Predicting toxicity of endocrine disruptors and blood–brain barrier permeability using chirality-sensitive descriptors and machine learning

Abstract

Estrogen receptor (ER) mediated endocrine disruption and blood–brain barrier (BBB) permeability are two crucial pharmacological endpoints that must be assessed for any drug candidate. However, experimental testing is expensive and time-consuming, and in recent years, Quantitative Structure-Property Relationships (QSPRs) have emerged as a viable in silico alternative. However, most QSPR models reported on ER toxicity and BBB permeability have been carried out using 2D descriptors, whereas it has been established that ER binding and BBB permeability are stereoselective processes in which the spatial arrangement of atoms in the molecule plays a key role. The current study addresses this problem using a chirality-sensitive 3D-QSPR methodology entitled ‘EigenValue ANalysiS (EVANS). The EVANS approach merges information from 3D molecular structure with 2D physicochemical properties to generate eigenvalues which are used as descriptors in QSPR modelling. For chiral compounds, EVANS computes descriptors by considering distance attributes from a plethora of enantiomeric states, thereby accounting for the contributions of multiple conformers towards a particular biological endpoint. We deploy the EVANS methodology with machine learning algorithms to build predictive QSPR models for estrogen receptor (ER) mediated endocrine disruption and BBB permeability. Regression analyses of ER binding on a dataset of 132 chemical entities returned a robust and predictive model, with the support vector machine model having rtrain2=0.84 and rtest2=0.70. Classification models for BBB permeability on a dataset of 607 chemicals also showed high prediction accuracy, with the artificial neural network model showing the best performance (Accuracy = 0.85, AUC = 0.82, precision = 0.85, F1 score = 0.89). For comparison, conventional 2D-QSPR models were also built for these endpoints, and it was observed that EVANS generates eigenvalues that are superior to descriptors used in standard 2D-QSPR. Overall, our results demonstrate that EVANS is a powerful 3D-QSPR methodology that offers several advantages over existing QSAR/QSPR methods, and can be a useful computational tool in the pharmacological and toxicological evaluation of new and existing drugs.