Prediction of serious eye damage or eye irritation potential of compounds via consensus labelling models and active learning models based on uncertainty strategies
文献类型：期刊论文
| 作者 | Di, Peiwen1,2; Zheng, Mingyue1,2 ; Yang, Tianbiao1; Chen, Geng1; Ren, Jianan1; Li, Xutong2; Jiang, Hualiang1,2
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| 刊名 | Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
 |
| 出版日期 | 2022-09-18 |
| 卷号 | 169页码:113420 |
| ISSN号 | 1873-6351 |
| 关键词 | Active learning Consensus labelling Eye irritation Serious eye damage Structural alerts |
| DOI | 10.1016/j.fct.2022.113420 |
| 文献子类 | Article |
| 英文摘要 | Serious eye damage and eye irritation have been authenticated to be significant human health issues in various fields such as ophthalmic pharmaceuticals. Due to the shortcomings of traditional animal testing methods, in silico methods have advanced to study eye toxicity. The models for predicting serious eye damage and eye irritation potential of compounds were developed using 2299 and 5214 compounds, respectively. The 40 global single models and 40 local models were developed by combining 5 molecular description methods and 4 machine learning methods. The 40 active learning models were developed by adopting uncertainty-based active learning strategies and taking local models as initial models. The 110 global consensus models based on 40 global single models were developed using a consensus strategy. Active learning models and global consensus models performed high prediction accuracy. The test accuracy of the best serious eye damage model and eye irritation model reached 0.972 and 0.959, respectively. The applicability domains for all models were calculated to verify the rationality of prediction effect. In addition, 8 structural alerts probably causing serious eye damage or eye irritation were sought out. The prediction models and structural alerts contributed to providing hazard identification and assessing chemical safety. |
| 语种 | 英语 |
| WOS记录号 | MEDLINE:36108981 |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/309286]  |
| 专题 | 新药研究国家重点实验室 |
| 作者单位 | 1.School of Pharmacology Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China; 2.Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China |
| 推荐引用方式 GB/T 7714 | Di, Peiwen,Zheng, Mingyue,Yang, Tianbiao,et al. Prediction of serious eye damage or eye irritation potential of compounds via consensus labelling models and active learning models based on uncertainty strategies[J]. Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,2022,169:113420. |
| APA | Di, Peiwen.,Zheng, Mingyue.,Yang, Tianbiao.,Chen, Geng.,Ren, Jianan.,...&Jiang, Hualiang.(2022).Prediction of serious eye damage or eye irritation potential of compounds via consensus labelling models and active learning models based on uncertainty strategies.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association,169,113420. |
| MLA | Di, Peiwen,et al."Prediction of serious eye damage or eye irritation potential of compounds via consensus labelling models and active learning models based on uncertainty strategies".Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association 169(2022):113420. |
入库方式： OAI收割
来源：上海药物研究所
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