Bio-Cryptography: Dual Deep Learning Framework for Protein Watermarking via Geometric-Chemical Fingerprinting

The rapid advancement of generative AI in synthetic biology poses significant challenges to intellectual property (IP) protection for functional biomolecules. Traditional authentication methods are often ineffective against AI-generated proteins and can compromise biological activity. To address this, we propose DB-Crypt, a dual-stream bio-cryptography framework that integrates deep learning-driven geometric topology analysis with biological binding specificity. The framework consists of a "rigid authentication layer" that uses a differentiable geometric deep learning module (dMaSIF) to generate a unique, noise-resistant molecular fingerprint, and a "flexible steganography layer" that embeds authentication information within the protein-ligand interface with minimal functional perturbation. These cryptographic elements are immutably recorded on a blockchain, creating a verifiable and non-repudiable identity for synthetic biological products. Our experiments demonstrate that DB-Crypt can effectively distinguish between diverse natural and artificial proteins, including highly homologous antibody isoforms, with zero hash collisions, providing a robust solution for biomolecular IP management.