Network predicting drug’s anatomical therapeutic chemical code

王永翠,陈世龙,邓乃扬,王勇^*.Network predicting drug’s anatomical therapeutic chemical code.BIOINFORMATICS.2013,29(10):1317–1324.IF=5.323 

Abstract:Motivation: Discovering drug’s Anatomical Therapeutic Chemical(ATC) classification rules at molecular level is of vital importance to understand a vast majority of drugs action. However, few studies attempt to annotate drug’s potential ATC-codes by computational approaches.

Results: Here,we introduce drug-target network to computationallypredict drug’s ATC-codes and propose a novel method namedNetPredATC. 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 ATCcodes by integrating chemical structures and target proteins. Specifically, we first construct a gold-standard positive dataset from drugs’ ATC-code annotation databases. Then we characterize ATCcode and drug by their similarity profiles and define kernel function to correlate them. Finally, we use a kernel method, support vector machine, to automatically predict drug’s ATC-codes. Our method was validated on four drug datasets with various target proteins, including enzymes, ion channels, G-protein couple receptors and nuclear receptors. We found that both drug’s chemical structure and target protein are predictive, and target protein information has better accuracy. Further integrating these two data sources revealed more experimentally validated ATC-codes for drugs. We extensively compared our NetPredATC with SuperPred, which is a chemical similarity-only based method. Experimental results showed that our NetPredATC outperforms SuperPred not only in predictive coverage but also in accuracy. In addition, database search and functional annotation analysis support that our novel predictions are worthy of future experimental validation.

Conclusion: 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.