Blood–brain barrier penetration prediction enhanced by uncertainty estimation
文献类型：期刊论文
| 作者 | Tong,Xiaochu4,5; Wang,Dingyan4,5; Ding,Xiaoyu4,5; Tan,Xiaoqin4,5; Ren,Qun3,5; Chen,Geng2,4,5; Rong,Yu1; Xu,Tingyang1; Huang,Junzhou1; Jiang,Hualiang4,5
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| 刊名 | Journal of Cheminformatics
 |
| 出版日期 | 2022-07-07 |
| 卷号 | 14期号:1 |
| 关键词 | Blood–brain barrier penetration BBBp prediction Uncertainty estimation |
| DOI | 10.1186/s13321-022-00619-2 |
| 通讯作者 | Zheng,Mingyue(myzheng@simm.ac.cn) ; Li,Xutong(lixutong@simm.ac.cn) |
| 英文摘要 | AbstractBlood–brain barrier is a pivotal factor to be considered in the process of central nervous system (CNS) drug development, and it is of great significance to rapidly explore the blood–brain barrier permeability (BBBp) of compounds in silico in early drug discovery process. Here, we focus on whether and how uncertainty estimation methods improve in silico BBBp models. We briefly surveyed the current state of in silico BBBp prediction and uncertainty estimation methods of deep learning models, and curated an independent dataset to determine the reliability of the state-of-the-art algorithms. The results exhibit that, despite the comparable performance on BBBp prediction between graph neural networks-based deep learning models and conventional physicochemical-based machine learning models, the GROVER-BBBp model shows greatly improvement when using uncertainty estimations. In particular, the strategy combined Entropy and MC-dropout can increase the accuracy of distinguishing BBB?+?from BBB???to above 99% by extracting predictions with high confidence level (uncertainty score? |
| 语种 | 英语 |
| 出版者 | Springer International Publishing |
| WOS记录号 | BMC:10.1186/S13321-022-00619-2 |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/300834]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Zheng,Mingyue; Li,Xutong |
| 作者单位 | 1.Tencent AI Lab 2.Hangzhou Institute for Advanced Study, UCAS; School of Pharmaceutical Science and Technology 3.Nanjing University of Chinese Medicine 4.University of Chinese Academy of Sciences 5.Shanghai Institute of Materia Medica, Chinese Academy of Sciences; Drug Discovery and Design Center, State Key Laboratory of Drug Research |
| 推荐引用方式 GB/T 7714 | Tong,Xiaochu,Wang,Dingyan,Ding,Xiaoyu,et al. Blood–brain barrier penetration prediction enhanced by uncertainty estimation[J]. Journal of Cheminformatics,2022,14(1). |
| APA | Tong,Xiaochu.,Wang,Dingyan.,Ding,Xiaoyu.,Tan,Xiaoqin.,Ren,Qun.,...&Li,Xutong.(2022).Blood–brain barrier penetration prediction enhanced by uncertainty estimation.Journal of Cheminformatics,14(1). |
| MLA | Tong,Xiaochu,et al."Blood–brain barrier penetration prediction enhanced by uncertainty estimation".Journal of Cheminformatics 14.1(2022). |
入库方式： OAI收割
来源：上海药物研究所
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