Unlocking the Potential of Generative Artificial Intelligence in Drug Discovery

Deep generative models have been widely employed across diverse fields, ranging from image and video analysis to natural language processing. In combination with the increasing computational power and abundant data resources available in the public domain, generative models have made significant advancements into the area of drug discovery and development. In particular, generative models are being extensively explored for de novo design of novel molecules, endowed with desirable physicochemical properties or biological activity, thereby accelerating the hit discovery phase by more rapidly sampling the chemical space of drug-like compounds. However, despite their considerable potential, these methods do have limitations that warrant consideration. For instance, they tend to generate compounds that may exhibit chemical instability, pose challenges in synthesis, or bear resemblance to existing drugs, thereby raising concerns regarding patentability. Furthermore, the experimental validation of the generated molecules through exemplary case studies remains limited. This chapter focuses on the application of generative models in de novo drug design. Firstly, we provide a brief introduction to commonly used generative models, such as recurrent neural networks, autoencoders, generative adversarial networks, as well as transfer learning and reinforcement learning techniques. Secondly, we conduct a comprehensive review of the latest developments in utilizing various generative models for drug discovery. This includes an analysis of benchmarks, metrics, and performance evaluation methods through the examination of diverse case studies. Finally, we shed light on the challenges associated with generative methods and discuss future directions in this dynamic and rapidly evolving field.