Drug Combination

Drug combination research aims to identify optimal pairings of drugs for enhanced therapeutic efficacy and reduced side effects, addressing the limitations of single-drug therapies. Current research focuses on developing AI-driven methods, employing graph neural networks, transformer-based architectures, and reinforcement learning algorithms like neural Thompson sampling, to predict synergistic drug combinations and improve the efficiency of drug discovery. These computational approaches leverage diverse data sources, including molecular structures, gene expression profiles, and clinical trial data, to identify promising combinations and elucidate underlying mechanisms of action. This work holds significant potential to accelerate drug development, personalize treatment strategies, and ultimately improve patient outcomes.