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Ai Drug Repurposing

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Ai Drug Repurposing

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Research Frontiers in Graph Neural Networks Drug Target Prediction

Utilizing graph neural networks to predict novel drug-target interactions by modeling protein-ligand binding as graph structures with learned node and edge representations.

Heterogeneous Graph Dynamics in Polypharmacology Networks

Message Passing Architectures for Off-Target Effect Prediction

Temporal Graph Evolution in Drug-Disease Comorbidity Spaces

Equivariant Neural Networks for 3D Protein-Ligand Topology

Knowledge Graph Completion in Sparse Biomedical Networks

Graph Attention Mechanisms for Multi-Modal Drug Signatures

Adversarial Robustness in Neural Target Prediction Models

Scalable Graph Kernels for Large-Scale Repurposing Screening

Interpretable Node Embeddings in Drug-Gene-Disease Hypergraphs

Transfer Learning Across Heterogeneous Pharmacological Graphs

All AI Drug Repurposing PhD categories