The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.
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 .
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
(2012) · Cited by 8,009
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,925
Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354
An overview of gradient descent optimization algorithms
Ruder, S. (2016) · arXiv · doi:10.48550/arxiv.1609.04747 · Cited by 4,962
Face recognition: a convolutional neural-network approach
Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997) · IEEE Transactions on Neural Networks · doi:10.1109/72.554195 · Cited by 3,092
admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties
Cheng, F., Li, W., Zhou, Y., Shen, J., Wu, Z., Liu, G., Lee, P. W., & Tang, Y. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300367a · Cited by 2,279
Reoptimization of MDL Keys for Use in Drug Discovery
Durant, J. L., Leland, B. A., Henry, D. R., & Nourse, J. G. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010132r · Cited by 1,886
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631
Research works printed but not linked: 16
- Enslein K., 1977, J. Environ. Pathol. Toxicol., 2, 115
- Graves A., 2014, arXiv.org, arXiv:1410.5401 · arXiv
- Weston J., 2014, arXiv.org, arXiv:1410.3916 · arXiv
- Grefenstette E., 2015, NIPS, 1828
- Joulin A., 2015, NIPS, 190
- Neelakantan A., 2015, arXiv.org, arXiv:1511.0483
- Reed S. E., 2015, arXiv.org, arXiv:1511.0627
- Rocktäschel T., 2017, arXiv.org, arXiv:1705.1104
- Duvenaud D. K., 2015, NIPS, 2224
- Zeiler M. D., 2013, arXiv.org, arXiv:1311.2901 · arXiv
- Krizhevsky A., 2012, NIPS, 1097
- Bergstra J., 2012, J. Mach. Learn Res., 13, 281
- Ruder S., 2016, arXiv.org, arXiv:1609.0474
- U. S. Environmental Protection Agency, 2016, Label Review Manual
- Katritzky A. R., 1996, Rev. Roum. Chim., 41, 851
- Pedregosa F., 2011, J. Mach. Learn Res., 12, 2825
Cited by
Sorted by citations
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473
“…characteristics features of drug compounds such as SMILES2Vec (predicts chemical properties) [339], Chemception (DNN-based prediction of chemical properties) [245], DeepSynergy (prediction of anti-cancer drug synergy with DL) [340], and deepAOT (prediction of compound acute oral toxicity) [341].”
BackgroundDrug discovery with explainable artificial intelligence
Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,079
Machine Learning in Drug Discovery: A Review
Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M., & Ahsan, M. J. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10058-4 · Cited by 893
Metadata
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
