The EVA spectral descriptor

Abstract

− The EVA descriptor is derived from fundamental IR-and Raman range molecular vibrational frequencies. EVA is sensitive to 3D structure but has an advantage over field-based 3D-QSAR methods inasmuch as it is invariant to both translation and rotation of the structures concerned and thus structural superposition is not required. The latter property and the demonstration of the effectiveness of the descriptor for QSAR means that EVA has been the subject of a great deal of interest from the modelling community. This review describes the derivation of the descriptor, details its main parameters and how to apply them, and provides an overview of the validation that has been done with the descriptor. A recent enhancement to the technique is described which involves the localised adjustment of variance in such a way that enhanced internal and external predictivity may be obtained. Despite the statistical quality of EVA QSAR models the main draw-back to the descriptor at present is the difficulty associated with back-tracking from a PLS model to an EVA pharmacophore. Brief comment is made on the use of the EVA descriptor for diversity studies and the similarity searching of chemical structure databases.

Distance Profiles (DiP): A translationally and rotationally invariant 3D structure descriptor capturing steric properties of molecules

“…Bottom: Conformation similar to that found in the crystal structure by Stein and co-workers [35]. minimization using MMFF94 and a dielectric constant of 3.0) were performed on five compounds (1,5,9,14,17) to sample the low energy conformation space. All three bonds connecting the two rings were set to be rotateable in the search.…”

“…The structures are given in Table 2 . This data set was first studied by Krystek and co-workers using COMFA [33], and in the following by several authors with various 3D-QSAR techniques [9,11,34]. All structures were built as described in the original paper [33].…”

“…The first class comprises techniques such as CoMFA [2], CoMSIA [3], HASL [4], Compass [5] which depend on the position and the orientation of the molecules in space. The second class comprises descriptors such as Autocorrelation [6], CoMMA [7], EVA [8,9], WHIM [10], MS-WHIM [11], 3D-MoRSE [12], SESP [13], and GRIND [14] which are invariant to translation and rotation of the molecules. Translationally and rotationally invariant descriptors (TRIdescriptors) are independent of molecular orientation which is considered to be advantageous [7,9,11,14].…”

“…The second class comprises descriptors such as Autocorrelation [6], CoMMA [7], EVA [8,9], WHIM [10], MS-WHIM [11], 3D-MoRSE [12], SESP [13], and GRIND [14] which are invariant to translation and rotation of the molecules. Translationally and rotationally invariant descriptors (TRIdescriptors) are independent of molecular orientation which is considered to be advantageous [7,9,11,14]. Hence, a superposition of chosen conformers which is part of the alignment procedure is not necessary with TRI-descriptors.…”

Comparison of 2DSimilarity and 3DSuperposition. Application to Searching a Conformational Drug Database

“…7,8 But nevertheless even the accuracy of the prediction of one of six drug classes remains at 66%. 9 Descriptors representing 3D information 10,11 and pharmacophore based approaches 12,13 are opportunities to overcome the weaknesses of 2D descriptors. However a lot of experience and intuition has to be invested to achieve reasonable results.…”