Drug Target Interaction

Predicting drug-target interactions (DTIs) is crucial for accelerating drug discovery by identifying potential drug candidates and their mechanisms of action. Current research heavily utilizes machine learning, employing deep neural networks such as graph attention networks, transformers, and graph neural networks, often incorporating diverse data sources like molecular structures, protein sequences, and knowledge graphs to improve prediction accuracy and interpretability. These advancements offer the potential to significantly reduce the time and cost associated with traditional drug development, leading to more efficient and effective therapeutic interventions. Furthermore, the development of unified frameworks that predict not only interactions but also binding affinities and mechanisms of action is a growing area of focus.