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, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics. In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naïve B

SUBMITTER: Lenselink EB 

PROVIDER: S-EPMC5555960 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature