COVER: conformational oversampling as data augmentation for molecules

COVER: conformational oversampling as data augmentation for molecules

Abstract Training neural networks with small and imbalanced datasets often leads to overfitting and disregard of the minority class. For predictive toxicology, however, models with a good balance between sensitivity and specificity are needed. In this paper we introduce conformational oversampling a...

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Bibliographic Details
Main Authors: Jennifer Hemmerich, Ece Asilar, Gerhard F. Ecker
Format: Article
Language:English
Published: BMC 2020-03-01
Series:Journal of Cheminformatics
Subjects:
Deep learning
Toxicity
Imbalanced learning
Upsampling
Online Access:http://link.springer.com/article/10.1186/s13321-020-00420-z

Internet

http://link.springer.com/article/10.1186/s13321-020-00420-z

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