Multi-dimensional QSAR in drug discovery (original) (raw)
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Review on: quantitative structure activity relationship (QSAR) modeling
Journal of analytical & pharmaceutical research, 2018
Quantitative Structure Activity Relationship (QSAR) are mathematical models that seek to predict complicated physicochemical /biological properties of chemicals from their simpler experimental or calculated properties .QSAR enables the investigator to establishes a reliable quantitative relationship between structure and activity which will be used to derive an insilico model to predict the activity of novel molecules prior to their synthesis. The past few decades have witnessed much advances in the development of computational models for the prediction of a wide span of biological and chemical activities that are beneficial for screening promising compounds with robust properties. This review covers the concept, history of QSAR and also the components involved in the development of QSAR models.
Computer-Aided Linear Modeling Employing Qsar for Drug Discovery
Scientific Research and Essays, 2009
Quantitative structure-activity relationship (QSAR) is a computational process that relates the chemical structure of compounds with their activities, especially biologic activities or effects. It employs series of computer-based processes to analyze quantitative experimental data of the activities of given compounds with known chemical structures in order to predict a relationship, model or equation that will help to propose the activity of known compounds with unknown activities or unknown compounds and their activities. Commonly used computer softwares in QSAR analysis include HYPERCHEM, MATLAB, DRAGON and RECKON. Key words: QSAR, biological activity, prediction, computer software.
3D-QSAR in Drug Design -A Review
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 structureactivity 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.
Computational Methods in Developing Quantitative StructureActivity Relationships (QSAR): A Review
Combinatorial Chemistry & High Throughput Screening, 2006
Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure of compounds, for selection of informative descriptors and for activity prediction. We present both the well-established methods as well as techniques recently introduced into the QSAR domain.
Current Drug Discovery Technologies, 2007
Chemical breakthrough generates large numbers of prospective drug molecules; the use of ADMET (absorption, distribution, metabolism, excretion and toxicity) properties is flattering progressively more imperative in the drug discovery, assortment, development and promotion processes. Due to the inauspicious ADMET properties a huge amount of molecules in the development stage got failure. In the past years several authors reported that it possible to do some prediction of the ADMET properties using the structural features of the molecules, suing several approaches. One of the most important approaches is QSAR modeling of the data derived from their activity profiles and their different structural features (i.e., quantitative molecular descriptors). This review is critically assessing some of the most important issues for the effective prediction of ADMET properties of drug candidates based on QSAR modeling approaches.
Development of biologically active compounds by combining 3D QSAR and structure-based design methods
Journal of computer-aided molecular design, 2002
One of the major challenges in computational approaches to drug design is the accurate prediction of the binding affinity of novel biomolecules. In the present study an automated procedure which combines docking and 3D-QSAR methods was applied to several drug targets. The developed receptor-based 3D-QSAR methodology was tested on several sets of ligands for which the three-dimensional structure of the target protein has been solved--namely estrogen receptor, acetylcholine esterase and protein-tyrosine-phosphatase 1B. The molecular alignments of the studied ligands were determined using the docking program AutoDock and were compared with the X-ray structures of the corresponding protein-ligand complexes. The automatically generated protein-based ligand alignment obtained was subsequently taken as basis for a comparative field analysis applying the GRID/GOLPE approach. Using GRID interaction fields and applying variable selection procedures, highly predictive models were obtained. It ...
QSAR Studies as Strategic Approach in Drug Discovery
International Journal of Scientific Research in Chemistry, 2019
The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. It helps in the rational design of drugs by computer aided tools via molecular modeling, simulation and virtual screening of promising candidates prior to synthesis. In order to achieve a reliable statistical model for predicting the behaviors of new chemical entities, quantitative structure activity relationship (QSAR) have been used for decades to establish connections between the physicochemical properties of chemicals and their biological activities. The fundamental concept of formalism is that the biological differences in the compounds have a difference in structural properties. The atom, groups or molecular characteristics of ligands affinity to its sites, inhibition constants, frequency constants, and more biological endpoints have been linked with the classic QSAR studies such as lipophilicity, polarization, electronic and steric properties (hansch analysis) and basic structural characteristics (free wilson analysis).
Journal of Computer-aided Molecular Design, 2006
A novel approach termed comparative residue-interaction analysis (CoRIA), emphasizing the trends and principles of QSAR in a ligand–receptor environment has been developed to analyze and predict the binding affinity of enzyme inhibitors. To test this new approach, a training set of 36 COX-2 inhibitors belonging to nine families was selected. The putative binding (bioactive) conformations of inhibitors in the COX-2 active site were searched using the program DOCK. The docked configurations were further refined by a combination of Monte Carlo and simulated annealing methods with the Affinity program. The non-bonded interaction energies of the inhibitors with the individual amino acid residues in the active site were then computed. These interaction energies, plus specific terms describing the thermodynamics of ligand–enzyme binding, were correlated to the biological activity with G/PLS. The various QSAR models obtained were validated internally by cross validation and boot strapping, and externally using a test set of 13 molecules. The QSAR models developed on the CoRIA formalism were robust with good r 2, q 2 and r pred2 values. The major highlights of the method are: adaptation of the QSAR formalism in a receptor setting to answer both the type (qualitative) and the extent (quantitative) of ligand–receptor binding, and use of descriptors that account for the complete thermodynamics of the ligand–receptor binding. The CoRIA approach can be used to identify crucial interactions of inhibitors with the enzyme at the residue level, which can be gainfully exploited in optimizing the inhibitory activity of ligands. Furthermore, it can be used with advantage to guide point mutation studies. As regards the COX-2 dataset, the CoRIA approach shows that improving Coulombic interaction with Pro528 and reducing van der Waals interaction with Tyr385 will improve the binding affinity of inhibitors.
Application of 3D-QSAR in the Rational Design of Receptor Ligands and Enzyme Inhibitors
Chemistry & Biodiversity, 2005
Quantitative structure ± activity relationships (QSARs) are frequently employed in medicinal chemistry projects, both to rationalize structure ± activity relationships (SAR) for known series of compounds and to help in the design of innovative structures endowed with desired pharmacological actions. As a difference from the so-called structure-based drug design tools, they do not require the knowledge of the biological target structure, but are based on the comparison of drug structural features, thus being defined ligand-based drug design tools. In the 3D-QSAR approach, structural descriptors are calculated from molecular models of the ligands, as interaction fields within a three-dimensional (3D) lattice of points surrounding the ligand structure. These descriptors are collected in a large X matrix, which is submitted to multivariate analysis to look for correlations with biological activity. Like for other QSARs, the reliability and usefulness of the correlation models depends on the validity of the assumptions and on the quality of the data. A careful selection of compounds and pharmacological data can improve the application of 3D-QSAR analysis in drug design. Some examples of the application of CoMFA and CoMSIA approaches to the SAR study and design of receptor or enzyme ligands is described, pointing the attention to the fields of melatonin receptor ligands and FAAH inhibitors.
Analogue-based approaches in anti-cancer compound modelling: the relevance of QSAR models
Organic and Medicinal Chemistry Letters, 2011
Background QSAR is among the most extensively used computational methodology for analogue-based design. The application of various descriptor classes like quantum chemical, molecular mechanics, conceptual density functional theory (DFT)- and docking-based descriptors for predicting anti-cancer activity is well known. Although in vitro assay for anti-cancer activity is available against many different cell lines, most of the computational studies are carried out targeting insufficient number of cell lines. Hence, statistically robust and extensive QSAR studies against 29 different cancer cell lines and its comparative account, has been carried out. Results The predictive models were built for 266 compounds with experimental data against 29 different cancer cell lines, employing independent and least number of descriptors. Robust statistical analysis shows a high correlation, cross-validation coefficient values, and provides a range of QSAR equations. Comparative performance of each c...