C-13 NMR quantitative spectrometric data-activity relationship (QSDAR) models of steroids binding the aromatase enzyme

Five quantitative spectroscopic data-activity relationships (QSDAR) models for 50 steroidal inhibitors binding to aromatase enzyme have been developed based on simulated C-13 nuclear magnetic resonance (NMR) data. Three of th e models were based on comparative spectral analysis (CoSA), and the two ot her models were based on comparative structurally assigned spectral analysi s (CoSASA). A CoSA QSDAR model based on five principal components had an ex plained variance (r(2)) of 0.78 and a leave-one-out (LOO) cross-validated v ariance (q(2)) of 0.71. A CoSASA model that used the assigned C-13 NMR chem ical shifts from a steroidal backbone at five selected positions gave an r( 2) of 0.75 and a q2 of 0.66. The C-13 NMR chemical shifts from atoms in the steroid template position 9, 6, 3, and 7 each had correlations greater tha n 0.6 with the relative binding activity to the aromatase enzyme. All five QSDAR models had explained and cross-validated variances that were better t han the explained and cross-validated variances from a five structural para meter quantitative structure-activity relationship (QSAR) model of the same compounds. QSAR modeling suffers from errors introduced by the assumptions and approximations used in partial charges, dielectric constants, and the molecular alignment process of one structural conformation. One postulated reason that the variances of QSDAR models are better than the QSAR models i s that C-13 NMR spectral data, based on quantum mechanical principles, are more reflective of binding than the QSAR model's calculated electrostatic p otentials and molecular alignment process. The QSDAR models provide a rapid , simple way to model the steroid inhibitor activity in relation to the aro matase enzyme.