Evolution of Structure Prediction AI and Applications to Cancer Genomic Medicine: From AlphaFold 3 and Cryptic Pocket Metadynamics to Alpha1H Conformational Fluidity Therapeutics and Deterministic QFS-zkVM Pan-Cancer Governance

The resolution of the 50-year protein folding problem by DeepMind's AlphaFold 2 (AF2) and its biomolecular-complex successor AlphaFold 3 (AF3) has transformed structural biology from descriptive visualization into the foundational infrastructure of precision cancer genomic medicine. Prior to AF2, experimental structures and template-based homology models covered only 48% of the human proteome, leaving an extensive "Dark Proteome." AlphaFold expanded structural coverage to 76%, compressed the uncharacterized dark fraction from 26% to 10%, and augmented structural coverage across ClinVar pathogenic variants (69% baseline) and oncogenic somatic variants (88% baseline) by an additional 3% to 13%. In this monograph, we synthesize the biophysical, computational, clinical, and institutional governance dimensions of AI-driven structural oncology. First, we formalize the geometric and thermodynamic sensitivity of AF2/AF3 and AlphaMissense to single amino acid substitutions via Effective Strain (ES) within a 13 Å Cα neighborhood and pLDDT-derived stability metrics (AUROC = 0.852 across 26 hereditary cancer genes and 160 novel missense variants in 355 breast cancer patients), alongside differentiable simulation, stochastic Multiple Sequence Alignment (MSA) subsampling, Slow Feature Analysis (SFA), and metadynamics for rare-event cryptic pocket sampling (e.g., RIPK2, LRRK2 G2019S, TNK1). Second, we examine conformational fluidity therapeutics exemplified by Alpha1H—a synthetic α-lactalbumin α1-domain peptide complexed with oleic acid inspired by HAMLET—which downregulates >700 oncogenic and metabolic genes (including RAS and GJA1/Connexin 43) and achieved an 80% tumor response rate with 59% mean tumor reduction in Phase I/II Non-Muscle Invasive Bladder Cancer (NMIBC) trials (NCT03560479) under FDA Fast Track designation. Third, we integrate pan-cancer multi-omics target discovery across FUNDC2, ZNF668 (Dasatinib repurposing), Alpha-1-antitrypsin, the ImmunoVerse T-cell antigen atlas (15,079 tumor-specific HLA antigens and 88 surface targets across 21 cancer types), and COSMIC 3D residue-contact networks (61,364 missense mutations across 30 tumor types) with AlphaProteo de novo binder design. Finally, to resolve the static-to-dynamic translational bottleneck in clinical precision oncology, we establish a deterministic verification architecture using RTO P2.0 and the QFS-zkVM over the 64-bit Goldilocks prime field F_p (p = 2^64 - 2^32 + 1) with Q-SAFA, PHOENIX_BRIDGE, and Human-in-Command (HIC) fiduciary gates under Delaware Caremark oversight. (構造予測AIの進化とがんゲノム医療への応用:AlphaFoldの衝撃からAlpha1H等の新規標的治療薬開発と汎がん的適応への包括的解析)