Drug Target

Drug target identification is crucial for efficient drug discovery, aiming to predict which biological molecules are most effectively modulated by a drug candidate. Current research heavily utilizes graph-based deep learning models, including graph attention networks, graph convolutional networks, and transformers, often incorporating multiple data sources (e.g., protein structure, chemical properties, genomic data) to improve prediction accuracy and interpretability. These advancements aim to reduce the high cost and failure rate of traditional drug development, accelerating the identification of effective therapies and potentially leading to personalized medicine approaches.