SMILESGNN: Interpretable Clinical Toxicity Prediction via SMILES-Graph Cross-Attention Fusion

arXiv:2609.28553 · cs.LG, cs.AI · Submitted 2026-09-23 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• AUC — 18× in this paper · explained in 2 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Interpretability — 7× in this paper · explained in 15 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Multimodal Fusion — 4× in this paper · explained in 3 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 3× in this paper
• Regularization — 3× in this paper · explained in 3 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• GNNs — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• International Conference on Machine Learning — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

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