AutoQSAR: an automated machine learning tool for best-practice quantitative structure-activity relationship modeling

We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workfl...

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Bibliographic Details
Published in:Future medicinal chemistry 2016-10, Vol.8 (15), p.1825-1839
Main Authors: Dixon, Steven L, Duan, Jianxin, Smith, Ethan, Von Bargen, Christopher D, Sherman, Woody, Repasky, Matthew P
Format: Article
Language:English
Subjects:
binding affinity prediction
blood-brain barrier permeability
carcinogenicity
fish bioconcentration factor
mutagenicity
QSAR
solubility
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Summary:We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure-activity relationship (QSAR) models. The process of descriptor generation, feature selection and the creation of a large number of QSAR models has been automated into a single workflow within AutoQSAR. The models are built using a variety of machine-learning methods, and each model is scored using a novel approach. Effectiveness of the method is demonstrated through comparison with literature QSAR models using identical datasets for six end points: protein-ligand binding affinity, solubility, blood-brain barrier permeability, carcinogenicity, mutagenicity and bioaccumulation in fish. AutoQSAR demonstrates similar or better predictive performance as compared with published results for four of the six endpoints while requiring minimal human time and expertise.
ISSN:1756-8919
1756-8927
DOI:10.4155/fmc-2016-0093