EnSol：一种用于分子溶解度预测的环境感知图神经网络

中文摘要 AI 辅助

分子溶解度直接影响分子开发的关键方面，如反应可行性、配方性能、分离效率和溶剂选择。然而，跨溶质、溶剂和温度的实验测量仍然成本高昂且采样稀疏。现有的计算模型通常依赖于固定溶剂假设、确定性公式或溶质-溶剂相互作用的简化表示，限制了它们捕捉复杂分子相互作用、连续温度效应和实验不确定性的能力。在此，我们引入EnSol，一种用于分子溶解度预测的环境感知概率框架。EnSol将溶质和溶剂表示为分子图，并分别学习各自的表示，然后通过交叉注意力将它们结合起来以捕捉溶质-溶剂相互作用。温度通过特征级调制直接纳入溶剂环境，混合密度网络预测完整的溶解度分布，以捕捉温度依赖行为和实验不确定性。在独立的SolProp和Leeds基准数据集上，EnSol分别实现了0.876和0.601的Spearman相关系数，在两个基准上均优于最先进的溶解度预测模型。除了计算基准测试外，跨化学多样性溶质-溶剂对的实验验证表明，EnSol保持了强大的预测性能，并支持可靠的溶剂排序，实现了0.715的Spearman相关系数。这些结果表明，EnSol能够在考虑预测不确定性的情况下，支持跨多样化学系统的可靠溶解度预测和溶剂选择。

英文摘要

Molecular solubility directly affects key aspects of molecular development such as reaction feasibility, formulation performance, separation efficiency, and solvent selection. However, experimental measurement across solutes, solvents, and temperatures remains costly and sparsely sampled. Existing computational models often rely on fixed-solvent assumptions, deterministic formulations, or simplified representations of solute-solvent interactions, limiting their ability to capture complex molecular interactions, continuous temperature effects, and experimental uncertainty. Here, we introduce EnSol, an environment-aware probabilistic framework for molecular solubility prediction. EnSol represents the solute and solvent as molecular graphs and learns separate representations for each before bringing them together through cross-attention to capture solute-solvent interactions. Temperature is incorporated directly into the solvent environment through feature-wise modulation, and a mixture density network predicts full solubility distributions to capture both temperature-dependent behavior and experimental uncertainty. On the independent SolProp and Leeds benchmark datasets, EnSol achieved Spearman correlations of 0.876 and 0.601, respectively, outperforming state-of-the-art solubility prediction models across both benchmarks. Beyond computational benchmarking, experimental validation across chemically diverse solute-solvent pairs showed that EnSol maintained strong predictive performance and supported reliable solvent ranking, achieving a Spearman correlation of 0.715. These results show that EnSol can support reliable solubility prediction and solvent selection across diverse chemical systems while accounting for predictive uncertainty.

发表机构

• Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校西贝尔计算与数据科学学院）
• Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校卡尔·R·沃斯基因组生物学研究所）
• NSF Molecule Maker Lab Institute, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校NSF分子制造实验室研究所）
• Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校化学与生物分子工程系）
• DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校DOE先进生物能源与生物制品创新中心）

机构由 AI 辅助整理，请以论文原文为准。