Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction
文献类型：期刊论文
| 作者 | Xu, Youjun1; Pei, Jianfeng1; Lai, Luhua1,2,3 |
| 刊名 | JOURNAL OF CHEMICAL INFORMATION AND MODELING
 |
| 出版日期 | 2017-11-01 |
| 卷号 | 57期号:11页码:2672-2685 |
| ISSN号 | 1549-9596 |
| DOI | 10.1021/acs.jcim.7b00244 |
| 英文摘要 | Median lethal death, LD50, 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://w-ww.pkumdl.cn/DLAOT/ DLAOThome.php. |
| 语种 | 英语 |
| WOS记录号 | WOS:000416614900006 |
| 出版者 | AMER CHEMICAL SOC |
| 源URL | [http://ir.iccas.ac.cn/handle/121111/45061]  |
| 专题 | 中国科学院化学研究所 |
| 通讯作者 | Pei, Jianfeng; Lai, Luhua |
| 作者单位 | 1.Peking Univ, Ctr Quantitat Biol, Acad Adv Interdisciplinary Studies, Beijing 100871, Peoples R China 2.Peking Univ, Coll Chem & Mol Engn, State Key Lab Struct Chem Unstable & Stable Speci, Beijing Natl Lab Mol Sci, Beijing 100871, Peoples R China 3.Peking Univ, Peking Tsinghua Ctr Life Sci, Beijing 100871, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Youjun,Pei, Jianfeng,Lai, Luhua. Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2017,57(11):2672-2685. |
| APA | Xu, Youjun,Pei, Jianfeng,&Lai, Luhua.(2017).Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction.JOURNAL OF CHEMICAL INFORMATION AND MODELING,57(11),2672-2685. |
| MLA | Xu, Youjun,et al."Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction".JOURNAL OF CHEMICAL INFORMATION AND MODELING 57.11(2017):2672-2685. |
入库方式： OAI收割
来源：化学研究所
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