Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study

doi:10.1186/s13065-024-01324-x

	Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study
	Pitakbut, Thanet 1,3,4; Munkert, Jennifer 1,2; Xi, Wenhui 3,4; Wei, Yanjie 3,4; Fuhrmann, Gregor 1,2
	2024-12-20
发表期刊	BMC CHEMISTRY
卷号	18 期号:1 页码:16
摘要	In virtual drug screening, consensus docking is a standard in-silico approach consisting of a combined result from optimized docking experiments, a minimum of two results combination. Therefore, consensus docking is subjected to a lower success rate than the best docking method due to its mathematical nature, an unavoidable limitation. This study aims to overcome this drawback via random forest, an ensemble machine learning model. First, in vitro beta-lactamase inhibitory screening was performed using an in-house chemical library. The in vitro results were later used as a validation. Consequently, we optimized docking protocols for AutoDock Vina and DOCK6 programs. With an appropriate scoring function, we found that DOCK6 could identify up to 70% of all active molecules, double the inappropriate. Further consensus analysis reduced the success rate to 50%. Simultaneously, a false positive rate was down to 16%, which was experimentally favorable for a drug search. Finally, we trained two quantitative structure-activity relationship (QSAR) models using logistic regression as a reference model and a random forest as a test model. After combining consensus docking results, random forest-based QSAR outperformed a logistic regression by restoring the success rate to 70% and maintaining a low false positive rate of around 21%. In conclusion, this study demonstrated the benefit of using a random forest (machine learning)-based QSAR model to overcome a standard consensus docking limitation in beta-lactamase inhibitor search as a proof-of-concept.
关键词	Molecular docking Consensus docking Random forest-based QSAR model Beta-lactamase inhibitory screening
DOI	10.1186/s13065-024-01324-x
收录类别	SCI
语种	英语
资助项目	Friedrich-Alexander-Universitt Erlangen-Nrnberg (1041)
WOS研究方向	Chemistry
WOS类目	Chemistry, Multidisciplinary
WOS记录号	WOS:001380721400001
出版者	BMC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41061
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Fuhrmann, Gregor
作者单位	1.Friedrich Alexander Univ Erlangen Nurnberg, Dept Biol, Pharmaceut Biol, Staudtstr 5, D-91058 Erlangen, Germany 2.FAU NeW Res Ctr New Bioact Cpds, Nikolaus Fiebiger Str 10, D-91058 Erlangen, Germany 3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab Intelligent Bioinformat, Shenzhen 518055, Peoples R China 4.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr High Performance Comp, Shenzhen 518055, Peoples R China
推荐引用方式 GB/T 7714	Pitakbut, Thanet,Munkert, Jennifer,Xi, Wenhui,et al. Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study[J]. BMC CHEMISTRY,2024,18(1):16.
APA	Pitakbut, Thanet,Munkert, Jennifer,Xi, Wenhui,Wei, Yanjie,&Fuhrmann, Gregor.(2024).Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study.BMC CHEMISTRY,18(1),16.
MLA	Pitakbut, Thanet,et al."Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study".BMC CHEMISTRY 18.1(2024):16.