Cancer has become a major global health threat due to its high incidence and mortality. However, the development of anti-cancer drugs is limited by high costs, long cycles, and low success rates, slowing the progress of new treatments. As a method that simulates human cognitive functions, artificial intelligence (AI) has greatly improved the efficiency of drug development. Machine learning is a core part of AI and supports applications such as natural language processing and computer vision. This paper reviews recent advances in AI for optimizing anti-cancer drug discovery, development, and medication therapy management. First, we highlight the applications of AI in target identification, druggability assessment, drug screening, and repurposing. Second, we detail how AI optimizes drug combination therapy and clinical trial design. Finally, we describe the role of AI in treatment management, including nanoparticle delivery systems, personalized dosing, and adaptive therapy. AI greatly streamlines anti-cancer drug development and provides new directions for precision cancer therapy.

Plant-derived bioactive peptides have garnered widespread interest for their functions in managing obesity and associated metabolic disorders. This study investigated the lipid-lowering activity and underlying mechanisms of VPIIMH, a hexapeptide derived from Chlorella pyrenoids, using in vitro enzymatic assays, Caenorhabditis elegans models, and network pharmacology. In vitro, VPIIMH acted as a reversible non-competitive inhibitor of pancreatic lipase, achieving an inhibition rate of 43.17 ± 1.47% at 8.0 mg/mL. Molecular docking revealed that this inhibition likely occurs through ionic bonds between VPIIMH and PL (1LPB) at Arg256. In a high-fat C. elegans model, treatment with 0.5 mg/mL VPIIMH significantly reduced fat accumulation by 37.2% and triglyceride levels by 26.9%. Furthermore, VPIIMH extended the lifespan of C. elegans under oxidant stress by 40.3% and under heat stress by 17.5%. Network pharmacology predicted that VPIIMH targets nine core proteins, which were classified into three synergistic modules: the SIRT1-PPAR for core regulation, the RAS for systemic coordination, and the inflammatory target (CCR5, MMP9, EGFR) for microenvironment support. This study elucidates the multi-target and multi-pathway mechanism of VPIIMH, suggesting its potential application in combating obesity and related lipid metabolism disorders. These findings provide a scientific basis for the development of VPIIMH as a functional food ingredient targeting metabolic health.