Synthelite: Chemist-aligned and feasibility-aware synthesis planning with LLMs
This provides a tool for synthetic chemists to integrate expert insights through natural language, though it appears incremental as it builds on existing LLM capabilities for a specific domain.
The authors tackled the problem of computer-aided synthesis planning (CASP) lacking interaction with human experts by introducing Synthelite, a framework using large language models (LLMs) to propose retrosynthetic transformations, achieving up to 95% success rates in constrained synthesis tasks.
Computer-aided synthesis planning (CASP) has long been envisioned as a complementary tool for synthetic chemists. However, existing frameworks often lack mechanisms to allow interaction with human experts, limiting their ability to integrate chemists' insights. In this work, we introduce Synthelite, a synthesis planning framework that uses large language models (LLMs) to directly propose retrosynthetic transformations. Synthelite can generate end-to-end synthesis routes by harnessing the intrinsic chemical knowledge and reasoning capabilities of LLMs, while allowing expert intervention through natural language prompts. Our experiments demonstrate that Synthelite can flexibly adapt its planning trajectory to diverse user-specified constraints, achieving up to 95\% success rates in both strategy-constrained and starting-material-constrained synthesis tasks. Additionally, Synthelite exhibits the ability to account for chemical feasibility during route design. We envision Synthelite to be both a useful tool and a step toward a paradigm where LLMs are the central orchestrators of synthesis planning.