3D perspective into MIA‐QSAR: A case for anti‐HCV agents

This study reports an attempt to incorporate conformational information in MIA‐QSAR to become it a three‐dimensional method. The geometry optimization and alignment steps to generate 2D‐image projections from 3D‐chemical structures was successful, but the QSAR model obtained traditionally was better. Because image superposition of the flexible structures does not allow a perfectly aligned perspective of the congeneric moieties, it follows that connectivity of atoms, as well as atomic properties, are still more encoding molecular descriptors in MIA‐QSAR than conformational fingerprints.

Abstract

Quantitative Structure‐Activity Relationship (QSAR) is a molecular modeling technique widely used in the discovery of novel drugs. Currently, there are many approaches for performing such analysis, which are commonly classified from 1D‐6D. 2D and 3D techniques are amongst the most exploited ones. Multivariate Image Analysis applied to QSAR (MIA‐QSAR) is an example of 2D methodology that has presented a satisfactory performance in the generation of effective prediction models for biological/physicochemical properties. However, once this is a 2D method, conformational information is not explicitly considered, despite the well‐known role of such type of information in explaining the biochemical behavior. Thus, the importance of conformation is undeniable, but the requirement of this information for QSAR analysis still needs to be studied. Therefore, this work aimed to provide a method for encoding 3D information into MIA‐QSAR descriptors and analyze the consequences of this inclusion on this methodology. The strategy consisted in fully optimizing the molecular geometries of anti‐HCV compounds and three‐dimensionally align them before performing the MIA‐QSAR procedure. As a result, it was possible to verify that this type of information does not improve the MIA‐QSAR modeling performance; instead, the traditional procedure consisting of maximally congruent substructures generated a more reliable prediction model.

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