the application of deep learning in drug discovery targeting non-apoptotic RCD mechanisms, from computational prediction toward experimental validation and translational research.