#DRUG ATC CODE HOW TO#
#DRUG ATC CODE CODE#

The World Health Organization has established a complete drug classification system, Anatomical Therapeutic Chemical (ATC) ( MacDonald and Potvin, 2004).

#DRUG ATC CODE HOW TO#

How to find new indications from existing approved drugs and reduce the cost of research discovery is a hot field in bioinformatics ( Pushpakom et al., 2019 Jarada et al., 2020). A new drug, from development to launch, takes decades of research and hundreds of millions of dollars. 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.

#DRUG ATC CODE CODE#

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.Supplementary data are available at Bioinformatics online.Chaokun Yan 1,2 Zhihao Suo 1,2 Jianlin Wang 1,2 Ge Zhang 1,2 Huimin Luo 1,2* NetPredATC is available at or information: In conclusion, our new method, NetPredATC, can predict drug's ATC-codes more accurately by incorporating drug-target network and integrating data, which will promote drug mechanism understanding and drug repositioning and discovery. In addition, database search and functional annotation analysis support that our novel predictions are worthy of future experimental validation. Experimental results showed that our NetPredATC outperforms SuperPred not only in predictive coverage but also in accuracy. We extensively compared our NetPredATC with SuperPred, which is a chemical similarity-only based method. Further integrating these two data sources revealed more experimentally validated ATC-codes for drugs. We found that both drug's chemical structure and target protein are predictive, and target protein information has better accuracy. Our method was validated on four drug datasets with various target proteins, including enzymes, ion channels, G-protein couple receptors and nuclear receptors. Finally, we use a kernel method, support vector machine, to automatically predict drug's ATC-codes. Then we characterize ATC-code and drug by their similarity profiles and define kernel function to correlate them. Specifically, we first construct a gold-standard positive dataset from drugs' ATC-code annotation databases. Starting from the assumption that drugs with similar chemical structures or target proteins share common ATC-codes, our method, NetPredATC, aims to assign drug's potential ATC-codes by integrating chemical structures and target proteins.

Here, we introduce drug-target network to computationally predict drug's ATC-codes and propose a novel method named NetPredATC. However, few studies attempt to annotate drug's potential ATC-codes by computational approaches. Discovering drug's Anatomical Therapeutic Chemical (ATC) classification rules at molecular level is of vital importance to understand a vast majority of drugs action.

Drug atc code