Tag: drug-target interaction

Researchers introduce an auditable cold-start evaluation framework to ensure drug interaction models accurately demonstrate real-world generalization.

Researchers incorporated clustered somatic mutations into a knowledge graph embedding framework to enhance the computational prediction of drug-target interactions for ...

A hybrid quantum-classical graph neural network preserved ranking information under extreme compression on a kinase benchmark, but multi-seed testing shows ...

An AI framework combining Kolmogorov–Arnold network drug–target prediction with AutoML modeling of Chinese Materia Medica properties identified lipid-lowering candidates from ...

A new dual-modality deep learning framework called DHF-DTI combines sequence and graph data with cross-attention fusion and supervised contrastive learning ...

A new multiscale heterogeneous graph transformer called HierHGT-DTI achieves large gains in cold-start drug-target interaction prediction, especially for protein targets ...

Researchers have developed TCM-Complexity, an active learning framework that uses the local structural complexity of drug–target embedding spaces to select ...

A controlled ablation study shows that classifier design and optimisation, not cross-attention alone, account for most of the improvement a ...

A new review maps how artificial intelligence is transforming oncology drug discovery, clinical decision-making, and regulatory science from molecule design ...