A Hypothesis-Gated Computational Framework for Precision Oncology: Targeted In Silico Perturbation, Protein Stability, and Microenvironment Stratification in Breast Cancer

Current generative artificial intelligence (AI) approaches in protein drug discovery exhibit high attrition rates during wet-lab transition. This failure stems primarily from an architectural defect: generating structural candidates in isolation without validating underlying biological mechanisms, target linkage, structural viability, and microenvironment delivery dynamics. Here, we present a Hypothesis-Gated Decision Framework that introduces Progressive Evidence Escalation to govern candidate advancement through four sequential quantitative checkpoints (Gates I–IV). Focusing on the oncogenic miR-615-3p / EPDR1 / p110α (PIK3CA) signaling axis in breast cancer, Gate I validated pathway reproducibility (100% directional concordance) and quantified target linkage sensitivity, prioritizing p110α kinase as the primary actionable driver (Sensitivity Index = 0.84). Gate II enforced rigorous structural confidence thresholding (pLDDT ≥ 85) across de novo candidates generated via RFdiffusion, ProteinMPNN, and AlphaFold, yielding a significantly superior structural profile (mean pLDDT = 91.5) compared to traditional unconstrained AI pipelines (mean pLDDT = 83.0). Gate III modeled compartmental transport and pH-responsive nanoparticle release dynamics, demonstrating sustained drug retention within the therapeutic window (40%–90% concentration) under tumor microenvironment (TME) acidosis (pH 6.5). Finally, Gate IV implemented an artificial neural network (ANN) achieving 94.2% classification accuracy across TME phenotypes (Immune-Hot, Immune-Cold, Immune-Excluded), with SHAP interpretability identifying miR-615-3p and EPDR1 as primary phenotypic determinants. By treating biological and physical uncertainty as explicit decision variables, our framework prevents capital waste on unviable candidates and provides a highly reproducible, interpretable computational paradigm for precision oncology.