3D-QSAR in drug design--a review.

By Jitender Verma, Vijay M. Khedkar, Evans C. Coutinho. Current Topics in Medicinal Chemistry, 2010

722 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology, Organic Chemistry

View Paper: https://www.semanticscholar.org/paper/b63d50b7ffbebc612e658af563d2228d666e0841

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Citations in the corpus

In silico methods and tools for drug discovery (2021, methodology): “Recently, 3D-QSAR approaches have been developed as an extension of the classical QSAR methods to overcome their limitations [115].”
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles (2021, background): “…2018, Klicpera et al., 2019, Axelrod and Gomez-Bombarelli, 2020b, Schütt et al., 2021] such as charge distribution, potential energy, docking poses [McGann, 2011], shape similarity [Kumar and Zhang, 2018], pharmacophore searching [Schwab, 2010], or descriptors for 3/4D QSAR [Verma et al., 2010].”
Virtual screening strategies in drug discovery: a critical review. (2013, neutral): “The quality of QSAR models is affected by compatibility of concepts, representativeness of structure-activity data, selection of molecular descriptors, influence of data outliers, fitness of developed quantitative relationships, starting geometry in 3D-QSAR and multiple choices of solutions [103].”
Development of Dual Inhibitors against Alzheimer's Disease Using Fragment-Based QSAR and Molecular Docking (2014, neutral): “GQSAR model can be developed by applying statistical methods like partial least square (PLS), principle component regression,multiple regression, continuum regression, and kNearest Neighbour on a series of congeneric compounds in order to gain insights into the effects of descriptors on their biolog”
Machine Learning Methods in Drug Discovery (2020, background): “3D-QSAR has allowed further inquiry of geometric structure impacting ligand-target interactions [33,73,74].”