Feedback to Reasoning: LLM-Assisted Molecular Optimization with Domain Feedback and Historical Reasoning

Wenhan Gao, Xiran Fan, Chin-Chia Michael Yeh, Jiarui Sun, Yuzhong Chen, Menghai Pan, Mahashweta Das, Yi Liu

Keywords: Molecular Optimization, LLM for Science

Abstract: The remarkable success of large language models (LLMs) across diverse fields demonstrates their transformative potential in science, with molecular optimization representing a promising frontier. Traditionally, molecular optimization involves iterative discussions with domain experts, who progressively refine molecules with feedback until the desired properties are achieved. This interactive and feedback-driven process aligns well with the inherent strengths of LLMs, positioning them as promising tools for this task. **As an experience-driven task, molecular optimization depends critically on the domain feedback and accumulation of historical knowledge. However, none of the existing methods fully leverages such feedback and historical knowledge; especially, the reasoning trace and chemical insights that have led to successful optimization.** In this work, we propose **F2R**: Feedback to Reasoning, a conversational molecular optimization pipeline that allows LLMs to dynamically accumulate and retrieve historical knowledge about prior actions, rationales, and feedback. Moreover, just like humans whose reasoning is not always correct or precise, LLMs can also produce imperfect reasoning traces; F2R is the first work to leverages detailed domain feedback to critically reflect on and improve this reasoning. In this way, LLMs can evolve from passive language processors to agentic experts that emulate human experts in learning both actions and reasoning from experience. F2R is also the first work that leverages historical optimization results and reasoning traces from historical feedback. Consequently, F2R shows remarkable performance.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 4917