Prediction of anti-HIV activity on the basis of stacked auto-encoder-Reference-Cited by-同舟云学术

Prediction of anti-HIV activity on the basis of stacked auto-encoder

Published:2017-07-18 Issue:10 Volume:31 Page:e2916
ISSN:0886-9383
Container-title:Journal of Chemometrics
language:en
Short-container-title:Journal of Chemometrics

Author:

Tian Shengwei¹²³,Yan Yilin⁴^ORCID,Yu Long²³⁵,Qian Jin²³,Ye Feiyue²³

Affiliation:

1. School of Software; Xinjiang University; 499 Xibei Road Ürümqi 830008 China

2. School of Computer Engineering; Jiangsu University of Technology; Zhongwu Avenue #1801 Changzhou 213001 China

3. Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City; Jiangsu University of Technology; Changzhou 213000 China

4. Institute of Information Science and Engineering; Xinjiang University; 14 Shengli Road Ürümqi 830046 China

5. Network Center; Xinjiang University; 14 Shengli Road Ürümqi 830046 China

Funder

Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City, China

Publisher

Wiley

Subject

Applied Mathematics,Analytical Chemistry

Reference36 articles.

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3. Synthesis and structure-activity relationships of a new model of arylpiperazines. 6. Study of the 5-HT1A/α1-adrenergic receptor affinity by classical Hansch analysis, artificial neural networks, and computational simulation of ligand recognition;López-Rodríguez;J Med Chem,2001

4. Principles of data mining;Hand;Drug Saf,2007

5. Machine-learning approaches in drug discovery: methods and applications;Lavecchia;Drug Discov Today,2014

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