AttriLens-Mol：基于属性引导的强化学习用于利用大语言模型进行分子属性预测
AttriLens-Mol: Attribute Guided Reinforcement Learning for Molecular Property Prediction with Large Language Models
AI总结：
AttriLens-Mol通过属性引导的强化学习框架提升大语言模型在分子属性预测中的性能，有效激发相关属性，提高可解释性和预测效果。
摘要：
大语言模型（LLMs）在协助分子属性预测任务中显示出潜力，但通常依赖于人工编写的提示和推理链模板。尽管最近的先进大推理模型如DeepSeek-R1采用强化学习来扩展“推理”过程，但其推理过程可能冗长且缺乏相关性。我们介绍了AttriLens-Mol，这是一种用于利用大语言模型进行分子属性预测的属性引导强化学习框架。AttriLens-Mol通过使用：（1）一种鼓励基于属性的结构化输出的格式奖励，（2）一种避免枚举无关属性的计数奖励，以及（3）一种利用先进LLM和RDKit验证生成属性之间相关性的理性奖励，来引导模型的推理过程。这种方法隐式地激发了模型在推理过程中对相关分子属性的内在知识，使模型能够更有效地进行分子属性预测。在分布内和分布外数据集上的实验表明，使用我们提出的方法在4000个样本上训练7B大小的R1-Distilled-Qwen2.5和R1-Distilled-LLaMA3.1模型，显著提升了性能，获得了与监督微调模型（Mol-Instructions、ChemDFM等）以及先进模型（GPT-3.5、GPT-4o、DeepSeek-V3、DeepSeek-R1等）相当或更好的结果。此外，我们提取的目标属性，当用作可解释决策树模型的特征时，比通过提示LLM生成的属性表现更优。这表明AttriLens-Mol能够有效激发更多相关且预测性强的分子属性，从而提高属性预测的可解释性和性能。我们发布了代码在https://github.com/szu-tera/AttriLens-Mol。
英文摘要：
Large Language Models (LLMs) have shown promise in assisting molecular property prediction tasks but often rely on human-crafted prompts and chain-of-thought templates. While recent advanced large reasoning models like DeepSeek-R1 employ reinforcement learning for an extended ``thinking'' process, their reasoning can be verbose and lack relevance. We introduce AttriLens-Mol, an attribute-guided reinforcement learning framework for molecular property prediction with LLMs. AttriLens-Mol steers the model's reasoning by using: (1) a format reward encouraging attribute-based structured output, (2) a count reward to avoid enumerating irrelevant attributes, and (3) a rationality reward using advanced LLMs and RDKit to verify the relatedness of the generated attributes. This approach implicitly elicits the model's inherent knowledge of relevant molecular attributes during reasoning, enables making predictions for the molecular property more effectively. Experiments on both in-distribution and out-of-distribution datasets show that, training both 7B-size R1-Distilled-Qwen2.5 and R1-Distilled-LLaMA3.1 models on 4,000 samples with our proposed AttriLens-Mol method significantly boosts the performance, getting comparable or better results than supervised fine-tuning models (Mol-Instructions, ChemDFM, etc.) and advanced models (GPT-3.5, GPT-4o, DeepSeek-V3, DeepSeek-R1, etc.). Further, our extracted attributes for the target property, when used as features for an interpretable decision tree model, yield superior performance compared to attributes generated by prompting LLMs. This shows that AttriLens-Mol effectively elicits more relevant and predictive molecular attributes, leading to enhanced interpretability and performance for property prediction. We release the code in https://github.com/szu-tera/AttriLens-Mol.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
