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Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction

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Xu, Youjun, Pei, Jianfeng, Lai, Luhua·2017·Journal of Chemical Information and Modeling

DOI 10.1021/acs.jcim.7b00244

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Abstract

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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Authors: Xu, Y., Pei, J., & Lai, L.
Affiliations: Peking University (CN)
Corresponding author: Pei, Jianfeng
Lai, Luhua
ORCID: Xu, Youjun
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
DOI: 10.1021/acs.jcim.7b00244
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: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Social Sciences › Arts and Humanities › History and Philosophy of Science › 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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Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction

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