Drug Pair Scoring

Drug pair scoring aims to predict the combined effect of two drugs, crucial for optimizing drug combinations in personalized medicine and understanding drug-drug interactions. Current research focuses on developing and applying deep learning models, including neural ranking methods and graph neural networks, to analyze large datasets of drug responses and molecular structures to improve prediction accuracy. These advancements are improving the efficiency of drug discovery and development, leading to more effective and safer combination therapies. A unified theoretical framework is emerging to standardize model architectures, datasets, and evaluation metrics, facilitating broader collaboration and progress in the field.