期刊
JOURNAL OF CHEMINFORMATICS
卷 12, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s13321-020-00420-z
关键词
Deep learning; Toxicity; Imbalanced learning; Upsampling
类别
资金
- Innovative Medicines Initiative 2 Joint Undertaking [777365]
- European Union
- EFPIA
- Austrian Science Fund FWF [W1232]
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 as a means to balance and oversample datasets for prediction of toxicity. Conformational oversampling enhances a dataset by generation of multiple conformations of a molecule. These conformations can be used to balance, as well as oversample a dataset, thereby increasing the dataset size without the need of artificial samples. We show that conformational oversampling facilitates training of neural networks and provides state-of-the-art results on the Tox21 dataset.
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