Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

Dong-Sheng Cao,Qian-Nan Hu,Qing-Song Xu,Shao Liu,Guang-Hua Zhou,Liu-Xia Zhang,Min He,Zhe Deng,Zi-Xin Deng,Yi-Zeng Liang

doi:10.1371/journal.pone.0057680

Abstract

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki.

Highlights

The identification of drug-target interaction networks is an area of intense research in drug discovery [1,2,3]
We found the significant enrichment of drug-target associations according to our random forest (RF) prediction probability (Figure 5)
We have shown that a RF can accurately predict the drug-target interactions based on integrated features, following the spirit of the chemogenomics approach

Summary

Introduction

The identification of drug-target interaction networks is an area of intense research in drug discovery [1,2,3]. Finding potential applications in other therapeutic categories of those FDA-approved drugs by predicting their targets, known as drug repositioning, is supported by the core observation that a single drug often interacts with multiple targets [5]. It offers an appealing strategy, and can be regarded as a very efficient and time-saving method in drug discovery [6,7,8]. It is almost impossible to carry out all experiments detecting the toxicity of a drug candidate by checking the interactions between this candidate and related proteins

Methods

Results

Conclusion