3D-QSAR in Drug Design - A Review

Abstract

Quantitative structure-activity relationships (QSAR) have been applied 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 (Hansch analysis) or with certain structural features (Free-Wilson analysis) 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. Although the trial and error factor involved in the development of a new drug cannot be ignored completely, QSAR certainly decreases the number of compounds to be synthesized by facilitating the selection of the most promising candidates. Several success stories of QSAR have attracted the medicinal chemists to investigate the relationships of structural properties with biological activity. This review seeks to provide a bird's eye view of the different 3D-QSAR approaches employed within the current drug discovery community to construct predictive structure-activity relationships and also discusses the limitations that are fundamental to these approaches, as well as those that might be overcome with the improved strategies. The components involved in building a useful 3D-QSAR model are discussed, including the validation techniques available for this purpose.

Synthesis, Biological Evaluation, and 3D QSAR Study of 2-Methyl-4-oxo-3-oxetanylcarbamic Acid Esters as N-Acylethanolamine Acid Amidase (NAAA) Inhibitors

“…Eventually, we decided to use six latent vectors meeting the generally accepted threshold of one latent vector for every five compounds employed in the training set. 40 In this way, our model showed a good R 2 coefficient between the experimental pIC 50 (pIC 50exp ) and the predicted pIC 50 (pIC 50pred ) (R 2 = 0.93, see Figure 2B) and a good predictivity with a Q 2 = 0.69 (see Figure S1). In Table 3, the experimental, predicted, and leave-one-out cross-validated pIC 50 values for the training set are reported.…”

Synthesis, antibacterial, and antifungal activities of new drimane sesquiterpenoids with azaheterocyclic units

“…The common substructure has been extensively applied as a base for molecular alignment (Verma et al 2010;Damale et al 2014). However, better results can be obtained when the 3D-QSAR models could be built and verified on the active conformations of training and test set compounds, in particular when similar ligands occupy different binding poses in the binding site (Urniaż and Jóźwiak 2013).…”

Structural insights into mode of actions of novel natural Mycobacterium protein tyrosine phosphatase B inhibitors

“…This model serves as a valuable tool for the design of new chemical entities and to predict their activity. The QSAR model so developed facilitates identification of promising lead candidates, thus decreasing the number of compounds required to be synthesized and tested in vitro [11]. …”