Compound Protein Interaction

Compound-protein interaction (CPI) prediction aims to identify which compounds bind to which proteins, a crucial task for drug discovery and repurposing. Current research heavily utilizes deep learning, employing various architectures like graph neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), often incorporating multimodal data (protein sequences, structures, and compound properties) and contrastive learning techniques to improve prediction accuracy and generalization. These advancements are improving the efficiency and effectiveness of drug development by enabling more accurate identification of potential drug candidates and facilitating the exploration of previously "undruggable" targets. The field is also actively addressing challenges related to data scarcity, imbalanced datasets, and robust evaluation methodologies.