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Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors

文献类型：期刊论文

作者Wang, Dong1; Wu, Zhenxing1; Shen, Chao1; Bao, Lingjie1; Luo, Hao1; Wang, Zhe1; Yao, Hucheng2; Kong, De-Xin2; Luo, Cheng2,3; Hou, Tingjun1
刊名BRIEFINGS IN BIOINFORMATICS
出版日期2022-12-27
页码14
ISSN号1467-5463
关键词uncertainty estimation graph neural network KDM1A LSD1 inhibitors upper confidence bound method virtual screening
DOI10.1093/bib/bbac592
通讯作者Kong, De-Xin(dxkong@mail.hzau.edu.cn) ; Luo, Cheng(cluo@simm.ac.cn) ; Hou, Tingjun(tingjunhou@zju.edu.cn)
英文摘要Machine learning including modern deep learning models has been extensively used in drug design and screening. However, reliable prediction of molecular properties is still challenging when exploring out-of-domain regimes, even for deep neural networks. Therefore, it is important to understand the uncertainty of model predictions, especially when the predictions are used to guide further experiments. In this study, we explored the utility and effectiveness of evidential uncertainty in compound screening. The evidential Graphormer model was proposed for uncertainty-guided discovery of KDM1A/LSD1 inhibitors. The benchmarking results illustrated that (i) Graphormer exhibited comparative predictive power to state-of-the-art models, and (ii) evidential regression enabled well-ranked uncertainty estimates and calibrated predictions. Subsequently, we leveraged time-splitting on the curated KDM1A/LSD1 dataset to simulate out-of-distribution predictions. The retrospective virtual screening showed that the evidential uncertainties helped reduce false positives among the top-acquired compounds and thus enabled higher experimental validation rates. The trained model was then used to virtually screen an independent in-house compound set. The top 50 compounds ranked by two different ranking strategies were experimentally validated, respectively. In general, our study highlighted the importance to understand the uncertainty in prediction, which can be recognized as an interpretable dimension to model predictions.
WOS关键词REVERSIBLE INHIBITORS ; APPLICABILITY DOMAIN ; DRUG DISCOVERY ; PREDICTION ; DESIGN ; QUANTIFICATION ; TUTORIAL
资助项目National Key R&D Program of China[2021YFE0206400] ; National Natural Science Foundation of China[22220102001] ; Natural Science Foundation of China of Zhejiang Province[LD22H300001] ; Natural Science Foundation of China of Zhejiang Province[LZ19H300001]
WOS研究方向Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种英语
出版者OXFORD UNIV PRESS
WOS记录号WOS:000904614600001
源URL[http://119.78.100.183/handle/2S10ELR8/304174]  
专题中国科学院上海药物研究所
通讯作者Kong, De-Xin; Luo, Cheng; Hou, Tingjun
作者单位1.Zhejiang Univ, Coll Pharmaceut Sci, Hangzhou, Peoples R China
2.Huazhong Agr Univ, Coll Informat, Wuhan, Peoples R China
3.Shanghai Inst Mat Med, Shanghai, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Dong,Wu, Zhenxing,Shen, Chao,et al. Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors[J]. BRIEFINGS IN BIOINFORMATICS,2022:14.
APA Wang, Dong.,Wu, Zhenxing.,Shen, Chao.,Bao, Lingjie.,Luo, Hao.,...&Hou, Tingjun.(2022).Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors.BRIEFINGS IN BIOINFORMATICS,14.
MLA Wang, Dong,et al."Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors".BRIEFINGS IN BIOINFORMATICS (2022):14.

入库方式： OAI收割

来源：上海药物研究所

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