Deep Generative Models in Drug Discovery

This lecture explores the application of deep generative models in drug discovery, focusing on the use of these models to design small molecules, optimize molecular structures, and enhance the synthesis of novel compounds. The lecture covers topics such as generative models for molecular engineering, de novo molecular design, molecular optimization strategies, and the evaluation of generative models in chemical space coverage. It also delves into the potential of meta-learning for small molecule optimization and the development of open-source tools for data-driven molecular design. The lecture showcases real-world applications of deep generative models in drug discovery and highlights the latest advancements in the field.