![]() ĭrug research and development is time-consuming and costly. The source codes of our method are available at. Experiments on the benchmark datasets demonstrate that the proposed DACPGTN model can achieve better prediction performance than the existing methods. Based on the constructed composite features and learned heterogeneous networks, we employ graph convolution network to generate the embedding of drug nodes, which are further used for the multi-label learning tasks in drug discovery. Inspired by the application of Graph Transformer Network, we learn potential novel interactions among drugs diseases and targets from the known interactions to construct drug-target-disease heterogeneous networks containing comprehensive interaction information. DACPGTN constructs composite features of drugs, diseases and targets by applying diverse biomedical information. In this article, we propose an end-to-end model DACPGTN to predict the ATC code for the given drug. Predicting the ATC code of a given drug helps to understand the indication and potential toxicity of the drug, thus promoting its use in the therapeutic phase and accelerating its development. The ATC system assigns different ATC codes to drugs based on their anatomy, pharmacological, therapeutics and chemical properties. The Anatomical Therapeutic Chemical (ATC) classification system is a drug classification scheme proposed by the World Health Organization, which is widely used for drug screening, repositioning, and similarity research. 2Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China.1School of Computer and Information Engineering, Henan University, Kaifeng, China.Chaokun Yan 1,2 Zhihao Suo 1,2 Jianlin Wang 1,2 Ge Zhang 1,2 Huimin Luo 1,2*
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