Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity

Abstract

An alternative approach is reported to compute property fields based on similarity indices of drug molecules that have been brought into a common alignment. The fields of different physicochemical properties use a Gaussian-type distance dependence, and no singularities occur at the atomic positions. Accordingly, no arbitrary definitions of cutoff limits and deficiencies due to different slopes of the fields are encountered. The fields are evaluated by a PLS analysis similar to the CoMFA formalism. Two data sets of steroids binding to the corticosteroid-binding-globulin and thermolysin inhibitors were analyzed in terms of the conventional CoMFA method (Lennard-Jones and Coulomb potential fields) and the new comparative molecular similarity indices analysis (CoMSIA). Models of comparative statistical significance were obtained. Field contribution maps were produced for the different models. Due to cutoff settings in the CoMFA fields and the steepness of the potentials close to the molecular surface, the CoMFA maps are often rather fragmentary and not contiguously connected. This makes their interpretation difficult. The maps obtained by the new CoMSIA approach are superior and easier to interpret. Whereas the CoMFA maps denote regions apart from the molecules where interactions with a putative environment are to be expected, the CoMSIA maps highlight those regions within the area occupied by the ligand skeletons that require a particular physicochemical property important for activity. This is a more significant guide to trace the features that really matter especially with respect to the design of novel compounds.

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

“…In 1994, Klebe and coworkers introduced the Comparative Molecular Similarity Indices Analysis (CoMSIA) approach, in which molecular fields are expressed in the form of Gaussian-type functions. To attempt an account of the entropic contribution to receptor-ligand binding, alongside steric and electrostatic fields CoMSIA considers also hydrophobic fields [21].…”

Molecular Modeling on Pyrimidine-Urea Inhibitors of TNF-α Production: An Integrated Approach Using a Combination of Molecular Docking, Classification Techniques, and 3D-QSAR CoMSIA

“…CoMSIA models were generated using the combination of the following similarity fields: steric, electrostatic, hydrophobic, H-bond donor and H-bond acceptor. 63 These fields illustrate the various properties into spatial locations where they play important roles in determining the inhibitory activity. The best CoMSIA model was derived using the steric, electrostatic, and H-bond acceptor fields (Table 6).…”

3D-QSAR studies of triazolopyrimidine derivatives of Plasmodium falciparum dihydroorotate dehydrogenase inhibitors using a combination of molecular dynamics, docking, and genetic algorithm-based methods

“…The CoMSIA [21] descriptors, namely, steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor, were generated using a sp3 carbon probe atom with +1.0 charge and a van der Waals radius of 1.4 Å. CoMSIA similarity indices (A F , K ) between a molecule j and atoms i at a grid point were calculated using Eq. 1 as follows:…”