Drug Representation

Drug representation focuses on developing computational methods to encode the complex properties of drugs into numerical vectors, facilitating tasks like drug-drug interaction prediction, drug-target interaction prediction, and adverse drug reaction prediction. Current research emphasizes the use of graph neural networks, incorporating diverse data modalities (chemical structures, biological pathways, clinical data), and leveraging techniques like contrastive learning and transfer learning to improve model performance and generalizability. These advancements are crucial for accelerating drug discovery, improving patient safety, and personalizing medicine by enabling more accurate and efficient prediction of drug effects and interactions.