Drug Synergy Prediction

Drug synergy prediction aims to computationally identify drug combinations that produce a greater therapeutic effect than the sum of their individual effects, accelerating drug discovery and personalized medicine. Current research heavily utilizes deep learning, particularly graph neural networks and transformer-based models like those inspired by GPT architectures, to analyze diverse data sources including drug chemical structures, gene expression profiles, and even pre-trained language models. These models strive to improve the accuracy of predictions, especially for novel drug combinations and cell lines, ultimately aiming to optimize cancer therapies and other treatments.