LLM-Augmented Chemical Synthesis and Design Decision Programs

Understand

Retrosynthesis, the process of breaking down a target molecule into simpler precursors through a series of valid reactions, stands at the core of organic chemistry and drug development.

• Although recent machine learning (ML) research has advanced single-step retrosynthetic modeling and subsequent route searches, these solutions remain restricted by the extensive combinatorial space of possible pathways.
• Concurrently, large language models (LLMs) have exhibited remarkable chemical knowledge, hinting at their potential to tackle complex decision-making tasks in chemistry.
• In this work, we explore whether LLMs can successfully navigate the highly constrained, multi-step retrosynthesis planning problem.