Luoshu-Markov Geometric Framework: Fiedler Eigenvalue (lambda_2) for Protein Druggability, Allosteric Site Discovery, and Vina Discrepancy Correction (n=123) — v2 Clean

v2 update: All Unicode symbols corrected (lambda_2, C-alpha, Angstrom, summation). Full content identical to v1.

We introduce the Markov-Fiedler spectral complement to the Lo-Shu druggability framework. For n=123 DUD-E proteins (87 druggable, 36 undruggable), the Fiedler eigenvalue lambda_2 of the residue-contact graph Laplacian significantly distinguishes druggable from undruggable proteins (drug=0.0570 vs undrug=0.0896, Mann-Whitney p=0.0028). Combined model [CHC + Lo-Shu + LTE + lambda_2 + pocket_flex] achieves 5-fold CV AUC=0.8393. Lo-Shu Markov allosteric score recovers 5 validated allosteric proteins (cMyc-Max, CaM, HIF-1alpha, XIAP, PTPB) in top-10 from C-alpha geometry alone. CDK2 Vina discrepancy analysis explains BMS387032 gap (+3.13 kcal/mol) via entropic N/C-lobe boundary binding.