QSAR Applications Research Articles

QSAR application of natural therapeutics inhibitors against Alzheimer's disease through in-silico virtual-screening, docking-simulation, molecular dynamics, and pharmacokinetic prediction analysis

Alzheimer's disease (AD) is a brain disorder that is known to be one of the deadliest diseases affecting humanity, especially adults from the age of sixty (60) years and above. It mostly affects thinking ability, behaviour and social skills, eventually, AD causes the brain to shrink and brain cells to die. To curb the menace of this disease, virtual screening of potent, non-toxic hybrid natural therapeutic inhibitors was performed on some inhibitors of AD. We performed simulations on the screened compounds and predicted their druggability. A model with satisfactory statistical properties was developed in this study. The ligands underwent molecular docking, C-19 exhibited the highest docked score of −12.8 ​kcal/mol against the target, while the referenced compound (harmine) indicated the lowest docked score of −8.2 ​kcal/mol. The docked complex was validated using molecular dynamic simulations. Trajectory plots of C-19 were obtained and found to be stable. C-19 was stable during the 100 ns intervals which implies that the compounds were better than the referenced compound. In addition, ADMET has demonstrated that these ligands have good pharmacokinetic properties. All the evaluations were more comprehensive and beneficial to researchers and the medical community as outstanding results were obtained.

Zeolite AST: Relativistic degree and distance based topological descriptors

Zeolite AST framework is one of the most versatile cage frameworks due to its functional and structural simplicity, consisting of two four- and six-membered rings with tunable pore sizes. The AST framework provides an excellent model for the systematic study of the possible substitution of atoms at the centre of the primary building unit of the framework. It has recently been demonstrated to be one of the most attractive structures for deriving metal organic frameworks and zeolite-like metal organic frameworks, with adjustable large and extra-large cavities. This specific property has given tremendous thrust to the synthesis, characterization, and modification of new novel frameworks with an AST structure that are proving to be very effective materials in the fields of optics, heterogeneous catalysis, and hydrogen storage. In our study, we employ relativistic topological models that facilitate computations of exact mathematical expressions for the degree and distance based structural descriptors by using an orthogonal approach to partition the bonds of the rectangular AST framework. Such expressions would find novel applications in QSAR and QSPR models for large AST zeolites, especially in the derivations of thermodynamic, spectroscopic and topological properties of these materials.

Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis

ABSTRACT The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r 2 VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.

Estimating the reliability of simulated metabolism using documented data and theoretical knowledge. QSAR application

Establishing the reliability of simulated metabolism continues to be pivotal in accepting predictions of both fate and toxicological endpoints, especially when metabolic activation of a parent chemical is deemed crucial. A quintessential way of estimating the reliability of simulated metabolism is by comparing a simulated metabolic map with an appropriate documented metabolic map. The approach is constructed on two core parts - experimental and theoretical corroboration. Specifically, the three-layer algorithm is used to support experimentally the adequacy of the simulated maps. The first layer defines similarity boundaries between the parent chemical or metabolite starting the sequence, the root of the simulated series of biotransformations, and the corresponding initial structure of the analogue from the database with documented maps. Different criteria (e.g., the commonality between organic functional groups) are used for this rationale. The second layer delineates the metabolic transformation sequences applied to the target chemical or the initial metabolite of the transformation sequence. The last layer establishes the similarity between the final transformation product in the simulated and documented sequences. To support the adequacy of the simulated molecular transformations, a library of theoretical knowledge is used, providing mechanistic justification on applied transformations. The results of applications of the above procedure are shown using two examples.

On Topological Properties of Planar Octahedron Networks

In the research of QSAR and QSPR correlations, topological indices such as the Randi’c index, Zagreb index, ABC index, and geometric-arithmetic index have been proposed to analyze bio-compatibility of chemical compounds. Chemical graph theory is the combination of Chemistry and Graph theory, and this theory deals with the topology such as the mathematical study of isomerism and the development of topological indices which has applications in QSAR and QSPR. In this article, we consider the Octahedron networks and find out the above mentioned degree based topological indices.

QSARINS-Chem standalone version: A new platform-independent software to profile chemicals forphysico-chemical properties, fate, and toxicity.

The new software QSARINS‐Chem standalone version is a multiplatform tool, freely downloadable, for the in silico profiling of multiple properties and activities of organic chemicals. This software, which is based on the concept of the QSARINS‐chem module embedded in the QSARINS software, has been fully redesigned and redeveloped in the Java™ language. In addition to a selection of models included in the old module, the new software predicts biotransformation rates and aquatic toxicities of pharmaceuticals and personal care products in multiple organisms, and offers a suite of tools for the analysis of predictions. Furthermore, a comprehensive and transparent database of molecular structures is provided. The new QSARINS‐Chem standalone version is an informative and solid tool, which is useful to support the assessment of the potential hazard and risks related to organic chemicals and is dedicated to users which are interested in the application of QSARs to generate reliable predictions.

Evaluation of the applicability of existing (Q)SAR models for predicting the genotoxicity of pesticides and similarity analysis related with genotoxicity of pesticides for facilitating of grouping and read across: An EFSA funded project

To facilitate the practical implementation of the guidance on the residue definition for dietary risk assessment, EFSA has organized an evaluation of applicability of existing in silico models for predicting the genotoxicity of pesticides and their metabolites, including literature survey, application of QSARs and development of Read Across methodologies. This paper summarizes the main results. For the Ames test, all (Q)SAR models generated statistically significant predictions, comparable with the experimental variability of the test. The reliability of the models for other assays/endpoints appears to be still far from optimality. Two new Read Across approaches were evaluated: Read Across was largely successful for predicting the Ames test results, but less for in vitro Chromosomal Aberrations. The worse results for non-Ames endpoints may be attributable to the several revisions of experimental protocols and evaluation criteria of results, that have made the databases qualitatively non-homogeneous and poorly suitable for modeling. Last, Parent/Metabolite structural differences (besides known Structural Alerts) that may, or may not cause changes in the Ames mutagenicity were identified and catalogued. The findings from this work are suitable for being integrated into Weight-of-Evidence and Tiered evaluation schemes. Areas needing further developments are pointed out.

Application of 3D QSAR and Docking Studies in Optimization of Perylene di imides as Anti Cancer Agent

Abstract: Introduction: Telomerase is an enzyme which binds to telomeres and increases its length which leads to extension of lifespan of cells. These enzymes are expressed at detectable levels in cancer cells which makes an attractive target for cancer therapy. The G Quadruplex ligands which bind to telomerase with respect to duplex genomic DNA is of special importance. The Perylene di imides are selected, designed and QSAR study has been done, finally from the QSAR results’ docking has been done by G4LDB database. To compare and to narrow down the Docking results from G4LDB database, we have chosen AutoDock tool by selecting a target Telomerase protein (PDB ID: 4B18) to analyse the binding affinity of the protein with respect to the Perylene diimides. The best scored compounds will be efficient for designing new molecules as well as for the anti-cancer therapy. Material: G4LDB Database, AutoDock 4.2, Discovery Studio Visualizer 4.1. Methods: The study was to investigate and compare the results from G4LDB database with the AutoDock results for anti cancer activity of Perylene di imides. The results are visualized by Discovery Studio 4.1 Visualizer. Compound 20 and compound 48 shows best ligand interaction with the selected targets from G4LDB database. From the AutoDock results the compounds are docked with the specific Telomerase protein 4B18 and Compound 11 shows good binding energy when compared with the PIPER. Results: Compounds 11, 20 and 48 showed good biological activity also possessing best binding affinity with the target. Discussion: From the QSAR and Docking studies (G4LDB and AutoDock), 3 compounds (11, 20 and 48) showed good biological activity possessing a strong correlation coefficient, endorses that Perylene derivatives are having strong affinity with the targets. Docking has been done from the results of QSAR study, targeting Telomerase protein to study the binding affinity with the target. Conclusion: From the results, the best compounds will be efficient to inhibit telomerase enzyme and these compounds can be used to design new molecules which will be effective for anticancer therapy. Key words: Perylene Derivatives, QSAR Plus, G-Quadruplex Ligand Database, Docking.

The Synthetic and Biological Attributes of Pyrazole Derivatives: A Review.

Pyrazole nucleus is a unique structural scaffold which acts as an interesting template for combinatorial as well as medicinal chemistry. This review presents a detailed overview on the synthesis, QSAR and pharmacological applications of pyrazole. In addition, it covers most recent reports on structural modifications on pyrazole illustrating vital structural activity relationship.

Synthesis and physicochemical studies of vanillin chalcones

Se sintetizaron algunos compuestos del tipo chalcona y su caracterizaron mediante técnicas espectroscópicas tales como IR, RMN y espectrometría de masa. Algunas propiedades fisicoquímicas tales como propiedades acústicas, índice de refracción, conductancia y coeficiente de reparto se estudiaron, para los compuestos sintetizados, en N,N-dimetil formamida y cloroformo a 303,15 K. Las propiedades estudiadas son útiles en estudios QSAR y en aplicaciones de estos compuestos en otros campos. Se observa que estos parámetros se ven afectados por el disolvente y las sustituciones presentes en los compuestos.

The Application of Classical QSAR to Agrochemical Research

Since the first paper for the QSAR procedure was published in 1962, a number of agrochemical QSAR studies have been accumulated. Among them, some studies were to construct models to design new compounds of the improved activity profiles. Others were to search for a clue for molecular mechanisms of action and/or physicochemical backgrounds for some “compulsory or favorable” substructures and substituents within the compound series. In this article, some examples of studies from these two categories are reviewed.

Application of group-based QSAR on 2-thioxo-4-thiazolidinone for development of potent anti-diabetic compounds

To identify the structural requirement for development of lead structures with PPAR gamma binding activity group based quantitative structure activity relationship (GQSAR) studies on 46 reported structures were carried out. The molecules in the current dataset were fragmented into seven functional groups fragments (R1, R2, R3, R4, R5, R6 and R7). GQSAR models were derived using multiple linear regressions analysis. Four generated GQSAR models were selected based on the statistical significance of the model. It was found that the presence of smaller groups on fragment R7 and presence of lipophilic group at fragment R2 was conducive for PPAR gamma binding. Additionally, the existence of hydrogen bond acceptor at fragments R6 was fruitful PPAR gamma binding. The generated models provide a site-specific insight into the structural requirements PPAR γ binding which can be used to design and develop potent antidiabetic compounds.

Novel virtual lead identification in the discovery of hematopoietic cell kinase (HCK) inhibitors: application of 3D QSAR and molecular dynamics simulation

High level of hematopoietic cell kinase (Hck) is associated with drug resistance in chronic myeloid leukemia. Additionally, Hck activity has also been connected with the pathogenesis of HIV-1 and chronic obstructive pulmonary disease. In this study, three-dimensional (3D) QSAR pharmacophore models were generated for Hck based on experimentally known inhibitors. A best pharmacophore model, Hypo1, was developed with high correlation coefficient (0.975), Low RMS deviation (0.60) and large cost difference (49.31), containing three ring aromatic and one hydrophobic aliphatic feature. It was further validated by the test set (r = 0.96) and Fisher’s randomization method (95%). Hypo 1 was used as a 3D query for screening the chemical databases, and the hits were further screened by applying Lipinski’s rule of five and ADMET properties. Selected hit compounds were subjected to molecular docking to identify binding conformations in the active site. Finally, the appropriate binding modes of final hit compounds were revealed by molecular dynamics (MD) simulations and free energy calculation studies. Hence, we propose the final three hit compounds as virtual candidates for Hck inhibitors.

Docking, LIE, Metadynamics, QSAR application on CAII ligands set: methodological weaknesses and suggested improvements

Docking, LIE, Metadynamics, QSAR application on CAII ligands set: methodological weaknesses and suggested improvements

Application of Group-Based QSAR and Molecular Docking in the Design of Insulin-Like Growth Factor Antagonists

Purpose: To identify the structural requirements for designing a lead key for insulin-like growth factor (IGF-1R) inhibition using group-based quantitative structure activity relationship (GQSAR) and molecular docking.Methods: GQSAR method requires fragmentation of molecules. The molecules in the current dataset were fragmented into three (R1, R2 and R3) by applying common fragmentation pattern, and fragmentbased 2D descriptors were then calculated. GQSAR models were derived by applying various methods including multiple linear regressions and partial least square or k-nearest neighbour.Results: Four generated GQSAR models were selected based on the statistical significance of the model. It was found that the presence of flexible and non-aromatic groups on fragment R1 was conducive for inhibition. Additionally, the existence of amino groups as hydrogen bond donors at fragments R2 and R3 was fruitful for inhibition. Docking studies revealed the binding orientation adopted by the active compounds at several amino acid residues, including Met 1126, Arg, 1128, Met 1052, GLU 1050, Met 1112, Leu 1051, Met 1049, Val 1033, and Val 983 at ATP binding sites of IGF-1R kinase domain.Conclusion: The generated models provide a site-specific insight into the structural requirements for IGF-1R inhibition which can be used to design and develop potent inhibitors.Keywords: Insulin-like growth factor 1 (IGF-1) receptor, Quantitative structure-activity relationship, Adenosine triphosphate, Competitive inhibitors, Electrotopological state index.

QSAR modeling: where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

QSAR Applications on Polycyclic Aromatic Hydrocarbons and Some Derivatives

Polycyclic Aromatic Hydrocarbons (PAHs) have been the focus of great attention for a long time owing to their impact on public health and the environment. In this article we overview various QSAR studies on PAHs and some derivatives, that have been developed during the last decade by different authors. Several properties of broad interest are analyzed, such as carcinogenesis, mutagenicity, biocatalytic oxidation, phototoxicity, dermal penetration, biodegradation, and others. The molecular descriptors types and modeling techniques used are briefly commented.

The application of conformal prediction to the drug discovery process

QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using machine learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity during the drug discovery process. However, the confidence or reliability of predictions from a QSAR model are difficult to accurately assess. We frame the application of QSAR to preclinical drug development in an off-line inductive conformal prediction framework and apply it prospectively to historical data collected from four different assays within AstraZeneca over a time course of about five years. The results indicate weakened validity of the conformal predictor due to violations of the randomness assumption. The validity can be strengthen by adopting semi-off-line conformal prediction. The non-randomness of the data prevents exactly valid predictions but comparisons to the results of a traditional QSAR procedure applied to the same data indicate that conformal predictions are highly useful in the drug discovery process.

OECD QSAR Application Toolbox를 이용한 화학물질의 건강유해성 및 생태독성 예측

Objectives: The OECD QSAR Application Toolbox was developed by the Organisation for Economic Cooperation and Development (OECD) to facilitate the practical use of QSAR approaches in regulatory contexts as well as to reduce the need for additional animal testing. In this study, human health and the ecotoxicity of chemicals were predicted by applying the OECD QSAR Application Toolbox and the results were compared with experimental data in order to evaluate the applicability of this program. Methods: Read-across, trend analysis, and QSAR of OECD QSAR Application Toolbox were used for the prediction of toxicity. Results: The toxicity prediction was conducted on 6,354 chemicals for which toxicity data have been produced on the six endpoints of skin sensitization, skin irritation, eye irritation, mutagenicity, and acute toxicities of fish and Daphnia. From the total of 6,354, we obtained prediction results for 1,621 chemicals (25.5%). Conclusions: The predicted properties of mutagenicity, skin sensitization, and acute aquatic toxicities were reasonably good when compared with experimental data, but other endpoints were not due to the limitation of applicable chemical groups.

Use and perceived benefits and barriers of QSAR models for REACH: findings from a questionnaire to stakeholders

The ORCHESTRA online questionnaire on “benefits and barriers to the use of QSAR methods” addressed the academic, consultant, regulatory and industry communities potentially interested by QSAR methods in the context of REACH. Replies from more than 60 stakeholders produced some insights on the actual application of QSAR methods, and how to improve their use.Respondents state in majority that they have used QSAR methods. All have some future plans to test or use QSAR methods in accordance with their stakeholder role.The stakeholder respondents cited a total of 28 models, methods or software that they have actually applied. The three most frequently cited suites, used moreover by all the stakeholder categories, are the OECD Toolbox, EPISuite and CAESAR; all are free tools.Results suggest that stereotyped assumptions about the barriers to application of QSAR may be incorrect. Economic costs (including potential delays) are not found to be a major barrier. And only one respondent “prefers” traditional, well-known and accepted toxicological assessment methods.Information and guidance may be the keys to reinforcing use of QSAR models. Regulators appear most interested in obtaining clear explanation of the basis of the models, to provide a solid basis for decisions. Scientists appear most interested in the exploration of the scientific capabilities of the QSAR approach. Industry shows interest in obtaining reassurance that appropriate uses of QSAR will be accepted by regulators.