The influence of molecular lowest-energy conformation on the quality of the subsequent quantitative structure-activity relationship models (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.
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.
2018
Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology,<br> Jadavpur University, Kolkata-700 032, India<br> E-mail: kunal.roy@jadavpuruniversity.in Fax: 91-33-28371078<br> Manuscript received online 02 November 2018, accepted 26 November 2018 In this presentation, different quantitative structure-activity relationship (QSAR) modeling approaches and their use in drug<br> design and ecotoxicological modeling are briefly stated. The aspects of feature selection, modeling algorithms and validation<br> strategies are mentioned at an elementary level. Different novel strategies for improving statistical quality and predictive ability<br> of QSAR models are also cursorily presented. Finally, four useful tools for QSAR model validation as developed by the Drug<br> Theoretics and Cheminformatics (DTC) Laboratory of Jadavpur University are discussed. These tools are available for public<br> use via http://t...
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.
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.
Journal of Computer-Aided Molecular Design, 2009
The quantitative influence of the choice of energy evaluation method used in the geometry optimization step prior to the calculation of molecular descriptors in QSAR and QSPR models was investigated. A total of 11 energy evaluation methods on three molecular datasets (toxicological compounds, aromatic compounds and PPARc agonists) were studied. The methods employed were: MMFF94 s, MM3* with e r (relative dielectric constant) = 1, MM3* with e r = 80, AM1, PM3, HF/STO-3G, HF/6-31G, HF/6-31G(d,p), B3LYP/STO-3G, B3LYP/6-31G, and B3LYP/6-31G(d,p). The 3D-descriptors used in the QSAR/ QSPR models were calculated with commercially available molecular descriptor programs primarily directed toward pharmaceutical research. In order to evaluate the uncertainties involved in the QSAR/QSPR predictions bootstrapping was used to validate all models using 1,000 drawings for each data set. The scale free error-term, q 2 , was used to compare the relative quality of the models resulting from different optimization methods on the same set of molecules. Depending on the dataset, the average 0.632 bootstrap estimated q 2 varies from 0.55 to 0.57 for the toxicological compounds, from 0.58 to 0.62 for the aromatic compounds, and from 0.69 to 0.75 for the PPARc agonists. The B3LYP/6-31G(d,p) provided the best overall results, albeit the increase in q 2 was small in all cases. The results clearly indicate that the choice of the energy evaluation method has very limited impact. This study suggests that QSAR or QSPR studies might benefit from the choice of a rapid optimization method with little or no loss in model accuracy. Keywords QSAR Á QSPR Á Energy evaluation Á PLS regression Á Quantum mechanics Á Semi-empirical Á Molecular mechanics Abbreviations QSPR Quantitative structure property relationship QSAR Quantitative structure activity relationship MM3* Allinger's molecular mechanics AM1 Austin model 1 PM3 Parameterized model 3, HF, Hartree-Fock B3LYP The hybrid exchange-correlation functional based on work from Becke, Lee, Yang and Par PLS Partial least squares RMSD Root mean square distance MCMM Monte Carlo multiple minimum Electronic supplementary material The online version of this article (
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.
Quantitative Structure-activity Relationships, 1992
A simple computerized molecular structure scanning method was developed for generating a large, fixed number of calculated structural variables based on model structures derived from force-field and quantum mechanics methods. These were reduced to one or a few parameters for QSAR studies by means of principal component analysis. When analyzed by principal component regression, the resulting descriptors summarize such gross features as electron distribution and substituent shape and volume very well. For two data sets (some substituted amphetamine hallucinogens and some dihydropyridine vasodilators), these calculated parameters were at least equivalent in their ability to model drug potency to those commonly used in QSAR.
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).
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.