Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets

Abstract Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure–activity relationships (QSARs), there is no agreed single best algorithm for QSAR learning. Therefore, a comprehensive understanding of the performance characteristics of popular M...

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Veröffentlicht in:	Briefings in bioinformatics Jg. 22; H. 4
Hauptverfasser:	Wu, Zhenxing, Zhu, Minfeng, Kang, Yu, Leung, Elaine Lai-Han, Lei, Tailong, Shen, Chao, Jiang, Dejun, Wang, Zhe, Cao, Dongsheng, Hou, Tingjun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Oxford University Press 01.07.2021 Oxford Publishing Limited (England)
Schlagworte:	Algorithms Artificial neural networks Computer applications Datasets Gaussian process Learning algorithms Learning theory Least squares method Linear functions Machine learning Neural networks Physicochemical properties Predictions Principal components analysis Radial basis function Regression analysis Spline functions Structure-activity relationships Support vector machines Toxicity ensemble learning support vector machine machine learning QSAR XGBoost
ISSN:	1467-5463, 1477-4054, 1477-4054
Online-Zugang:	Volltext
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