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Deep learning has gone through breakthroughs in many research fields including computer vision, natural language processing, etc. due to multiple driving factors such as knowledge, data, algorithms and computing power. In addition, it has gradually spawned a number of new research directions with the migration and application as well as cross-integration among various disciplines. Cheminformatics is a discipline that solves chemical problems with the applied informatics methods, and deep learning can be useful since it is very powerful in nonlinear learning. Deep learning model can be used to screen and predict in the data set, and then verify the feasibility of the results based on theoretical calculation. Finally, the results are represented by experiments, which shortens the experimental period, reduces the labor cost and accelerates the intelligence of cheminformatics. This paper briefly introduces the development history and main network model architecture of deep learning as well as the latest research and application status of deep learning in synthesis planning, compound structure-activity relation and catalyst design in recent years, and also discusses and expects the future development direction. [ABSTRACT FROM AUTHOR]