In the last decade, most drug development research has concentrated on identifying compounds that operate on a singular target ( ). Nevertheless, the selective affinity for a specific target consistently results in the instability of the cellular metabolic system, significantly impacting the regular physiological processes of cells and subsequently giving rise to detrimental effects. The selective affinity development of single-target medicines is subject to significant limitations ( ). Conversely, certain intricate illnesses, including drug-induced liver damage (DILI), are controlled by numerous targets and frequently pose challenges for treatment with a medicine that targets only one of them ( ). Therefore, the necessity of multi-target medications in treating complicated disorders has been established ( ). For example, Lim et al. developed a model to design multi-target drug prediction ([67]) (Figure ). (A) Illustration of a one-drug-multi-target-multi-indication strategy aimed at identifying drugs that enhance therapeutic effects while mitigating side effects, leveraging drug-off-target interactions. (B) Schema of 3D-REMAP, a robust multi-target screening platform integrating structural and chemical genomics data. The framework combines bioinformatics, chemoinformatics, protein-ligand docking, and machine-learning tools. Matrices R and Q represent observed and predicted protein-chemical interactions, respectively, used as inputs for the weighted imputed neighborhood-regularized One-Class Collaborative Filtering (winOCCF) algorithm to predict genome-wide drug-target interactions. Detailed methodologies are described in the Methods section ( ).