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

作者：Dixon Steven L; Duan Jianxin; Smith Ethan; Von Bargen Christopher D; Sherman Woody; Repasky Matthew P

来源：Future Medicinal Chemistry, 2016, 8(15): 1825-1839.

DOI：10.4155/fmc-2016-0093

<jats:p> Aim: We introduce AutoQSAR, an automated machine-learning application to build, validate and deploy quantitative structure–activity relationship (QSAR) models. Methodology/results: 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. Conclusion: 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. </jats:p>

• 出版日期2016-10