QSAR Approach Research Articles

Anti-gastric cancer activity of 1,2,3-triazolo[4,5-d]pyrimidine hybrids (1,2,3-TPH): QSAR and molecular docking approaches.

Gastric cancer as a dreaded disease which occurs in the digestive system of human being remain a threat to the medical world. Bioactivity of series of designed and synthesized molecular compounds containing triazole and pyrimidine moieties were subjected to quantum chemical calculations using B3LYP/6-31+G∗. The calculated molecular descriptors such as the EHOMO (eV), ELUMO (eV), band gap (eV), chemical hardness (η), global nucleophilicity, dipole moment (Debye), chemical potential, log P, molecular weight (amu) and Ovality. The descriptors that describe anti-gastric cancer activity of the studied compounds were used for QSAR analysis using SPSS and Gretl software packages for multiple linear regression (MLR), XLSTAT for partial least square (PLS) and MATLAB for artificial neural network (ANN). The methods (MLR, PLS, and ANN) were predictive. Nevertheless, ANN performed better than MLR and PLS. More so, molecular docking study was executed on the studied compounds and gastric cancer cell line (PDB ID:4oum); the docking studies showed that 2-(1-(2-(3-benzyl-5-(benzylthio)-3H-[1,2,3]triazolo[4,5-d]pyrimidin-7-yl)hydrazono)ethyl)phenol (A22) having the lowest binding affinity (-8.40 kcal/mol); this was correlated to the observed inhibitory activity of the compound against gastric cancer. Thus, it showed better inhibition than other studied compounds. The amino acid residues that were involved in stabilizing A22 in the active site of the 4oum are: VAL-9, ALA-10, THR-49, ASN-48, PRO-47 and TYR-46. Also, a good relationship was observed between the calculated binding affinity and the observed inhibition concentration (IC50).

Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach.

Organic fluorescent molecules play critical roles in fluorescence inspection, biological probes, and labeling indicators. More than ten thousand organic fluorescent molecules were imported in this study, followed by a machine learning based approach for extracting the intrinsic structural characteristics that were found to correlate with the fluorescence emission. A systematic informatics procedure was introduced, starting from descriptor cleaning, descriptor space reduction, and statistical-meaningful regression to build a broad and valid model for estimating the fluorescence emission wavelength. The least absolute shrinkage and selection operator (Lasso) regression coupling with the random forest model was finally reported as the numerical predictor as well as being fulfilled with the statistical criteria. Such an informatics model appeared to bring comparable predictive ability, being complementary to the conventional time-dependent density functional theory method in emission wavelength prediction, however, with a fractional computational expense.

The activity prediction of indole inhibitors against HCV NS5B polymerase.

Non-structural viral protein 5B (NS5B) is a viral protein in hepatitis C virus. Although various inhibitors against NS5B have been found, the activity prediction of similar untested inhibitors is still highly desirable. In this respect, the Tchebichef moments (TMs) calculated from the images of molecular structures were regarded as the independent variables while the inhibitory activity (pIC50 ) was the dependent variable, and the predictive model was established by means of stepwise regression. The R-squared of leave-one-out cross-validation (Q2 ) for the training set and the R-squared of prediction ( ) for external independent test set were 0.919 and 0.927, respectively. The obtained model was also evaluated strictly. Compared with the multivariate curve resolution with alternating least squares (MCR-ALS) and the QSAR approaches derived from the literature, the proposed method is more accurate and reliable. This study not only provides an effective approach to predict the biological activity of RNA replication's inhibitors, but also extends the QSAR modeling technique.

Lead Optimization Resources in Drug Discovery for Diabetes.

Diabetes, defined as a chronic metabolic syndrome, exhibits global prevalence and phenomenal rise worldwide. The rising incidence accounts for a global health crisis, demonstrating a profound effect on low and middle-income countries, particularly people with limited healthcare facilities. Highlighting the prevalence of diabetes and its socio-economic implications on the population across the globe, the article aimed to address the emerging significance of computational biology in drug designing and development, pertaining to identification and validation of lead molecules for diabetes treatment. The drug discovery programs have shifted the focus on in silico prediction strategies minimizing prolonged clinical trials and expenses. Despite technological advances and effective drug therapies, the fight against life-threatening, disabling disease has witnessed multiple challenges. The lead optimization resources in computational biology have transformed the research on the identification and optimization of anti-diabetic lead molecules in drug discovery studies. The QSAR approaches and ADMET/Toxicity parameters provide significant evaluation of prospective "drug-like" molecules from natural sources. The science of computational biology has facilitated the drug discovery and development studies and the available data may be utilized in a rational construction of a drug 'blueprint' for a particular individual based on the genetic organization. The identification of natural products possessing bioactive properties as well as their scientific validation is an emerging prospective approach in antidiabetic drug discovery.

Programming the Knowledge: A Trial QSAR Study Towards the Stability of Dialkoxy Benzene Based Catholyte Molecules for Nonaqueous Redox Flow Batteries

Programming the knowledge: A trial QSAR study towards the stability of dialkoxy benzene based catholyte molecules for nonaqueous redox flow batteries Lu Zhang1, Jingjing Zhang1, Benjamin Silcox3, Illya A. Shkrob1, Rajeev S. Assary2, Lei Cheng2, Levi Thompson4 1Chemical Sciences and Engineering Division, 2Material Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, United States 3Chemical Engineering, University of Michigan, 500 S State St, Ann Arbor, MI 48109, United States 4Department of Chemical and Biomolecular Engineering, University of Delaware,Newark, DE 19716, Unite States The stability of redox active molecules is a paramount property that dramatically affects the cycling performance in redox flow batteries. Computationally, it requires huge amount of resource to exhaustively iterate all the possible degradation pathways in order to estimate the stability of active species, such as radical cations.1 QSAR, or quantitative structure–activity relationship, provides an interesting approach to correlate chemical structures and properties based on the idea of property changes are all coming from structure modifications. While this approach simplified the insightful analysis of the detailed mechanism, it becomes particular useful to simulate or even predict complex properties, such as the stability of redox molecules. In this talk, we developed a series of dialkoxy benzene based catholyte molecules that are of potential use to nonaqueous redox flow batteries and systematically changed their substitutions on the alkoxy moieties. Then their electrochemical/chemical properties, including redox potential, charged state stability (reflected by EPR life time), and even cycling numbers, have been characterized and analyzed. Importantly, a number of descriptors have been identified based on our long accumulated knowledge regarding stability of the charged radical cations. Finally, a linear regression model was established using those descriptors to simulate the EPR life time, and even cycling numbers and the resulted fittings delivered quite satisfactory correlation between the selected descriptors and properties. While this is only a trial study based on a small set of molecules, the QSAR approach proves to be useful in understanding complex properties for redox active molecules, which could stimulate further understanding and development of such promising materials. Assary, R. S.; Zhang, L.; Huang, J.; Curtiss, L. A., Molecular Level Understanding of the Factors Affecting the Stability of Dimethoxy Benzene Catholyte Candidates from First-Principles Investigations. The Journal of Physical Chemistry C 2016, 120 (27), 14531-14538.

Predictive models in ecotoxicology: Bridging the gap between scientific progress and regulatory applicability-Remarks and research needs.

This paper concludes a special series of 7 articles (4 on toxicokinetic-toxicodynamic [TK-TD] models and 3 on quantitative structure-activity relationship [QSAR] models) published in previous issues of Integrated Environmental Assessment and Management (IEAM). The present paper summarizes the special series articles and highlights their contribution to the topic of increasing the regulatory applicability of effect models. For both TK-TD and QSAR approaches, we then describe the main research needs. The use of TK-TD models for describing sublethal effects must be better developed, particularly through the improvement of the dynamic energy budget (DEBtox) approach. The potential of TK-TD models for moving from lower (molecular) to higher (population) hierarchical levels is highlighted as a promising research line. Some relevant issues to improve the acceptance of QSAR models at the regulatory level are also described, such as increased transparency of the performance assessment and of the modeling algorithms, model documentation, relevance of the chosen target for regulatory needs, and improved mechanistic interpretability. Integr Environ Assess Manag 2019;00:000-000. © 2019 SETAC.

Integrated Ligand and Structure-Based Investigation of Structural Requirements for Silent Information Regulator 1 (SIRT1) Activation.

A series of imidazothiazole and oxazolopyridine derivatives as human Silent Information Regulator 1 (SIRT1) activators were subjected to the integrated 2D and 3D QSAR approaches. The derived 3D QSAR models yielded high cross-validated q2 values of 0.682 and 0.628 for CoMFA and CoMSIA, respectively. The non-cross validated values of r2 training = 0.89; predictive r2 test = 0.69 for CoMFA and r2=0.87; predictive r2 test =0.67 for CoMSIA reflected the statistical significance of the developed model. The steric, electrostatic, hydrophobic and hydrogen bond acceptor interactions have been found important in describing the variation in human SIRT1 activation. Further, 2D QSAR model for the same dataset yielded high statistical significance and derived 2D model's parameters corroborated with the 3D model in terms of features. Derived model was also validated by the crystal structure of active conformation of SIRT1. Developed models may be useful for the identification of potential novel human SIRT1 activators as a therapeutic agent.

Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery

Structure–activity relationship modelling is frequently used in the early stage of drug discovery to assess the activity of a compound on one or several targets, and can also be used to assess the interaction of compounds with liability targets. QSAR models have been used for these and related applications over many years, with good success. Conformal prediction is a relatively new QSAR approach that provides information on the certainty of a prediction, and so helps in decision-making. However, it is not always clear how best to make use of this additional information. In this article, we describe a case study that directly compares conformal prediction with traditional QSAR methods for large-scale predictions of target-ligand binding. The ChEMBL database was used to extract a data set comprising data from 550 human protein targets with different bioactivity profiles. For each target, a QSAR model and a conformal predictor were trained and their results compared. The models were then evaluated on new data published since the original models were built to simulate a “real world” application. The comparative study highlights the similarities between the two techniques but also some differences that it is important to bear in mind when the methods are used in practical drug discovery applications.

4D-QSAR Study of 17β-Hydroxysteroid Dehydrogenase Type 3 Inhibitors

Prostate cancer is one of the most common form of cancer among men, and its incidence has been increasing progressively. Since the prostate depends on androgenic hormones to regulate its growth and development, interference with androgenic biosynthesis are important to control the disease. Thus, in this work, a 4D-quantitative structure-activity relationship (QSAR) receptor independent study was carried out using the Laboratorio de Quimiometria Teorica e Aplicada (LQTA)-QSAR approach and the new tool Web-4D-QSAR with a set of benzylidene oxazolidinedione and thiazolidinedione inhibitor of the 17β-hydroxysteroid dehydrogenase type 3. The obtained model is robust, free from chance correlation, and with good predictability in all tests performed. In addition to this result, a good mecanistic interpretation related to the binding with the biological target could be traced, which strengthens its potential application in virtual screening and design of new inhibitors of the studied enzyme with the possibility of use in the pharmacological treatment of prostate cancer.

Acceptable‐by‐design QSARs to predict the dietary biomagnification of organic chemicals in fish

This work presents the first-time QSAR approach to predict the laboratory-based fish biomagnification factor (BMF) of organic chemicals, to be used as a supporting tool for assessing bioaccumulation at the regulatory level. The developed strategy is based on 2 levels of prediction, with a varying trade-off between interpretability and performance according to the user's needs. We designed our models to be intrinsically acceptable at the regulatory level (in what we defined as "acceptable-by-design" strategy), by (i) complying with OECD principles directly in the approach development phase, (ii) choosing easy-to-apply modeling techniques, (iii) preferring simple descriptors when possible, and (iv) striving to provide data-driven mechanistic insights. Our novel tool has an error comparable to the observed experimental inter- and intraspecies variability and is stable on borderline compounds (root mean square error [RMSE] ranging from RMSE = 0.45 to RMSE = 0.45 log units on test data). Additionally, the models' molecular descriptors are carefully described and interpreted, allowing us to gather additional mechanistic insights into the structural features controlling the dietary bioaccumulation of chemicals in fish. To improve the transparency and promote the application of the model, the data set and the stand alone prediction tool are provided free of charge at https://github.com/grisoniFr/bmf_qsar Integr Environ Assess Manag 2019;15:51-63. © 2018 SETAC.

Combining a QSAR Approach and Structural Analysis to Derive an SAR Map of Lyn Kinase Inhibition.

Lyn kinase, a member of the Src family of protein tyrosine kinases, is mainly expressed by various hematopoietic cells, neural and adipose tissues. Abnormal Lyn kinase regulation causes various diseases such as cancers. Thus, Lyn represents, a potential target to develop new antitumor drugs. In the present study, using 176 molecules (123 training set molecules and 53 test set molecules) known by their inhibitory activities (IC50) against Lyn kinase, we constructed predictive models by linking their physico-chemical parameters (descriptors) to their biological activity. The models were derived using two different methods: the generalized linear model (GLM) and the artificial neural network (ANN). The ANN Model provided the best prediction precisions with a Square Correlation coefficient R2 = 0.92 and a Root of the Mean Square Error RMSE = 0.29. It was able to extrapolate to the test set successfully (R2 = 0.91 and RMSE = 0.33). In a second step, we have analyzed the used descriptors within the models as well as the structural features of the molecules in the training set. This analysis resulted in a transparent and informative SAR map that can be very useful for medicinal chemists to design new Lyn kinase inhibitors.

Docking and 3D QSAR Studies on Substituted Cyclobutylphenyl Quinoline Derivatives as Inhibitors of Bacterial DNA Gyrase.

Docking and 3D QSAR studies were performed on Inhibitor of Bacterial DNA gyrase to develop a predictive Common Pharmacophore Hypothesis (CPH). Molecular interaction and binding affinities of these analogues, differing with nucleotides and amino acids are studied are performed. Biological activity is predicted by QSAR approach by relating the set of compounds. QSAR study was applied using PHASE, the docking studies were performed using Glide module and Prime/MM-GB/SA is used for free energy ligand binding calculations. We obtained high pred_r2 value (pred_r2= 0. 9239), suggesting a significant external predictive ability of the QSAR model. Mutant docking score is -9. 023. In Wild type docking it shows lower binding energy and binding affinity is a decrease so, docking score of wild most dock 26 molecule is - 6.093. Protein shows various sites for molecular interaction, so from MM-GBSA. In the present study, development of predictive hypothesis (CPH) for 4-substituted cyclobutylphenyl quinoline derivatives and CPH were generated and results were interpreted on a developed model containing five features gives good and predictive statistical significance. Docking studies explained hydrogen bonding, pi-pi stacking with deoxy-ribonucleotide and with Ala 1120 and positive charge with deoxyribonucleotide. MM-GBSA explains the binding affinity of most docked ligand with protein.

Computational study of 2,3-disubstituted-4(3H)quinazolinone derivatives as CNS active compounds: QSAR approach

Computational study of 2,3-disubstituted-4(3H)quinazolinone derivatives as CNS active compounds: QSAR approach

Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3.

In context of D3R Grand Challenge 3 we have investigated several ligand activity prediction protocols that combined elements of a physics-based energy function (ICM VLS score) and the knowledge-based Atomic Property Field 3D QSAR approach. Activity prediction models utilized poses produced by ICM-Dock with ligand bias and 4D receptor conformational ensembles (LigBEnD). Hybrid APF/P (APF/Physics) models were superior to pure physics- or knowledge-based models in our preliminary tests using rigorous three-fold clustered cross-validation and later proved successful in the blind prediction for D3R GC3 sets, consistently performing well across four different targets. The results demonstrate that knowledge-based and physics-based inputs into the machine-learning activity model can be non-redundant and synergistic.

Investigations of Structural Requirements for BRD4 Inhibitors through Ligand- and Structure-Based 3D QSAR Approaches.

The bromodomain containing protein 4 (BRD4) recognizes acetylated histone proteins and plays numerous roles in the progression of a wide range of cancers, due to which it is under intense investigation as a novel anti-cancer drug target. In the present study, we performed three-dimensional quantitative structure activity relationship (3D-QSAR) molecular modeling on a series of 60 inhibitors of BRD4 protein using ligand- and structure-based alignment and different partial charges assignment methods by employing comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches. The developed models were validated using various statistical methods, including non-cross validated correlation coefficient (r2), leave-one-out (LOO) cross validated correlation coefficient (q2), bootstrapping, and Fisher’s randomization test. The highly reliable and predictive CoMFA (q2 = 0.569, r2 = 0.979) and CoMSIA (q2 = 0.500, r2 = 0.982) models were obtained from a structure-based 3D-QSAR approach using Merck molecular force field (MMFF94). The best models demonstrate that electrostatic and steric fields play an important role in the biological activities of these compounds. Hence, based on the contour maps information, new compounds were designed, and their binding modes were elucidated in BRD4 protein’s active site. Further, the activities and physicochemical properties of the designed molecules were also predicted using the best 3D-QSAR models. We believe that predicted models will help us to understand the structural requirements of BRD4 protein inhibitors that belong to quinolinone and quinazolinone classes for the designing of better active compounds.

Designing of phenol-based β-carbonic anhydrase1 inhibitors through QSAR, molecular docking, and MD simulation approach.

Tuberculosis (Tb) is an airborne infectious disease caused by Mycobacterium tuberculosis. Beta-carbonic anhydrase 1 (β-CA1) has emerged as one of the potential targets for new antitubercular drug development. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR), molecular docking, and molecular dynamics (MD) simulation approaches were performed on a series of natural and synthetic phenol-based β-CA1 inhibitors. The developed 3D-QSAR model (r2 = 0.94, q2 = 0.86, and pred_r2 = 0.74) indicated that the steric and electrostatic factors are important parameters to modulate the bioactivity of phenolic compounds. Based on this indication, we designed 72 new phenolic inhibitors, out of which two compounds (D25 and D50) effectively stabilized β-CA1 receptor and, thus, are potential candidates for new generation antitubercular drug discovery program.

On the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity

Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC50 values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality (Q2LOO, Q2(F2), and Q2(F3)), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.

A multipronged QSAR approach to predict algal low-toxic-effect concentrations of substituted phenols and anilines

Environmental risk assessment procedures require acute and chronic toxicity values of hazardous chemicals. In this respect, the 96-h toxicity bioassays of nitro-, methyl-, methoxy-, chloro-, and nitrile- substituted phenols and anilines to Chlorella vulgaris were performed. Median inhibitory and low-toxic-effect concentrations were reported. Significant correlations between acute and chronic toxicities were found for the chemicals in the data set regardless of mode of action. Consequently, linear models employing theoretical and empirical descriptors were developed for the prediction of NOEC and IC20. The outcome of the study will be beneficial in the risk assessments of organic chemicals and setting water quality standards by the regulators.

An integrated QSAR modeling approach to explore the structure-property and selectivity relationships of N-benzoyl-L-biphenylalanines as integrin antagonists.

Integrins [Formula: see text] and [Formula: see text] are important targets to treat different inflammatory diseases, such as multiple sclerosis, inflammatory bowel diseases, rheumatoid arthritis, atherosclerosis, and asthma. Despite being valuable targets, only a few work has been reported to date regarding molecular modeling studies on these integrins. Not only that, none of these reports addressed the selectivity issue between integrins [Formula: see text] and [Formula: see text]. Therefore, a major challenge regarding the design and discovery of selective integrin antagonists remains. In this study, a series of 142 N-benzoyl-L-biphenylalanines having both integrin [Formula: see text] and [Formula: see text] inhibitory activities were considered for a variety of QSAR approaches including regression and classification-based 2D-QSARs, Hologram QSARs, 3D-QSAR CoMFA and CoMSIA studies to identify the structural requirements of these integrin antagonists. All these QSAR models were statistically validated and subsequently correlated with each other to get a detailed understanding of the activity and selectivity profiles of these molecules.

Evaluating the toxicity of TiO2-based nanoparticles to Chinese hamster ovary cells and Escherichia coli: a complementary experimental and computational approach.

Titania-supported palladium, gold and bimetallic nanoparticles (second-generation nanoparticles) demonstrate promising photocatalytic properties. However, due to unusual reactivity, second-generation nanoparticles can be hazardous for living organisms. Considering the ever-growing number of new types of nanoparticles that can potentially contaminate the environment, a determination of their toxicity is extremely important. The main aim of presented study was to investigate the cytotoxic effect of surface modified TiO2-based nanoparticles, to model their quantitative nanostructure–toxicity relationships and to reveal the toxicity mechanism. In this context, toxicity tests for surface-modified TiO2-based nanoparticles were performed in vitro, using Gram-negative bacteria Escherichia coli and Chinese hamster ovary (CHO-K1) cells. The obtained cytotoxicity data were analyzed by means of computational methods (quantitative structure–activity relationships, QSAR approach). Based on a combined experimental and computational approach, predictive models were developed, and relationships between cytotoxicity, size, and specific surface area (Brunauer–Emmett–Teller surface, BET) of nanoparticles were discussed.