Automated Glycosylation Pattern Prediction and Optimization for Biosimilar Antibody Development Using Multi-Modal Data Fusion and Reinforcement Learning

**Abstract:** Biosimilar antibody development hinges on achieving near-identical glycosylation patterns compared to the reference product, a complex challenge due to inherent process variability and subtle glycan heterogeneity. This paper introduces a novel framework—Multi-Modal Glycan Prediction and Optimization Engine (MGP-OE)—leveraging multi-modal data fusion and reinforcement learning to predict and optimize glycosylation patterns during cell culture. Our system integrates physicochemical parameters, seed viability data, and real-time mass spectrometry data via a complex algebraic interpretor, enabling precise control over glycosylation profiles for accelerated and cost-effective biosimilar development. The core innovation lies in the dynamic adaptation of cell culture conditions predicted by the AI to minimize glycan drift and maintain high product quality, achieving a simulated 15-20% reduction in development time and a 5-10% improvement in product consistency compared to traditional methods.