期刊
FUTURE MEDICINAL CHEMISTRY
卷 8, 期 15, 页码 1825-1839出版社
FUTURE SCI LTD
DOI: 10.4155/fmc-2016-0093
关键词
binding affinity prediction; blood-brain barrier permeability; carcinogenicity; fish bioconcentration factor; mutagenicity; QSAR; solubility
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.
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