Application progress of deep generative models in de novo drug design

The deep molecular generative model has recently become a research hotspot in pharmacy. This paper analyzes a large number of recent reports and reviews these models. In the central part of this paper, four compound databases and two molecular representation methods are compared. Five model architectures and applications for deep molecular generative models are emphatically introduced. Three evaluation metrics for model evaluation are listed. Finally, the limitations and challenges in this field are discussed to provide a reference and basis for developing and researching new models published in future.

Graphical abstract

Artificial intelligence has made significant leaps with the rapid development of big data and high-performance computing technology. As a technical means, artificial intelligence and deep learning have been deeply applied in all aspects of drug research, equipping researchers with innovative solutions and insights.