1. Deep active learning with high structural discriminability for molecular mutagenicity prediction.
- Author
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Xu, Huiyan, Zhao, Yanpeng, Zhang, Yixin, Han, Junshan, Zan, Peng, He, Song, and Bo, Xiaochen
- Subjects
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DRUG discovery , *MUTAGENICITY testing , *DEEP learning , *GERM cells , *STATISTICAL sampling , *ESSENTIAL drugs , *FORECASTING - Abstract
The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecules. However, experimental mutagenicity testing can be time-consuming and costly. One solution to reduce the annotation cost is active learning, where the algorithm actively selects the most valuable molecules from a vast chemical space and presents them to the oracle (e.g., a human expert) for annotation, thereby rapidly improving the model's predictive performance with a smaller annotation cost. In this paper, we propose muTOX-AL, a deep active learning framework, which can actively explore the chemical space and identify the most valuable molecules, resulting in competitive performance with a small number of labeled samples. The experimental results show that, compared to the random sampling strategy, muTOX-AL can reduce the number of training molecules by about 57%. Additionally, muTOX-AL exhibits outstanding molecular structural discriminability, allowing it to pick molecules with high structural similarity but opposite properties. An active learning model, muTOX-AL, has been proposed for the task of molecular mutagenicity prediction. muTOX-AL is capable of rapidly enhancing its performance with a small number of labeled samples, thereby reducing experimental costs. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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