Bioinformatics Computer Aided Drug Designing Molecular Dynamics


Pinterest LinkedIn Tumblr

Structure-activity methods that consider the 3D structure of modeled compounds in spatial relation to one another are collectively known as 3-dimensional QSAR (3D-QSAR) methods. These methods attempt to identify spatially-localized features across a series of molecules that correlate with activity, and show needs for ligand binding and complementarity to a postulated receptor binding site. These procedures extend the QSAR approach in 3 dimensions by choosing manually or automatically, one particular geometry for each modeled compound and using the molecular scaffold, the pharmacophore, and/or the molecular field method for superimposition.

Quantitative structure-activity relationships (QSAR) have been used for decades in the development of relationships between physicochemical properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. The fundamental principle underlying the formalism is that the difference in structural properties is responsible for the variations in biological activities of the compounds. In the classical QSAR studies, affinities of ligands to their binding sites, inhibition constants, rate constants, and other biological end points, with atomic, group or molecular properties such as lipophilicity, polarizability, electronic and steric properties or with certain structural features have been correlated. However such an approach has only a limited utility for designing a new molecule due to the lack of consideration of the 3D structure of the molecules. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free-Wilson approaches, which exploits the three-dimensional properties of the ligands to predict their biological activities using robust chemometric techniques such as PLS, G/PLS, ANN etc. It has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. 

3D-QSAR focuses on nondynamic molecular representations even though in reality molecular configurations are dynamic and their shape can usually significantly fluctuate depending on the molecular environment. The drug receptor interactions can further cause conformational changes of both the interacting moieties that allow them to further adopt their shapes, for example, induced fit effect. Molecular dynamics is used in 4D-QSAR for the mapping of the space available for molecular shapes. The enormous number of conformers exploring different spatial regions in this method and the likelihood of the formation of the common 3D-patterns of a series of molecules is sought after to increase the chances for mapping a proper pharmacophore. 

The underlying assumptions of 3D-QSAR methods are as follows:

  • The modeled compound, and not its metabolites or other transformation products, is responsible for the biological effect.
  • The proposed or modeled conformation is the bioactive one.
  • All compounds are binding in the same way to the same site.
  • The biological activity is largely explained by enthalpic processes (steric, electrostatic, hydrogen bonding, etc.).
  • Entropic terms are similar for all compounds.
  • The system is at equilibrium.
  • Common solvent effects diffusion, transport, etc. apply to the studied molecules and thus are not considered.

In general, the objective of the 3D-QSAR procedures is to place molecules with common alignments in a 3D grid (or region), calculate interaction values for each grid point, and place the values for each point in a QSAR table. Then create an equation, based on PLS regression, to describe the relationship between the values and the reported activities, verify the predictive ability of the QSAR by cross-validation (and determine the optimal number of components), visualize the final QSAR model by plotting coefficients in the corresponding regions of space, and use the final QSAR equation to estimate the biological activity for other new compounds not included in the model.

Write A Comment