BioChk: A Regulation-Aligned Quality Assurance Framework for AI-Generated Biologics

AI-driven de novo biologics design has outpaced standardized in silico validation and regulatory readiness. We introduce BioChk, a modular quality-assurance framework that unifies structural feasibility, stability, binding affinity, homology assessment, and immunogenicity screening into a single pipeline. Each module employs configurable prediction tools and evaluates outputs against empirically calibrated thresholds derived from 1,000 EvoDiff-Seq-OADM generated peptides. The structural feasibility module was further refined by analyzing ten wellcharacterized human proteins (five drug targets and five nontargets) using Neurosnap hosted AlphaFold2 and ESMFold, establishing a conservative pLDDT cutoff of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\geq 70$</tex>. BioChk's architecture supports audit ready reports that map each pass/fail decision to FDA Good Machine Learning Practices and cGMP guidelines, with human-in-the-loop review for borderline cases. By combining automated screening, data-driven thresholds, and complete regulatory traceability, BioChk accelerates the safe, compliant preclinical development of AI-generated biologics.