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. Author manuscript; available in PMC: 2012 May 1.
Published in final edited form as: Medchemcomm. 2011 May;2(5):356–370. doi: 10.1039/C1MD00044F

Table 1. Overview of nD-QSAR approaches (3 ≤ n ≤ 7).

method description
CoMFA Probes placed on grid points in the 3D field around a molecule experience an interaction energy with the ligands that defines the molecular shape and electrostatic properties in the surrounding environment.
CoMSIA It expands CoMFA by including hydrophobic and hydrogen bonding contributions and calculates how these contributions are similar between molecules.
GRIND It eliminates alignment-dependency by using distances between 3D grid points. Highly relevant regions among a set of molecules are selected as nodes and the intensity of molecular interaction field at those nodes are used as descriptors. The program ALMOND provides tools to compute, analyze, and interpret the GRIND.
VolSurf Information on 3D grid voxels (shape, electrostatic, volume) are compressed into 2D numerical descriptors by image analysis tools.
4D-QSAR Multiple conformations in a grid box generate the occupancies at grid points, with those occupancies used as the descriptors.
5D-QSAR Multiple hypothetical binding pockets are generated around ligands based on a 3D grid and the receptor models are evolved by GA with the most favorable binding pocket model evaluated by relative free energy of ligand binding.
6D-QSAR It includes optimization of structures in aqueous solution and calculates solvation energy and charges by semi-empirical QM method, AMSOL86. Ligands' arrangement in pseudo-binding pocket is determined by MC simulation.