Drug Discovery Process

Drug discovery aims to identify and develop new therapeutic agents efficiently and effectively. Current research heavily utilizes machine learning, particularly deep learning models like graph neural networks and generative adversarial networks (GANs), alongside evolutionary algorithms and large language models (LLMs), to predict drug-target interactions, generate novel molecules with desired properties, and optimize existing compounds. These computational approaches are integrated with knowledge graphs and various data sources to improve prediction accuracy, reduce experimental costs, and accelerate the overall drug development process. The ultimate goal is to improve the success rate of clinical trials and provide patients with safer and more effective treatments.