Abstract

The field of drug discovery has long been a challenging and time-consuming process, often requiring years of research and testing to develop new treatments for diseases. However, recent advancements in artificial intelligence (AI) have the potential to revolutionize the drug discovery process and significantly accelerate the development of new therapeutics. AI-based drug discovery methods involve using machine learning algorithms to analyze large datasets of biological and chemical information. This approach can quickly identify potential drug candidates and predict their efficacy, reducing the need for expensive and time-consuming experimental testing. One example of AI-based drug discovery is the use of deep learning algorithms to analyze the three-dimensional structure of proteins and predict how small molecules could bind to them. This approach has already led to the discovery of new drug candidates for diseases such as Alzheimer's and cancer.