发表机构
The Hong Kong University of Science and Technology (Guangzhou); Westlake University; University of Science and Technology of China（香港科技大学（广州）; 西湖大学; 中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有分子结构解析模型预测结果不合理的问题，提出化学反馈驱动的FIRMPO框架，在三类红外数据集上显著提升了解析准确性。
AI 中文摘要
红外（IR）光谱提供分子结构的特征信号，专家通常通过官能团识别或库匹配对其进行解读，该过程耗时且存在歧义。近期机器学习方法利用分子式和红外光谱在分子结构解析方面取得了进展，但这些模型常推断出不合理的候选分子结构，包括排名靠前的预测结果：具体而言，候选结构隐含的分子式常与输入分子式不匹配，且候选结构的理论红外光谱常与观测红外光谱不一致。为解决这些问题，我们提出了分子式与红外光谱匹配的偏好优化（Formula- and IR-Matched Preference Optimization，FIRMPO），这是一种通用且即插即用的、由化学反馈驱动的分子结构解析偏好优化框架。FIRMPO将基于精确分子式匹配和红外光谱一致性的化学反馈作为偏好信号，以指导合理的结构预测。与通用偏好优化方法不同，FIRMPO专为分子结构解析定制，同时保持模型无关性，使其可轻松与该类中的不同结构预测模型集成。这促使模型优先考虑满足化学反馈的结构，从而大幅提升排名靠前预测结果的准确性。在三个广泛使用的红外数据集上进行的大量实验表明，FIRMPO相较于现有基准方法显著提升了分子结构解析的准确性。
英文摘要
Infrared (IR) spectra provide characteristic signals of molecular structure, which are often interpreted by experts via functional-group identification or library matching, making the process time-consuming and ambiguous. Recent machine learning methods have made progress in molecular structure elucidation using molecular formulas and IR spectra. However, these models often infer unreasonable candidate molecular structures, including top-ranked predictions. More specifically, the molecular formula implied by a candidate structure often fails to match the input molecular formula, and the candidate's theoretical IR spectrum is often inconsistent with the observed IR spectrum. To address these issues, we propose Formula- and IR-Matched Preference Optimization (FIRMPO), a general and plug-and-play chemical feedback-driven preference optimization framework for molecular structure elucidation. FIRMPO incorporates chemical feedback as preference signals based on exact molecular formula matching and IR spectral consistency to guide reasonable structure predictions. Unlike generic preference optimization methods, FIRMPO is tailored to molecular structure elucidation while remaining model-agnostic, enabling it to be readily integrated with different structure prediction models in this class. This encourages models to prioritize structures that satisfy the chemical feedback, leading to a substantial improvement in the accuracy of top-ranked predictions. Extensive experiments on three widely used IR datasets show that FIRMPO significantly improves molecular structure elucidation accuracy over existing baselines.
