Drug Response

Predicting drug response is crucial for personalized medicine, aiming to identify optimal treatments based on individual patient characteristics. Current research heavily utilizes machine learning, employing diverse architectures like graph neural networks, transformers, and attention-based models to integrate multi-omics data (genomics, transcriptomics) and even natural language processing of drug descriptions to improve prediction accuracy. These efforts focus on enhancing model interpretability and addressing challenges like data scarcity and the heterogeneity of drug responses across different cell lines and patients. Improved drug response prediction holds significant promise for accelerating drug development, optimizing treatment strategies, and ultimately improving patient outcomes.