Drug Target Interaction Prediction

Predicting drug-target interactions (DTIs) is crucial for accelerating drug discovery by identifying potential drug candidates and their biological targets. Current research heavily utilizes deep learning, employing graph neural networks, transformers, and other architectures to analyze molecular structures and heterogeneous biological data, often integrating information from multiple sources like protein sequences, chemical structures, and knowledge graphs. These advanced computational methods aim to improve prediction accuracy and interpretability, ultimately reducing the time and cost associated with traditional drug development. The resulting insights can significantly impact drug design, repurposing, and personalized medicine.