GRid-INdependent Descriptors (GRIND):  A Novel Class of Alignment-Independent Three-Dimensional Molecular Descriptors

Abstract

Traditional methods for performing 3D-QSAR rely upon an alignment step that is often time-consuming and can introduce user bias, the resultant model being dependent upon and sensitive to the alignment used. There are several methods which overcome this problem, but in general the necessary transformations prevent a simple interpretation of the resultant models in the original descriptor space (i.e. 3D molecular coordinates). Here we present a novel class of molecular descriptors which we have termed GRid-INdependent Descriptors (GRIND). They are derived in such a way as to be highly relevant for describing biological properties of compounds while being alignment-independent, chemically interpretable, and easy to compute. GRIND are obtained starting from a set of molecular interaction fields, computed by the program GRID or by other programs. The procedure for computing the descriptors involves a first step, in which the fields are simplified, and a second step, in which the results are encoded into alignment-independent variables using a particular type of autocorrelation transform. The molecular descriptors so obtained can be used to obtain graphical diagrams called "correlograms" and can be used in different chemometric analyses, such as principal component analysis or partial least-squares. An important feature of GRIND is that, with the use of appropriate software, the original descriptors (molecular interaction fields) can be regenerated from the autocorrelation transform and, thus, the results of the analysis represented graphically, together with the original molecular structures, in 3D plots. In this respect, the article introduces the program ALMOND, a software package developed in our group for the computation, analysis, and interpretation of GRIND. The use of the methodology is illustrated using some examples from the field of 3D-QSAR. Highly predictive and interpretable models are obtained showing the promising potential of the novel descriptors in drug design.

Combining Computational and Biochemical Studies for a Rationale on the Anti‐Aromatase Activity of Natural Polyphenols

“…The use of GRIND [18] descriptors calculated with the GRID force field and extracted from relevant MIFs (hydrophobicity, hydrogen bonding, and molecular shape) was a valid strategy to characterize a virtual receptor site and gain further insight into the molecular environment surrounding polyphenol inhibitors on the active site of aromatase. Variables with a direct high impact on the inhibitory activity (Figure 4) defined a putative virtual receptor site ( Figure 6): two hydrogen bond acceptor regions are expected to be close to C5 and C7 of flavone ring A, and a hydrogen bond donor/acceptor area close to C4' of ring B. C3' was found to be permissive to bulky substitutions, allowing good shape complementarity with the active site.…”

“…GRIND descriptors [18] were calculated for the flavones studied with ALMOND [21] v 3.3 using MIFs [19] computed with the chemical probes DRY (hydrophobic probe), O (hydrogen bond acceptor interactions), and N1 (hydrogen bond donor inter-actions), and the molecular shape probe TIP, [24] with the grid spacing set to 0.5 . These descriptors encode geometrical relationships between pairs of non-bonded interactions; therefore, to represent only the highly favorable interactions, one initial filtering step was performed, and 100 low-energy interaction points, separated from each other as much as possible, were extracted from each MIF.…”

“…[18] The bioactive conformation of the flavones on the active site of aromatase is unknown. Therefore, the minimum-energy conformation, obtained by a conformational search described in detail in the Experimental Section, was chosen as representative.…”

“…Maximum auto and cross-covariance (MACC2) [18] energy products and grid-filtered MIFs are shown in Figure 5 for correlograms O-O, O-N1, and O-TIP obtained with the most active flavone 5 and flavone 12, a weak inhibitor. The three most relevant variables with direct impact on biological activity (OO-9, ON-43, and OT-14) are indicated with an arrow in the MACC2 profile, and the pairs of points connected by a line in the filtered MIFs.…”

“…[18] Based on these preliminary data, molecular interaction fields (MIFs) [19] were translated into the main relevant non-bonded interactions expected to occur in the process of enzyme-inhibitor recognition and binding, allowing us to obtain a representation of the putative active site. Finally, this virtual receptor site was compared with the active site structure of a homology model of the enzyme.…”