Towards Literature-Grounded Agentic Co-Crystal Synthesis Design

Co-crystallization has become an important approach for modulating the physicochemical properties of drug molecules, yet experimental development remains challenging. While recent machine-learning methods have advanced co-crystal screening, the downstream problem of synthesis design remains largely unresolved. We present CoSyn, a literature-grounded LLM-based agentic framework for supporting co-crystal synthesis design that combines (i) hierarchical extraction of method-specific synthesis information, (ii) a large-scale machine-readable database of 20,783 co-crystal synthesis records across multiple methods, and (iii) method-specific starting-condition prediction for new molecular pairs. We systematically evaluate each component using manually annotated datasets and prospectively validate recommended conditions, achieving phase-pure co-crystals for three of four held-out pairs. At the system level, tool-augmented orchestration improves the average score on our targeted domain benchmark from 1.88 for an LLM without tool use to 3.58 on a 1–5 scale. These results demonstrate that structured, literature-derived experimental evidence can support co-crystal synthesis decision-making beyond pair-level screening.