From Literature to Experimental Conditions: Large Language Models for Co-Crystal Synthesis Design

Co-crystallization enables modulation of drug physicochemical properties, yet practical synthesis design remains largely empirical. Existing AI methods mainly address molecular pair selection, leaving synthesis-condition design underexplored because the required experimental knowledge is scattered across scientific articles. We present CoSyn, a framework that uses large language models to convert dispersed procedure descriptions into machine-readable synthesis records, enabling experimental condition recommendations for new molecular pairs and tool-augmented assistance. Using manually annotated datasets, we evaluate both individual components and system-level performance. Our results highlight the potential of a modular LLM-based approach to organize literature-derived evidence and support co-crystal synthesis design beyond pair-level screening.