AUTHOR=Zheng Jie , Xiao Xuan , Qiu Wang-Ren 

TITLE=iCDI-W2vCom: Identifying the Ion Channel–Drug Interaction in Cellular Networking Based on word2vec and node2vec

JOURNAL=Frontiers in Genetics

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.738274

DOI=10.3389/fgene.2021.738274

ISSN=1664-8021

ABSTRACT=Ion channels are the second largest drug target family. Ion channel dysfunction may lead to a number of diseases such as Alzheimers disease, epilepsy, cephalagra, and type II diabetes. In the research work for predicting ion channel-drug, computational approaches are effective and efficient compared to the costly, labor-intensive and time-consuming experimental methods. Most of the existing methods can only be used to deal with the ion channel of knowing 3D structures, however, the 3D structures of most ion channels are still unknown. Many predictors based on protein sequence were developed to address the challenge while most of its results need to be improved or predicting webservers are missing. In this paper, a sequence-based classifier, called “iCDI-W2vCom”, was developed to identify the interactions between ion channels and drugs. In the predictor, the drug compound was formulated by SMILE-Word2vec, FP2-Word2vec, SMILE-Node2vec and ECFPs via a 1184D vector, ion channel was represented by the Word2vec via a 64D vector, and the prediction engine was operated by the LightGBM classifier. The overall success rate and AUC achieved by iCDI-W2vCom via the 5-fold cross validation were 91.95% and 97.21%, which are remarkably higher than any of the existing predictors in this area. A user-friendly web server for iCDI-W2vCom was established at http://www.jci-bioinfo.cn/icdiw2v. The proposed method may also be potential methods for predicting target-drug interaction.