Proposed Quantitative Structure-activity Relationship Model Research Articles

Quantitative structure-activity relationships of imidazole-containing farnesyltransferase inhibitors using different chemometric methods.

Farnesyltranseferase inhibitors (FTIs) are one of the most promising classes of anticancer agents, but though some compounds in this category are in clinical trials there are no marketed drugs in this class yet. Quantitative structure activity relationship (QSAR) models can be used for predicting the activity of FTI candidates in early stages of drug discovery. In this study 192 imidazole-containing FTIs were obtained from the literature, structures of the molecules were optimized using Hyperchem software, and molecular descriptors were calculated using Dragon software. The most suitable descriptors were selected using genetic algorithms-partial least squares (GA-PLS) and stepwise regression, and indicated that the volume, shape and polarity of the FTIs are important for their activities. 2D-QSAR models were prepared using both linear methods, i.e., multiple linear regression (MLR), and non-linear methods, i.e., artificial neural networks (ANN) and support vector machines (SVM). The proposed QSAR models were validated using internal and external validation methods. The results show that the proposed 2D-QSAR models are valid and that they can be applied to predict the activities of imidazole-containing FTIs. The prediction capability of the 2D-QSAR (linear and non-linear) models is comparable to and somewhat better than that of previous 3D-QSAR models and the non-linear models are more accurate than the linear models.

QSAR, molecular docking studies of thiophene and imidazopyridine derivatives as polo-like kinase 1 inhibitors

The purpose of the present study was to develop in silico models allowing for a reliable prediction of polo-like kinase inhibitors based on a large diverse dataset of 136 compounds. As an effective method, quantitative structure activity relationship (QSAR) was applied using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The proposed QSAR models showed reasonable predictivity of thiophene analogs (Rcv2=0.533, Rpred2=0.845) and included four molecular descriptors, namely IC3, RDF075m, Mor02m and R4e+. The optimal model for imidazopyridine derivatives (Rcv2=0.776,Rpred2=0.876) was shown to perform good in prediction accuracy, using GATS2m and BEHe1 descriptors. Analysis of the contour maps helped to identify structural requirements for the inhibitors and served as a basis for the design of the next generation of the inhibitor analogues. Docking studies were also employed to position the inhibitors into the polo-like kinase active site to determine the most probable binding mode. These studies may help to understand the factors influencing the binding affinity of chemicals and to develop alternative methods for prescreening and designing of polo-like kinase inhibitors.

A combined molecular modeling study on gelatinases and their potent inhibitors

Zinc-dependent matrix metalloproteinase (MMP) family is considered to be an attractive target because of its important role in many physiological and pathological processes. In the present work, a molecular modeling study combining protein-, ligand- and complex-based computational methods was performed to analyze a new series of beta-N-biaryl ether sulfonamide hydroxamates as potent inhibitors of gelatinase A (MMP-2) and gelatinase B (MMP-9). Firstly, the similarities and differences between the binding sites of MMP-2 and MMP-9 were analyzed through sequence alignment and structural superimposition. Secondly, in order to extract structural features influencing the activities of these inhibitors, quantitative structure-activity relationship (QSAR) models using genetic algorithm-multiple linear regression (GA-MLR), comparative molecular field (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed. The proposed QSAR models could give good predictive ability for the studied inhibitors. Thirdly, docking study was employed to further explore the binding mode between the ligand and protein. The results from all the above analyses could provide the information about the similarities and differences of the binding mode between the MMP-2, MMP-9 and their potent inhibitors. The obtained results can provide very useful information for the design of new potential inhibitors.

QSAR study of selective I1-imidazoline receptor ligands

Selective imidazoline1-receptor (I1-R) ligands are used clinically to reduce blood pressure. Thus, there is significant interest in developing new imidazoline analogs with high selectivity and affinity for I1 receptors. A quantitative structure–activity relationship (QSAR) study was carried out on 11 potent I1-R ligands (derivatives of imidazoline, oxazoline and pyrroline) using a multiple linear regression (MLR) procedure. The selected compounds have been studied using B3LYP/3–21G(d, p) and B3LYP/6–31G(d, p) methods. Among the 42 descriptors that were considered in generating the QSAR model, three descriptors (partial atomic charges of nitrogen in the heterocyclic moiety (N-2 charge), log D and the dipole moment of the ligands) resulted in a statistically significant model with r 2 > 0.874 and > 0.802. The QSAR models were validated through cross-validation and external test set prediction. The aim of the developed MLR models for the I1-R ligands was to link the structures to their reported I1-R binding affinity log(1/Ki). The proposed QSAR models indicate that an increase in log D and the dipole moment value and a decrease in N-2 charge in the heterocyclic moiety are predictors of better selectivity and affinity for I1 receptors. The developed QSAR model is intended to predict the I1-R binding affinity of related compounds and aid in the rational design of new potent and selective I1-R ligands.

Modeling the activity of furin inhibitors using artificial neural network

Quantitative structure–activity relationship (QSAR) models were constructed for predicting the inhibition of furin-dependent processing of anthrax protective antigen of substituted guanidinylated aryl 2,5-dideoxystreptamines. Molecular descriptors calculated by E-Dragon and RECON were subjected to variable reduction using the Unsupervised Forward Selection (UFS) algorithm. The variables were then used as input for QSAR model generation using partial least squares and back-propagation neural network. Prediction was performed via a two-step approach: (i) perform classification to determine whether the molecule is active or inactive, (ii) develop a QSAR regression model of active molecules. Both classification and regression models yielded good results with RECON providing higher accuracy than that of E-DRAGON descriptors. The performance of the regression model using E-Dragon and RECON descriptors provided a correlation coefficient of 0.807 and 0.923 and root mean square error of 0.666 and 0.304, respectively. Interestingly, it was observed that appropriate representations of the protonation states of the molecules were crucial for good prediction performance, which coincides with the fact that the inhibitors interact with furin via electrostatic forces. The results provide good prospect of using the proposed QSAR models for the rational design of novel therapeutic furin inhibitors toward anthrax and furin-dependent diseases.

QSAR studies on peptide α-ketoamides and α-ketohydroxamate derivatives as calpain I inhibitors

Quantitative Structure Activity Relationship (QSAR) studies were conducted on 34 peptide α-ketoamide and α-ketohydroxamate derivatives of Calpain I using multiple linear regression (MLR) procedure. The activity contributions of these compounds were determined from regression equation and the validation procedures that analyze the predictive ability of QSAR models were described. Among forty six descriptors that were considered in generating the QSAR model, three descriptors such as LogP, Heat of formation and HOMO resulted in a statistically significant model with 0.877 r2 and 0.937 q2 respectively. The inter-correlation between descriptors was 0.42. The proposed QSAR model indicates an increase in logP value increases hydrophobicity in order to achieve cellular permeability and an increase in heat of formation as well as decrease in HOMO energy favors better binding and activity towards development of potent calpain I inhibitors.

In silico screening of estrogen-like chemicals based on different nonlinear classification models

Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that are adversely affecting human and wildlife health through a variety of mechanisms. There is a great need for an effective means of rapidly assessing endocrine-disrupting activity, especially estrogen-simulating activity, because of the large number of such chemicals in the environment. In this study, quantitative structure activity relationship (QSAR) models were developed to quickly and effectively identify possible estrogen-like chemicals based on 232 structurally-diverse chemicals (training set) by using several nonlinear classification methodologies (least-square support vector machine (LS-SVM), counter-propagation artificial neural network (CP-ANN), and k nearest neighbour (kNN)) based on molecular structural descriptors. The models were externally validated by 87 chemicals (prediction set) not included in the training set. All three methods can give satisfactory prediction results both for training and prediction sets, and the most accurate model was obtained by the LS-SVM approach through the comparison of performance. In addition, our model was also applied to about 58,000 discrete organic chemicals; about 76% were predicted not to bind to Estrogen Receptor. The obtained results indicate that the proposed QSAR models are robust, widely applicable and could provide a feasible and practical tool for the rapid screening of potential estrogens.

QSAR Prediction of Estrogen Activity for a Large Set of Diverse Chemicals under the Guidance of OECD Principles

A large number of environmental chemicals, known as endocrine-disrupting chemicals, are suspected of disrupting endocrine functions by mimicking or antagonizing natural hormones, and such chemicals may pose a serious threat to the health of humans and wildlife. They are thought to act through a variety of mechanisms, mainly estrogen-receptor-mediated mechanisms of toxicity. However, it is practically impossible to perform thorough toxicological tests on all potential xenoestrogens, and thus, the quantitative structure--activity relationship (QSAR) provides a promising method for the estimation of a compound's estrogenic activity. Here, QSAR models of the estrogen receptor binding affinity of a large data set of heterogeneous chemicals have been built using theoretical molecular descriptors, giving full consideration to the new OECD principles in regulation for QSAR acceptability, during model construction and assessment. An unambiguous multiple linear regression (MLR) algorithm was used to build the models, and model predictive ability was validated by both internal and external validation. The applicability domain was checked by the leverage approach to verify prediction reliability. The results obtained using several validation paths indicate that the proposed QSAR model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogen activity of organic compounds.

QSAR Study of Skin Sensitization Using Local Lymph Node Assay Data

Allergic Contact Dermatitis (ACD) is a common work-related skin disease that often develops as a result of repetitive skin exposures to a sensitizing chemical agent. A variety of experimental tests have been suggested to assess the skin sensitization potential. We applied a method of Quantitative Structure-Activity Relationship (QSAR) to relate measured and calculated physical-chemical properties of chemical compounds to their sensitization potential. Using statistical methods, each of these properties, called molecular descriptors, was tested for its propensity to predict the sensitization potential. A few of the most informative descriptors were subsequently selected to build a model of skin sensitization. In this work sensitization data for the murine Local Lymph Node Assay (LLNA) were used. In principle, LLNA provides a standardized continuous scale suitable for quantitative assessment of skin sensitization. However, at present many LLNA results are still reported on a dichotomous scale, which is consistent with the scale of guinea pig tests, which were widely used in past years. Therefore, in this study only a dichotomous version of the LLNA data was used. To the statistical end, we relied on the logistic regression approach. This approach provides a statistical tool for investigating and predicting skin sensitization that is expressed only in categorical terms of activity and nonactivity. Based on the data of compounds used in this study, our results suggest a QSAR model of ACD that is based on the following descriptors: nDB (number of double bonds), C-003 (number of CHR3 molecular subfragments), GATS6M (autocorrelation coefficient) and HATS6m (GETAWAY descriptor), although the relevance of the identified descriptors to the continuous ACD QSAR has yet to be shown. The proposed QSAR model gives a percentage of positively predicted responses of 83% on the training set of compounds, and in cross validation it correctly identifies 79% of responses.

Relationships between Molecular Properties and Antimycobacterial Activities of Steroids

Progesterone ( 1 ), cholesterol ( 2 ), stigmasterol ( 3 ), ergosterol endoperoxide ( 4 ), sitosterol ( 5 ), betulin ( 6 ), fusidic acid ( 7 ), chondrilasterol ( 8 ), and ergosterol ( 9 ) have been evaluated against Mycobacterium tuberculosis H37Rv. The maximum antimycobacterial activity seemed to depend on hydrophobicity and the type of substituents on the phytyl moiety on steroidal backbone. The variation in activity was rationalized using quantitative structure-activity relationship (QSAR) models based on several molecular descriptors including van der Waals surface area (VDW A ), van der Waals volume (VDW v ), polarizability, dipole moment, logP, and the differences between the Highest Occupied Molecular Orbital and the Lowest Unoccupied Molecular Orbital (HOMO-LUMO gap). The proposed QSAR models could be developed to predict the antimycobacterial activity of structurally similar steroids and to create a priority list for testing so that time, money, and effort can be focused on the potentially most promising steroids. The implications of QSAR data for the rational design of new antituberculosis agents are discussed. Because mycobacteria degrade steroids to progesterone-type compounds, unambiguous assignments of the high-field proton ( 1 H) and carbon-13 ( 13 C) NMR data of progesterone ( 1 ) were achieved through a combination of modern one- and two-dimensional (2D) NMR techniques.