Exploring cheminformatics tools to combat prostate cancer: diagnostics, therapeutics, and mechanistic studies

Despite recent progress, prostate cancer (PCa) remains one of the most prevalent forms of cancer in men worldwide, and a variety of challenges remain to prevent timely detection, biomarker specificity, and the development of treatment resistance due to tumor heterogeneity and changes in androgen receptor (AR) signaling. For PCa diagnosis, treatment, and mechanistic research, cheminformatics and bioinformatics tools like virtual screening, molecular docking, pharmacophore modeling, and QSAR modeling, computational drug repurposing, molecular dynamics simulation, network pharmacology, multi-omics profiling, and MRI/radiomics are viewed as an interconnected continuum, with AI/ML/DL as a common analytical layer. To distinguish between clinically useful diagnostic methods and discovery-stage computational methods, a comparative evaluation methodology is presented that assesses each method according to several criteria: clinical maturity, predictive value, validation requirements, and clinical translatability. In addition to MRI, the only clinically validated modality described, other virtual screening, docking, QSAR, and network pharmacology techniques are predominantly used for discovery and preclinical applications and must be further validated through experimental and prospective clinical studies before becoming translatable to clinical use. Although multi-omics and network pharmacology methods are used to gain mechanistic insights into resistance pathways, cheminformatics-based strategies have helped identify candidate compounds and repurpose drugs targeting AR- and CRPC-related targets. Aside from diagnostic classification, AI/ML/DL applications currently being developed involve multimodal integration, biomarker discovery, and patient stratification, yet to be adopted in clinical practice, there is a need for explainable AI and multi-cohort prospective validation. An integrated computational pathway from discovery to experimental and clinical validation is suggested to facilitate future translational efforts. These computational tools together show great promise for improving the precision of PCa management, but require proper, sequential experimental and clinical evaluation of their in-silico predictions.