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

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 K_HSA) 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.

Keywords:

• QSAR
• geometry optimization
• DFT
• DRAGON descriptors
• human serum albumin binding

Acknowledgements

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