The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Deep Learning for Drug-Induced Liver Injury
Open accessAbstract
Drug-induced liver injury (DILI) has been the single most frequent cause of safety-related drug marketing withdrawals for the past 50 years. Recently, deep learning (DL) has been successfully applied in many fields due to its exceptional and automatic learning ability. In this study, DILI prediction models were developed using DL architectures, and the best model trained on 475 drugs predicted an external validation set of 198 drugs with an accuracy of 86.9%, sensitivity of 82.5%, specificity of 92.9%, and area under the curve of 0.955, which is better than the performance of previously described DILI prediction models. Furthermore, with deep analysis, we also identified important molecular features that are related to DILI. Such DL models could improve the prediction of DILI risk in humans. The DL DILI prediction models are freely available at http://www.repharma.cn/DILIserver/DILI_home.php.
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Advances in Neural Information Processing Systems 25
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Cited by
The rise of deep learning in drug discovery
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Recent advances and applications of deep learning methods in materials science
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“DL for molecular materials have been used to predict technologically important properties such as aqueous solubility [135] and toxicity [136].”
MethodDeep learning for computational chemistry
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809
Protein–Ligand Scoring with Convolutional Neural Networks
Ragoza, M., Hochuli, J., Idrobo, E., Sunseri, J., & Koes, D. R. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00740 · Cited by 774
Deep Learning in Drug Discovery
Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723
Metadata
- Authors
- Xu, Y., Dai, Z., Chen, F., Gao, S., Pei, J., & Lai, L.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-10-13
- Volume
- 55
- Issue
- 10
- Pages
- 2085-2093
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2234529989
- Cited by
- 382
- References
- 63
- FWCI
- 24.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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