Predicting the binding affinities of compound–protein interactions by random forest using network topology features-Reference-Cited by-同舟云学术

Predicting the binding affinities of compound–protein interactions by random forest using network topology features

Published:2018 Issue:34 Volume:10 Page:4152-4161
ISSN:1759-9660
Container-title:Analytical Methods
language:en
Short-container-title:Anal. Methods

Author:

Li Zhanchao¹²³⁴⁵^ORCID,Wang Yang⁶⁷³⁴,Xie Yun¹²³⁴,Zhang Li⁶⁷³⁴,Dai Zong⁶⁷³⁴,Zou Xiaoyong⁶⁷³⁴^ORCID

Affiliation:

1. School of Chemistry and Chemical Engineering

2. Guangdong Pharmaceutical University

3. Guangzhou

4. People's Republic of China

5. Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica of State Administration of Traditional Chinese Medicine

6. School of Chemistry

7. Sun Yat-Sen University

Abstract

A predictor is proposed for the binding affinity of compound–protein interactions by constructing a comprehensive interaction network incorporating topology features.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Engineering,General Chemical Engineering,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2018/AY/C8AY01396A

Reference45 articles.

1. Identification of drug–target interaction from interactome network with ‘guilt-by-association’ principle and topology features

2. Predicting drug–target interaction for new drugs using enhanced similarity measures and super-target clustering

3. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique

4. Improving compound–protein interaction prediction by building up highly credible negative samples

5. DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

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