An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility

• URL: http://arxiv.org/abs/2607.02212v2
• Date: Fri, 03 Jul 2026 23:05:23 GMT
• Title: An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility
• Abstract summary: Aqueous solubility is a key property in early-stage drug discovery.<n>Most predictive models merge physicochemical descriptors and molecular graph information into a single representation.<n>We present an additive deep-learning framework that keeps these two sources of information separate throughout training.
• Score: 0.40105987447353786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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