QSAR and its Role in Target-Ligand Interaction

Abstract

Each molecule has its own specialty, structure and function and when these molecules are combined together they form a compound. Structure and function of a molecule are related to each other and QSARs (Quantitative StructureActivity relationships) are based on the criteria that the structure of a molecule must contain the features responsible for its physical, chemical, and biological properties, and on the ability to represent the chemical by one, or more, numerical descriptor(s). By QSAR models, the biological activity of a new or untested chemical can be inferred from the molecular structure of similar compounds whose activities have already been assessed. QSARs attempt to relate physical and chemical properties of molecules to their biological activities. For this there are so many descriptors (for example, molecular weight, number of rotatable bonds, Log P) and simple statistical methods such as Multiple Linear Regression (MLR) are used to predict a model. These models describe the activity of the data set and can predict activities for further sets of (untested) compounds. These types of descriptors are simple to calculate and allow for a relatively fast analysis. 3D-QSAR uses probe-based sampling within a molecular lattice to determine three-dimensional properties of molecules (particularly steric and electrostatic values) and can then correlate these 3D descriptors with biological activity. Physicochemical descriptors, include hydrophobicity, topology, electronic properties, and steric effects etc. These descriptors can be calculated empirically, statistically or through more recent computational methods. QSARs are currently being applied in many disciplines, with many pertaining to drug design and environmental risk assessment.

Antiproliferative potential, quantitative structure-activity relationship, cheminformatic and molecular docking analysis of quinoline and benzofuran derivatives

“…The quantitative structure activity relationship analysis of each unique molecule helps to synchronize the compounds with their biological activities as a physical and chemical property [38]. A QSAR modelling involving multiple linear regressions (MLR) analysis was applied to screen potential lead of quinoline and benzofuran analogues against HCT-116 cells.…”

Current computational methods for predicting protein interactions of natural products

“…Since its origin in the 1962 seminal paper of Hansch et al [65], quantitative structure—activity relationship (QSAR) has been one of the main computational methods applied in medicinal chemistry [66]. QSAR attempts to build mathematical models which quantitatively correlate structural properties of substances and their biological activities using statistical analysis such as multiple linear regression (MLR), partial least-squares (PLS), k-nearest neighbors (kNN), etc [67].…”

Development of 3D QSAR based pharmacophore model for neuraminidase in Infl uenza A Virus

“…The Fisher randomization test used for testifying and validating Hypo 1 indicates that this pharmacophore model does not occur due to the random correlation (Singh and Singh, 2013). The experimental activities of the training set were picked randomly and the resulting training set was used in HypoGen with the parameters chosen for the original pharmacophore generation.…”