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

Abstract

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.

Keywords: QSAR; binding affinity prediction; blood–brain barrier permeability; carcinogenicity; fish bioconcentration factor; mutagenicity; solubility.