模拟监督的基础模型用于超越实验数据覆盖范围的高效液相色谱保留时间预测
Simulation-Supervised Foundation Models for Retention Time Prediction in High-Performance Liquid Chromatography beyond Experimental Data Coverage
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中文总结 AI 辅助
本研究提出FUSE-RT模型，利用模拟数据迁移学习，将RT预测扩展到实验覆盖范围之外，显著提升泛化能力并遵循幂律关系。
中文摘要 AI 辅助
在不同分子和色谱方法中准确预测高效液相色谱（HPLC）保留时间（RTs）仍然具有挑战性，因为实验训练数据仅覆盖化学和方法空间的有限区域。在此，我们开发了FUSE-RT（用于保留时间的统一模拟与实验监督基础模型），这是一个多任务基础模型，整合了来自179种色谱方法的RT数据，并利用有限的目标域数据适应未见过的分子和方法。为了将可迁移性扩展到实验覆盖范围之外，我们引入了模拟到真实（Sim2Real）迁移学习，其中从大规模计算数据中学到的分子表示被迁移到实验RT预测中。具体来说，我们使用PolyOmics（包含通过分子动力学和密度泛函理论计算生成的约21,400个分子的39种性质）作为辅助监督。我们在分子、方法以及分子-方法联合分布偏移下评估泛化能力。模拟衍生的监督显著改善了超出实验分子域的迁移，特别是在明显的覆盖缺口和少样本适应情况下。此外，RT预测误差随着模拟数据规模的增加而系统性降低，遵循显著的幂律关系。这些结果确立了Sim2Real迁移作为一种可扩展策略，用于将RT预测扩展到实验色谱数据的有限覆盖范围之外。
英文摘要
Accurate prediction of high-performance liquid chromatography (HPLC) retention times (RTs) across diverse molecules and chromatographic methods remains challenging because experimental training data cover only a limited region of chemical and method spaces. Here, we develop FUSE-RT (Foundation model Unifying Simulation and Experimental supervision for Retention Time), a multitask foundation model that integrates RT data from 179 chromatographic methods and adapts to unseen molecules and methods using limited target-domain data. To extend transferability beyond experimental coverage, we introduce simulation-to-real (Sim2Real) transfer learning, in which molecular representations learned from large-scale computational data are transferred to experimental RT prediction. Specifically, we use PolyOmics, comprising 39 properties for approximately 21,400 molecules generated by molecular dynamics and density-functional theory calculations, as auxiliary supervision. We evaluate generalization under molecular, method, and joint molecular--method distribution shifts. Simulation-derived supervision substantially improves transfer beyond the experimental molecular domain, particularly under pronounced coverage gaps and few-shot adaptation. Moreover, RT-prediction error decreases systematically with increasing simulation-data size, following a significant power-law relationship. These results establish Sim2Real transfer as a scalable strategy for extending RT prediction beyond the finite coverage of experimental chromatographic data.
