Identification of Potent and Selective Acetylcholinesterase/Butyrylcholinesterase Inhibitors by Virtual Screening.
Identification of Potent and Selective Acetylcholinesterase/Butyrylcholinesterase Inhibitors by Virtual Screening.
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DOI：
10.1021/acs.jcim.3c00230
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发表时间：
2023-04-24
影响因子：
5.6
通讯作者：
Huang, Ruili
中科院分区：
文献类型：
--
作者：
Xu, Tuan;Li, Shuaizhang;Li, Andrew J.;Zhao, Jinghua;Sakamuru, Srilatha;Huang, Wenwei;Xia, Menghang;Huang, Ruili
Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) play important roles in human neurodegenerative disorders such as Alzheimer's disease. In this study, machine learning methods were applied to develop quantitative structure-activity relationship (QSAR) models for the prediction of novel AChE and BChE inhibitors based on data from quantitative high-throughput screening (qHTS) assays. The models were used to virtually screen an in-house collection of ~360K compounds. The optimal models achieved good performance with area under the receiver operating characteristic curve (AUC-ROC) values ranging from 0.83 ± 0.03 to 0.87 ± 0.01 for the prediction of AChE/BChE inhibition activity and selectivity. Experimental validation showed that the best performing models increased the assay hit rate by several folds. We identified 88 novel AChE and 126 novel BChE inhibitors, 25% (AChE) and 53% (BChE) of which showed potent inhibitory effects (IC50 < 5 μM). In addition, structure-activity relationship (SAR) analysis of the BChE inhibitors revealed scaffolds for chemistry design and optimization. In conclusion, machine learning models were shown to efficiently identify potent and selective inhibitors against AChE and BChE, and novel structural series for further design and development of potential therapeutics against neurodegenerative disorders.
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DOI：
10.2174/1875397301004010057
发表时间：
2010-10-21
期刊：
Current chemical genomics
影响因子：
--
作者：
Wang Y;Jadhav A;Southal N;Huang R;Nguyen DT
通讯作者：
Nguyen DT
DOI：
10.3390/molecules23051038
发表时间：
2018-04-28
期刊：
Molecules (Basel, Switzerland)
影响因子：
--
作者：
Ramírez D;Caballero J
通讯作者：
Caballero J
影响因子：
4.1
作者：
Xu, Tuan;Wu, Leihong;Xia, Menghang;Simeonov, Anton;Huang, Ruili
通讯作者：
Huang, Ruili
影响因子：
4.1
作者：
Richard AM;Huang R;Waidyanatha S;Shinn P;Collins BJ;Thillainadarajah I;Grulke CM;Williams AJ;Lougee RR;Judson RS;Houck KA;Shobair M;Yang C;Rathman JF;Yasgar A;Fitzpatrick SC;Simeonov A;Thomas RS;Crofton KM;Paules RS;Bucher JR;Austin CP;Kavlock RJ;Tice RR
通讯作者：
Tice RR
影响因子：
120.1
作者：
Bachovchin, Daniel A.;Cravatt, Benjamin F.
通讯作者：
Cravatt, Benjamin F.