An additive MLP–GNN model separates chemical and structural drivers of aqueous solubility, matches competitive MAE, and supports post-hoc branch-level interpretation plus transfer learning.
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An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility
An additive MLP–GNN model separates chemical and structural drivers of aqueous solubility, matches competitive MAE, and supports post-hoc branch-level interpretation plus transfer learning.