A new chemoinformatics approach with improved strategies for effective predictions of potential drugs-Reference-Cited by-同舟云学术

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A new chemoinformatics approach with improved strategies for effective predictions of potential drugs

Published:2018-10-11 Issue:1 Volume:10 Page:
ISSN:1758-2946
Container-title:Journal of Cheminformatics
language:en
Short-container-title:J Cheminform

Author:

Hao Ming^ORCID,Bryant Stephen H.,Wang Yanli

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Computer Graphics and Computer-Aided Design,Physical and Theoretical Chemistry,Computer Science Applications

Link

http://link.springer.com/content/pdf/10.1186/s13321-018-0303-x.pdf

Reference37 articles.

1. Hao M, Li Y, Wang Y, Yan Y, Zhang S (2011) Combined 3D-QSAR, molecular docking, and molecular dynamics study on piperazinyl-glutamate-pyridines/pyrimidines as potent P2Y12 antagonists for inhibition of platelet aggregation. J Chem Inf Model 51:2560–2572

2. Cai J, Li C, Liu Z, Du J, Ye J, Gu Q, Xu J (2017) Predicting DPP-IV inhibitors with machine learning approaches. J Comput Aided Mol Des 31:393–402

3. Hao M, Li Y, Wang Y, Zhang S (2011) A classification study of human β3-adrenergic receptor agonists using BCUT descriptors. Mol Divers 15:877

4. Myint K-Z, Wang L, Tong Q, Xie X-Q (2012) Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions. Mol Pharm 9:2912–2923

5. Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M (2008) Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24:i232–i240

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