Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical...

The development of novel cancer therapeutics is a protracted, costly endeavor with high attrition rates, largely attributed to tumor heterogeneity and acquired resistance. Artificial intelligence (AI) is emerging as a powerful technology to enhance the drug discovery pipeline, employing multimodal datasets to identify therapeutic targets, design de novo candidates, and discover biomarkers. However, a significant validation gap persists. AI models frequently hallucinate chemically implausible molecules, overfit to biased training datasets (particularly immortalized cell lines that poorly represent patient tumors), and generate predictions that perform poorly outside their training distribution. This gap exists because AI development has prioritized algorithmic sophistication over experimental rigor, creating an accumulation of in silico predictions without systematic biological testing. Analysis of landmark studies reveals that AI-driven target discovery is most successful when constrained by synthetic accessibility filters and functional genomic screening, while dose optimization and combination therapy predictions require validation in patient-derived xenografts (PDXs) that recapitulate tumor microenvironment complexity. The most clinically impactful AI applications in oncology, from immunotherapy biomarker discovery to resistance mechanism prediction, tend to employ closed-loop discovery frameworks in which experimental outcomes iteratively retrain computational models. We propose that the translational potential of AI in oncology is not solely defined by algorithmic complexity, but substantially shaped by the rigor of the experimental feedback loops that constrain and refine it, thereby accelerating the delivery of more effective, personalized therapies validated through the complete hierarchy of in vitro assays, in vivo PDX models, and prospective clinical trials to patients.