让分子模型学会识别自己不懂的分子，避免错误预测。
Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimization
- 通过配对学习优化模型对未知分子的判断偏好。
- 在三种分布偏移下，AUROC最高提升45.8%。
- 可无缝接入现有模型，适合药物与蛋白设计场景。
分子基础模型在科学发现中快速进步，但在分布外（OOD）样本上的不可靠性严重限制其在药物发现和蛋白质设计等高风险领域的应用。关键缺陷是化学幻觉——模型对未知分子做出高置信度但完全错误的预测。为此，我们提出Mole-PAIR，一种简单、即插即用的模块，可通过低成本后训练提升模型在OOD数据上的可靠性。该方法将OOD检测建模为对内分布（ID）与外分布（OOD）样本间相似度估计的偏好优化，采用成对学习目标实现。实验表明，该目标本质优化了衡量排序一致性的AUROC。在五个真实分子数据集上验证，本方法显著提升现有分子基础模型的OOD检测能力，在分子大小、骨架和检测任务分布偏移下，AUROC分别提升45.8%、43.9%和24.3%。
原文摘要 · Abstract (English)
Molecular foundation models are rapidly advancing scientific discovery, but their unreliability on out-of-distribution (OOD) samples severely limits their application in high-stakes domains such as drug discovery and protein design. A critical failure mode is chemical hallucination, where models make high-confidence yet entirely incorrect predictions for unknown molecules. To address this challenge, we introduce Molecular Preference-Aligned Instance Ranking (Mole-PAIR), a simple, plug-and-play module that can be flexibly integrated with existing foundation models to improve their reliability on OOD data through cost-effective post-training. Specifically, our method formulates the OOD detection problem as a preference optimization over the estimated OOD affinity between in-distribution (ID) and OOD samples, achieving this goal through a pairwise learning objective. We show that this objective essentially optimizes AUROC, which measures how consistently ID and OOD samples are ranked by the model. Extensive experiments across five real-world molecular datasets demonstrate that our approach significantly improves the OOD detection capabilities of existing molecular foundation models, achieving up to 45.8%, 43.9%, and 24.3% improvements in AUROC under distribution shifts of size, scaffold, and assay, respectively.
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