Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimization

• URL: http://arxiv.org/abs/2509.25509v1
• Date: Mon, 29 Sep 2025 21:06:52 GMT
• Title: Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimization
• Authors: Langzhou He, Junyou Zhu, Fangxin Wang, Junhua Liu, Haoyan Xu, Yue Zhao, Philip S. Yu, Qitian Wu,
• Abstract summary: We introduce Molecular-Aligned Preference Instance Ranking (Mole-PAIR), a plug-and-play module that can be flexibly integrated with existing foundation models.<n>We show that our approach significantly improves the OOD detection capabilities of existing molecular foundation models.
• Score: 54.22711328577149
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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