结构偏移下的分子性质预测与表格基础模型

中文摘要 AI 辅助

预测与标记训练分子在结构上不同的化合物的分子性质，对于药物发现和材料设计至关重要。表格基础模型（TFMs）通过上下文学习提供了一种有前景的方法，但它们在结构偏移下的性能以及分子比较在此设置中的价值仍未得到充分探索。我们研究了分子性质预测中的结构泛化问题，并引入了MolPAIR（分子对增强的上下文细化），这是一个无需特定任务参数更新的框架，结合了分子级和分子对上下文。一个全局表格基础模型（TFM）首先根据标记的分子示例预测查询的性质。第二个冻结的TFM预测查询与标记参考分子之间预测误差的差异，利用这些比较来细化初始预测。在58个MoleculeACE和Polaris任务中，CheMeleon表示结合TabPFN-3已经在大多数任务上优于每个评估的基线。MOLPAIR在58个任务中的46个上进一步改进了该预测器，在四种分子表示和三种TFM骨干上均有所提升。这些结果表明，显式的分子比较可以增强表格上下文学习以实现结构泛化，同时保持分子编码器和预训练模型权重不变。代码和数据集可在该https URL获取。

英文摘要

Predicting molecular properties for compounds that differ structurally from labeled training molecules is important for drug discovery and materials design. Tabular foundation models (TFMs) offer a promising approach through in-context learning, but their performance under structural shifts and the value of molecular comparisons in this setting remain underexplored. We study structural generalization in molecular property prediction and introduce MolPAIR (Molecular Pair-Augmented In-context Refinement), a framework that combines molecule-level and molecular-pair contexts without task-specific parameter updates. A global tabular foundation model (TFM) first predicts a query's property from labeled molecular examples. A second frozen TFM predicts differences in prediction errors between the query and labeled reference molecules, using these comparisons to refine the initial prediction. Across 58 MoleculeACE and Polaris tasks, CheMeleon representations combined with TabPFN-3 already outperform each evaluated baseline on a majority of tasks. MOLPAIR further improves this predictor on 46 of 58 tasks, with gains across four molecular representations and three TFM backbones. These results show that explicit molecular comparisons can strengthen tabular in-context learning for structural generalization while keeping the molecular encoder and pretrained model weights fixed. The code and datasets are available at https://github.com/nums-ai/MolPAIR.

发表机构

• Nums AI

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