Conformation-dependent QSAR approach for the prediction of inhibitory activity of bromodomain modulators

Abstract

Epigenetic drug discovery is a promising research field with growing interest in the scientific community, as evidenced by the number of publications and the large amount of structure-epigenetic activity information currently available in the public domain. Computational methods are valuable tools to analyse and understand the activity of large compound collections from their structural information. In this manuscript, QSAR models to predict the inhibitory activity of a diverse and heterogeneous set of 88 organic molecules against the bromodomains BRD2, BRD3 and BRD4 are presented. A conformation-dependent representation of the chemical structures was established using the RDKit software and a training and test set division was performed. Several two-linear and three-linear QuBiLS-MIDAS molecular descriptors (www.tomocomd.com) were computed to extract the geometric structural features of the compounds studied. QuBiLS-MIDAS-based features sets, to be used in the modelling, were selected using dimensionality reduction strategies. The multiple linear regression procedure coupled with a genetic algorithm were employed to build the predictive models. Regression models containing between 6 to 9 variables were developed and assessed according to several internal and external validation methods. Analyses of outlier compounds and the applicability domain for each model were performed. As a result, the models against BRD2 and BRD3 with 8 variables and the model with 9 variables against BRD4 were those with the best overall performance according to the criteria accounted for. The results obtained suggest that the models proposed will be a good tool for studying the inhibitory activities of drug candidates against the bromodomains considered during epigenetic drug discovery.

Keywords:

• Applicability domain
• bromodomains
• epigenetic drug discovery
• 3D molecular descriptors
• QSAR
• QuBiLS-MIDAS
• TOMOCOMD-CARDD

Acknowledgements

CRGJ acknowledges the support from “Dirección General de Asuntos del Personal Académico” (DGAPA) for a postdoctoral fellowship at “Instituto de Química, Universidad Nacional Autónoma de México (UNAM)” in 2016–2017. The authors acknowledge F. Prieto-Martinez for assembling the data set used in this work.