Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction
Open accessAbstract
Median lethal death, LD 50, is a general indicator of compound acute oral toxicity (AOT). Various in silico methods were developed for AOT prediction to reduce costs and time. In this study, we developed an improved molecular graph encoding convolutional neural networks (MGE-CNN) architecture to construct three types of high-quality AOT models: regression model (deepAOT-R), multiclassification model (deepAOT-C), and multitask model (deepAOT-CR). These predictive models highly outperformed previously reported models. For the two external data sets containing 1673 (test set I) and 375 (test set II) compounds, the R 2 and mean absolute errors (MAEs) of deepAOT-R on the test set I were 0.864 and 0.195, and the prediction accuracies of deepAOT-C were 95.5% and 96.3% on test sets I and II, respectively. The two external prediction accuracies of deepAOT-CR are 95.0% and 94.1%, while the R 2 and MAE are 0.861 and 0.204 for test set I, respectively. We then performed forward and backward exploration of deepAOT models for deep fingerprints, which could support shallow machine learning methods more efficiently than traditional fingerprints or descriptors. We further performed automatic feature learning, a key essence of deep learning, to map the corresponding activation values into fragment space and derive AOT-related chemical substructures by reverse mining of the features. Our deep learning architecture for AOT is generally applicable in predicting and exploring other toxicity or property end points of chemical compounds. The two deepAOT models are freely available at http://repharma.pku.edu.cn/DLAOT/DLAOThome.php or http://www.pkumdl.cn/DLAOT/DLAOThome.php .
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Xu et al. used MGE-CNN to process standard molecular structure data and obtained a deep-minded fingerprint, yielding classification accuracy up to 95.0% and regression R square value of 0.861.
“Xu et al. used an enhanced molecular graph encoding convolutional neural networks (MGE-CNN) (the gray box in Figure 3) to process the standard molecular structure data, and finally obtained the fingerprint. The fingerprint was further mined both forwardly and backwardly, which yielded the deep-minded fingerprint (the array of black dots in Figure 3). The deep-minded fingerprint was then tested by”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [127]
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- Authors
- Xu, Y., Pei, J., & Lai, L.
- Affiliations
- Peking University (CN)
- Corresponding author
- Pei, Jianfeng
- Lai, Luhua
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-10-27
- Volume
- 57
- Issue
- 11
- Pages
- 2672-2685
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2610646689
- Cited by
- 250
- References
- 53
- Citations by year
- 2026: 16 · 2025: 25 · 2024: 29 · 2023: 42 · 2022: 26 · 2021: 33 · 2020: 33 · 2019: 25 · 2018: 21
- FWCI
- 16.20
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Ministry of Science and Technology of the People's Republic of China (2015CB910302, 2016YFA0502303)
- National Natural Science Foundation of China (21633001, 21673010)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › History and advancements in chemistry
- Keywords
- median lethal dose, LD50, chemical substructures, in silico toxicity prediction
- MeSH terms
- Machine Learning, Administration, Oral, Automation, Lethal Dose 50, Regression Analysis, Models, Statistical, Toxicity Tests, Informatics
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