Target Interaction

Predicting drug-target interactions (DTIs) is crucial for accelerating drug discovery and understanding disease mechanisms. Current research heavily utilizes machine learning, particularly graph neural networks and federated learning approaches, to analyze large datasets integrating diverse information such as molecular structures, cellular assays (like Cell Painting), and protein-protein interaction networks. These methods aim to improve the accuracy and efficiency of DTI prediction, addressing challenges like data sparsity and the need for privacy-preserving data aggregation. Ultimately, advancements in DTI prediction hold significant promise for developing more effective and safer therapeutics.