Dynamic 3D QSAR techniques: applications in toxicology (original) (raw)
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Journal of Medicinal Chemistry, 2005
A conceptionally new 3D molecular descriptor type and methodology are deduced by simple statistical thermodynamic reasoning, based on the free energy change encountered during a transformation of a conformational ensemble of the ligand to an active conformation. The performance of the descriptor was first tested on 37 endomorphin analogues with µ-opiate activity. The method resulted in predictive 3D-QSAR models, and the active conformation was also predicted. Generally, the methodology can be combined with the traditional 3D-QSAR techniques in a 3+3D-QSAR manner. This feature was tested on a series of 38 PGF2R prostaglandin analogues with antinidatory activity; the extent to which the molecular flexibility explains the variation in the biological activity was estimated and the active conformation was predicted. The novel descriptors in combination with the grid-based SOMFA descriptors resulted in 3+3D-QSAR models with good levels of predictivity leading to the approach of separation of the effect of the molecular interaction field of the active conformation and the effect of the conformational free energy loss.
QSAR without arbitrary descriptors: the electron-conformational method
Journal of Computer-aided Molecular Design, 2008
The electron-conformational (EC) method in QSAR problems employs a unique (based on first principles) descriptor of molecular properties that incorporates the electronic structure and topology of the molecule and is presented in a digital-matrix form suitable for computer processing, the EC matrix of congruity (ECMC). Its matrix elements have clear-cut physical meanings of interatomic distances, bond orders, and atomic reactivity (interaction indices). By comparing these matrices for several active compounds of the training set a group of matrix elements is revealed that are common for these compounds within a minimum tolerance, the EC submatrix of activity (ECSA). The latter is the numerical pharmacophore for the level of activity and diversity of the tried compounds. The EC method was described in detail and used for pharmacophore identification and quantitative bioactivity prediction elsewhere. In this paper we give further general considerations of its uniqueness and emphasize its advantages as compared with traditional QSAR methods, outlining the following three novel points: (1) The unique, non-arbitrary descriptor employed in the EC method avoids the shortcomings of the arbitrary chosen descriptors and statistical estimation of their weight in the evaluation of the pharmacophore used in traditional QSAR methods. Arbitrary descriptors may be interdependent (“non-orthogonal”) and their sets are necessarily incomplete, hence they may lead to chance correlations and artifacts. The EC pharmacophore is void of these failures, thus deemed to be absolutely reliable within the accuracy of the experimental data and the diversity of the molecules used in its evaluation; (2) The tolerances in the matrix elements of the ECSA play a special role reflecting the flexibilities of the pharmacophore parameters and the dependence of the activity on the latter quantitatively; they are obtained in a minimization procedure; by increasing the tolerances one can get pharmacophores for larger intervals of activity. An advanced formula is derived for the activity as a function of the drug–receptor bonding energy which handles also the multi-conformational problem, and a regressional procedure is suggested to represent the interaction energy and the activity by the ECSA matrix elements or tolerances; (3) The possibility of bimolecular activity is discussed when a single molecule of the active compound has no pharmacophore, but the latter is present in the bimolecular structure. Examples are given from the problem of aquatic toxicity to fish.
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.
Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment, 2015
Developing effective structure-activity/property/toxicity relationships (QSAR/QSPR/QSTR) is very helpful in predicting biological activity, property, and toxicity of a given set of molecules. Regular change in these properties with the structural alteration is the main reason to obtain QSAR/QSPR/QSTR models. The advancement in making different QSAR/QSPR/QSTR models to describe activity, property, and toxicity of various groups of molecules is reviewed in this chapter. The successful implementation of Conceptual Density Functional Theory (CDFT)-based global as well as local reactivity descriptors in modeling effective QSAR/QSPR/QSTR is highlighted.
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 ...
Multi-dimensional QSAR in drug discovery
Drug Discovery Today, 2007
Quantitative structure-activity relationships (QSAR) is an area of computational research that builds virtual models to predict quantities such as the binding affinity or the toxic potential of existing or hypothetical molecules. Although a wealth of experimental data emphasizes the active role of the target protein in the binding process, QSAR studies are frequently restricted to the properties of the smallmolecule ligand. This review aims at discussing recent QSAR concepts exploring higher dimensions (simulation of induced fit, simultaneous exploration of alternative binding modes, and solvation scenarios), and their benefit for the drug-discovery process.
Multiconformational Method for Analyzing the Biological Activity of Molecular Structures
Journal of Structural Chemistry, 2002
A multiconformational method for analyzing the biological activity of compounds is proposed that combines conformer search algorithms and a 3D‐QSAR receptor modeling procedure. The method allows one to find high‐activity and low‐activity conformers and determine the receptor shape. The biological activity of a substance is determined as a superposition of the activities of its conformers with allowance for their proportions in the substance. Agreement between calculated and experimental conformations and between calculated and experimental biological activities pIC 50%) is demonstrated by the example of agonists of the 5‐HT1A receptor.
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.
Three-dimensional molecular descriptors and a novel QSAR method
Journal of Molecular Graphics and Modelling, 2002
A novel set of molecular descriptors suitable for use in quantitative structure-activity relationships and related methods is described. These descriptors are a smooth and interpretable representation of atomic physicochemical property values and intramolecular atom pair distances. Distance atomic physicochemical parameter energy relationships (DAPPER), a novel structure-activity relationship (QSAR) method using these descriptors, is validated on standard datasets.
Journal of computer-aided molecular design, 2000
CoMFA analysis, a widely used 3D-QSAR method, has limitations to handle a set of SAR data containing diverse conformational flexibility since it does not explicitly include the conformational entropic effects into the analysis. Here, we present an attempt to incorporate the conformational entropy effects of a molecule into a 3D-QSAR analysis. Our attempt is based on the assumption that the conformational entropic loss of a ligand upon making a ligand-receptor complex is small if the ligand in an unbound state has a conformational propensity to adopt an active conformation in a complex state. For a QSAR analysis, this assumption was interpreted as follows: a potent ligand should have a higher conformational propensity to adopt an 'active-conformation'-like structure in an unbound state than an inactive one. The conformational propensity value was defined as the populational ratio, Nactive/Nstable, of the number of energetically stable conformers, Nstable, to the number of ...