In Silico Prediction Of The Complex P-Glycoprotein Substrate Efflux Using A Novel Machine Learning Scheme
Permeability glycoprotein (P-gp), which belongs to the ATP-binding cassette (ABC) superfamily of membrane-bound transporters, can actively transport a wide range of structurally and mechanistically diverse endogenous and xenobiotic chemical agentsout of the cells or blood–brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites.A nonlinear quantitative structure–activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set and test set. When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR.This accurate and predictive HSVR model can be employed to predict P-pg substrate efflux ratioto facilitate drug discovery and development.
Keywords - P-gp, promiscuity,machine learning,hierarchical support vector regression.