Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

Abstract The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing...

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Bibliographic Details
Main Authors: Eelke B. Lenselink, Niels ten Dijke, Brandon Bongers, George Papadatos, Herman W. T. van Vlijmen, Wojtek Kowalczyk, Adriaan P. IJzerman, Gerard J. P. van Westen
Format: Article
Language:English
Published: BMC 2017-08-01
Series:Journal of Cheminformatics
Subjects:
Deep neural networks
ChEMBL
QSAR
Proteochemometrics
Chemogenomics
Cheminformatics
Online Access:http://link.springer.com/article/10.1186/s13321-017-0232-0

Internet

http://link.springer.com/article/10.1186/s13321-017-0232-0

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