这篇论文把化学描述和分子结构分开建模再相加，既准确又能解释预测依据。在AqSolDB和BigSolDB2上效果很好，适合医药研发。
该工作提出一个加性深度学习框架，用多层感知机（MLP）编码物理化学描述符作为化学分支，用图神经网络（GNN）编码分子图拓扑作为结构分支，两个分支的输出在预测阶段加性组合。在AqSolDB数据集上预训练、在BigSolDB2数据集上微调后，预测精度显著提升且运行变异降低。可解释性分析通过最佳线性投影、分子嵌入和GNNExplainer显示化学分支对齐已知物化描述符，结构分支捕捉图拓扑和官能团模式。在两个数据集上，该框架取得有竞争力的预测性能，同时使化学与结构信息的各自作用更透明。
An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility
Aqueous solubility is a key property in early-stage drug discovery, but most predictive models merge physicochemical descriptors and molecular graph information into a single representation, obscuring whether a prediction is driven by global chemistry, molecular structure, or both. We present an additive deep-learning framework that keeps these two sources of information separate throughout training: physicochemical descriptors are encoded by a multilayer perceptron (the chemical branch) and molecular graph topology by a graph neural network (the structural branch), with the two outputs combined only at the prediction stage through an additive model with an optional multiplicative interaction. This design provides a direct decomposition of chemical and structural components that can be examined separately after training. Furthermore, pretraining on the larger AqSolDB dataset and fine-tuning on the smaller BigSolDB2 dataset substantially improve accuracy and reduce run-to-run variations, indicating generalizability of the learned features from the data-rich settings. We further interpret the fitted model using best linear projections of the branch outputs, molecule-level embedding summaries across solubility classes, and atom-level GNNExplainer masks aggregated over functional groups. These analyses show that the chemical branch aligns with familiar physicochemical descriptors, while the structural branch captures graph-topological and functional-group patterns associated with solubility. Across both datasets, the framework attains competitive predictive performance while making the distinct roles of chemical and structural information more transparent.