ChemThinker: Thinking Like a Chemist with Multi-Agent LLMs for Deep Molecular Insights
Keywords: Molecular Property Prediction, Molecular Representation Learning, Multi-Agent LLMs
Abstract: Molecular property prediction is vital in drug discovery and cheminformatics, yet many current models lack interpretability, making it difficult for experts to understand the rationale behind predictions. To address this, we introduce ChemThinker, a novel large language models (LLMs) multi-agent framework designed to effectively control the internal representations of concepts and functions within LLMs. ChemThinker emulates the way chemists approach molecular analysis by integrating insights from three perspectives: general molecular properties, data-driven analysis, and task-specific factors. Each perspective uses an agentic approach to stimulate the LLM's internal representations, enabling more targeted and interpretable outputs based on the problem at hand, akin to how stimuli trigger the brain's cognitive processes. By feeding representations from these three perspectives into a simple multi-layer perceptron (MLP), ChemThinker achieves superior performance, significantly outperforming existing baselines across multiple benchmarks. Furthermore, our framework provides interpretable insights into the molecular mechanisms driving the predictions, making it a practical tool for drug discovery and other cheminformatics applications.
Supplementary Material: pdf
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 13199
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