Food Vector v1.0: Quantum-Enhanced Molecular Screening Platform for Protein-Targeted Compound Discovery

Food Vector v1.0 is a computational platform that screens food-derived bioactive compounds against therapeutic protein targets using quantum-mechanical molecular representations. The platform integrates four computational stages: (1) data ingestion from FooDB, PubChem, and HMDB to assemble a reference library of 10,000+ food-derived molecules; (2) Hamiltonian DIFF vector generation, in which each compound and target-protein pocket is reduced to per-element spectral fragments derived from a residue-localized Hamiltonian; (3) sliding-window cosine matching between compound and target vectors, with sharpness (curve_sharp) and size-ratio penalties to reject broad or trivially small alignments; (4) AutoDock Vina docking with quantum-enhanced scoring on AWS Braket to confirm top-ranked candidates.

The platform was validated preclinically against the P23H rhodopsin mutation associated with autosomal dominant retinitis pigmentosa. Validation results showed target-specific score discrimination versus decoy protein controls, decoy-shift statistical significance for top candidates, and wet-lab confirmation of lead-compound chaperone activity in cell-based conformational rescue assays.

The underlying methodology is described in two companion US Provisional Patent Applications: 63/915,602 (System and Method for Optimized Hybrid Quantum-Classical Processing Using Multi-Threaded Quantum Workflows, filed November 11, 2025) and 63/928,694 (System and Method for Quantum/AI-ML Based Screening and Identification of Food or Drug Agents for Reversal of Protein Mutations and Prevention of Disease-Associated Protein States, filed December 1, 2025).

Key technical specifications:
- Compound library: 10,000+ molecules indexed from FooDB, PubChem, HMDB
- Vector representation: Hamiltonian DIFF, element-projected spectral fragments
- Quantum compute layer: AWS Braket
- Docking engine: AutoDock Vina with quantum-enhanced scoring

Suggested citation: Gupta R, Sahasrabuddhe R. Food Vector v1.0: Quantum-Enhanced Molecular Screening Platform for Protein-Targeted Compound Discovery. Zero State Inc. 2026. Zenodo. https://doi.org/10.5281/zenodo.19834686