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 combin
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- LinkedLinked via arxiv author · 85%Sampreeti Bhattacharya →
“An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility”
- LinkedLinked via arxiv author · 85%Arkaprava Roy →
“An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility”
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