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Articles

Impact of geometry optimization methods on QSAR modelling: A case study for predicting human serum albumin binding affinity

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Pages 491-509 | Received 20 Apr 2017, Accepted 09 Jun 2017, Published online: 14 Jul 2017
 

Abstract

Quantitative structure–activity relationship (QSAR) modelling is a major tool employed in the prediction of various endpoints. However, current QSAR literature is missing a full understanding of the impact of quantum chemical calculation methods on the estimation of molecular descriptors and model performance. Here, we provide a comprehensive analysis of the quantitative effects of different geometry optimization methods (semi-empirical, ab initio Hartee-Fock and density functional theory) on the molecular descriptors. Using experimental binding affinity to human serum albumin (HSA) data, we comparatively investigated the influence of employing descriptors derived from three calculation methods on the QSAR models. We propose a 4-descriptor QSAR model in line with the OECD validation principles for the prediction of drug binding affinity to HSA (log KHSA) as a potential tool for drug development. We also confirm the prediction capability of the proposed model on a heterogeneous external set of chemicals. Furthermore, we recommend an activity-independent rational approach for the selection of geometry optimization method for an improved QSAR model development.

Acknowledgements

The authors express their gratitude to P. Gramatica (University of Insubria, Varese, Italy) for providing the QSARINS v. 2.2.1 software.

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