Drug Feature

Drug feature research focuses on extracting and utilizing relevant information from diverse sources to improve various pharmaceutical applications, primarily aiming to enhance drug discovery, development, and safety. Current research heavily employs machine learning, particularly deep learning models like convolutional neural networks and graph neural networks, often integrated with large language models and knowledge graphs to analyze complex data such as molecular structures, clinical trial results, and unstructured text. These advancements hold significant promise for accelerating drug development, improving prediction accuracy of drug properties and adverse events, and ultimately enhancing patient safety and treatment outcomes.