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3D perspective into MIA ‐ QSAR : A case for anti‐ HCV agents
Author(s) -
Daré Joyce K.,
Ramalho Teodorico C.,
Freitas Matheus P.
Publication year - 2019
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.13440
Subject(s) - quantitative structure–activity relationship , perspective (graphical) , computer science , molecular descriptor , chemistry , computational biology , machine learning , artificial intelligence , biology
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 to 6D. 2D and 3D techniques are among 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.