Deep learning approaches can improve drug discovery by allowing more precise forecasts of drug-target interactions, molecular characteristics, and potential adverse effects ). For example, the chemogenomics neural network (CN) is the formulation of chemogenomics with deep learning. The deep learning CN approach is superior to novel shallow methods ( ). In addition, a deep-learning model has been designed to predict retrosynthetic pathways ( ). Also, FRnet-Encode to distinguish 4096 features. FRnet-Encode is constructed on a deep convolutional neural network ( ). These accomplished researches indicate that the impact of the deep-learning algorithm on hit identification will increase over time.