Modeling of Activity of Efavirenz with the Mutant of HIV Reverse Transcriptase using Variable Connectivity Indices

Abstract

A model for estimation of activity for efavirenz analogues with the K 103N mutant of HIV-1 RT was proposed in this work. The new model is predictive that requires only variable connectivity indices in the calculations. Compared with the existing models based on Monte Carlo simulation, the new model is easier to apply with better predictive accuracy, giving an r 2 of 0.85, cross-validated Q 2 of 0.82 and average error of only 0.23 kcal/mol for the 47 efavirenz analogues concerned. This work also demonstrates that variable connectivity indices, a class of not widely recognized structural descriptors, are quite useful in the QSAR studies in the fields of pharmaceutics and biochemistry. These topological indices may play a more important role in these fields in future.

Variable Connectivity Index as a Tool for Modeling Structure-Property Relationships

“…Recently, in addition to the use of variable connectivity indices [35][36][37][38][39][40][41][42][43], several different flexible molecular descriptors have been considered in the literature, including the use of variable paths weights [44,45], variable distance related indices [46,47], and other variable descriptors [48]. Thus, we may well be at the beginning of a novel direction in structure-property-activity studies that will be dominated by the use of variable molecular descriptors that not only lead to better regressions with fewer molecular descriptors in comparison with regressions using "fixed" descriptors, but, because variable descriptors offer novel interpretation to the descriptors [49], may also offer guidance in the refinement of molecular models, as will be explained later in this article.…”