Automated Peptide Synthesis Route Optimization via Dynamic Bayesian Network-Guided Constraint Relaxation

**Abstract:** The synthesis of complex peptides is often hampered by low yields and inefficient reagent consumption due to intricate protection/deprotection strategies and branching reaction pathways. This paper introduces an automated route optimization framework leveraging Dynamic Bayesian Networks (DBNs) and constraint relaxation techniques to accelerate peptide synthesis development. Our system, PeptideRouteOptimizer (PRO), analyzes reaction datasets, predicts yield probabilities, and dynamically modifies synthesis routes in real-time to minimize reagent usage and maximize overall yield. Preliminary simulations demonstrate up to a 30% improvement in efficiency compared to conventional manual optimization, highlighting PRO's potential for revolutionizing peptide drug discovery and manufacturing.