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 interac...
| Authors | Sampreeti Bhattacharya, Arkaprava Roy |
|---|---|
| Primary category | stat.ML |
| Published | 2 July 2026 |
| Last updated at source | 6 September 2026 |
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