发表机构
Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出三个可测试标准评估分子模型的基础性，发现现有模型均未完全满足，进展取决于数据质量与先验知识而非规模，为领域发展提供评估框架。
AI 中文摘要
大规模模型已渗透到分子科学领域，然而在该领域中，什么使模型具有“基础性”仍缺乏明确定义。本文提出了三个可测试的标准来评估分子模型的基础性：（i）跨分子实体、性质和任务的通用性；（ii）在无需或极少任务特定重训练的情况下对新应用的迁移能力；（iii）超越训练分布的泛化能力。将这些标准应用于当前最先进的方法，显示出有前景的进展，特别是在生物分子结构预测和机器学习原子间势方面尤为明显，尽管所考察的方法中没有一个完全满足所有三个标准。成功集中在目标性质定义一致且训练数据丰富、标签噪声相对于物理上有意义的变异较低的领域。更广泛地看，分子科学的进展似乎较少依赖于模型规模本身，而更多依赖于可用数据的质量和结构，以及将先验知识纳入模型、预测任务或下游应用。这项工作将分子基础模型的概念从描述性标签转变为可测试的假设，为评估当前模型和指导未来发展提供了一个框架。
英文摘要
Large-scale models have permeated the molecular sciences, yet what makes a model 'foundational' in this domain remains poorly defined. This paper proposes three testable criteria for assessing the foundational nature of molecular models: (i) generality across molecular entities, properties, and tasks; (ii) transferability to new applications with no or minimal task-specific retraining; and (iii) generalization beyond the training distribution. Applying these criteria to the state of the art reveals promising progress, particularly visible in biomolecular structure prediction and machine-learned interatomic potentials, although none of the approaches examined fully satisfies all three. Success is concentrated in domains where target properties are consistently defined and training data are abundant, with low label noise relative to physically meaningful variation. More broadly, progress in the molecular sciences appears to depend less on model scale alone than on the quality and structure of available data, as well as the incorporation of prior knowledge into models, prediction tasks, or downstream applications. This work shifts the notion of a molecular foundation model from a descriptive label to a testable hypothesis, offering a framework for assessing current models and guiding future developments.
