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Application of the “EigenValue Analysis (EVANS)” Methodology to Build Quantitative Structure Pharmacokinetic Relationship Models
preprintsubmitted on 12.01.2021, 11:51 and posted on 13.01.2021, 08:55 by Anish Gomatam, Blessy Joseph, Mushtaque S. Shaikh, Poonam Advani, Evans C. Coutinho
We present EigenValue ANalySis (EVANS), a QSPR methodology that considers 3D molecular information of enantiomeric ensembles of chiral molecules without the need to perform an alignment step. EVANS follows an intricate molecular modelling protocol that generates orthogonal eigenvalues from hybrid matrices of physicochemical properties and 3D structure; these eigenvalues are used as independent variables in QSPR analyses. The EVANS formalism has been presented and deployed to build quantitative structure pharmacokinetic relationship (QSPKR) models on a benchmark dataset for three critical PK parameters: steady-state volume of distribution (VDss), clearance (CL), and half-life (t1/2). Predictive QSPKR models were built by using the eigenvalues generated via the EVANS methodology in conjunction with multiple linear regression (MLR), random forest (RF), and support vector machine (SVM) algorithms, and it was observed that the EVANS QSPKR models sync with published work in the literature. Thus, we present the EVANS methodology as a first-line prediction tool to prioritise compounds in drug discovery and development.