Drug Candidate

Drug candidate research focuses on identifying and optimizing molecules for therapeutic use, primarily through computational methods that accelerate and improve the drug discovery process. Current efforts leverage machine learning, particularly deep learning models like recurrent neural networks and transformers, alongside genetic algorithms and molecular docking, to generate novel molecules and predict their bioactivity and properties like ADMET. These advancements aim to improve the efficiency and success rate of drug development, addressing challenges such as long timelines and high failure rates in clinical trials, ultimately leading to faster development of effective treatments.