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

arXiv:2607.02212 · stat.ML, cs.LG · Submitted 2026-07-02 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-07-02

Updated: 2026-09-06

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 10× in this paper · explained in 1 episode
• Embeddings — 9× in this paper · explained in 2 episodes
• Transfer Learning — 8× in this paper · explained in 16 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Scaffold — 7× in this paper · explained in 2 episodes
• Interpretability — 6× in this paper · explained in 15 episodes
• Generalizability — 3× in this paper · explained in 6 episodes
• Adam — 2× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Graph Convolutional Network — 2× in this paper
• Overfitting — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Leakage — 1× in this paper · explained in 2 episodes
• Density Functional Theory (DFT) — 1× in this paper · explained in 3 episodes
• Feature Importance — 1× in this paper · explained in 2 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 13 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes

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