Shuffling multivariate adaptive regression splines and adaptive neuro-fuzzy inference system as tools for QSAR study of SARS inhibitors

Jalali‐Heravi, Mehdi; Asadollahi-Baboli, M.; Mani-Varnosfaderani, Ahmad

doi:10.1016/j.jpba.2009.07.009

Cited by 41 publications

(22 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More theoretical details about ANFIS could be find in the corresponding literature [450,453]. ANFIS has been shown good ability as a modeling tool in different QSARs/QSPRs [184,[454][455][456][457][458][459].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Yousefinejad

Hemmateenejad

2015

Chemometrics and Intelligent Laboratory Systems

123

View full text Add to dashboard Cite

Section: Accepted Manuscriptmentioning

confidence: 99%

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Yousefinejad

Hemmateenejad

2015

Chemometrics and Intelligent Laboratory Systems

123

View full text Add to dashboard Cite

“…This procedure was carried out 200 times. The theory behind these validation methods has been sufficiently described elsewhere (Jalali-Heravi et al, 2009;Shahbazikhah et al, 2011). The statistical results are given in Table 3 shows the stepwise MLR-LS-SVR and CART-LS-SVR models possessed all the criteria to be considered as a predictive model.…”

Section: Descriptor Selection Using Stepwise Mlr and Cart Techniquesmentioning

confidence: 99%

Molecular docking, molecular dynamics simulation, and QSAR model on potent thiazolidine-4-carboxylic acid inhibitors of influenza neuraminidase

Asadollahi-Baboli

Mani-Varnosfaderani

2012

Med Chem Res

Self Cite

View full text Add to dashboard Cite

In the current work, the relationship between the structure and activity of a series of novel thiazolidine-4-carboxylic acid derivatives as potent influenza virus neuraminidase inhibitors was studied using docking, molecular dynamics (MD) simulations, and QSAR analysis. A 7,000 ps MD simulation in a cubic water box were employed to build 3D structure of the 2HU4 in a water environment. After reaching the equilibrium, the inhibitors were docked into the 2HU4 to realize the binding site of the enzyme. The docking analysis showed that the interaction of the inhibitors with residues Arg371, Arg430, Gly429, Ile427, Lys432, Pro431, Trp403, and Tyr347 plays an important role in the activities of the inhibitors. The docked configurations of the inhibitors with the lowest free energy were used to calculate the most feasible descriptors. The selected descriptors were related to the inhibitory activities using stepwise multiple linear regression, classification and regression trees, and least squares support vector regression techniques. The satisfactory results (R p 2 = 0.883, Q LOO 2 = 0.872, R L25%O 2 = 0.835, RMSE LOO = 0.310, and RMSE L25%O = 0.352) demonstrate that CART-LS-SVR models present the relationship between influenza virus neuraminidase inhibitors activity and molecular descriptors clearly. An energetic analysis based on MD calculations, revealed that the potency of the most active compound binding is governed by electrostatic and van der Waals contacts. The results provide a set of useful guidelines for the rational design of novel influenza virus neuraminidase inhibitors.

show abstract

“…The algorithm of forward selection and backward deletion is proposed by Friedman for the selection of the basis functions and optimization of the GCV parameter [6]. The detailed theory of MARS can be found elsewhere [6,26,27].…”

Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

The use of Bayesian nonlinear regression techniques for the modelling of the retention behaviour of volatile components ofArtemisiaspecies

Jalali‐Heravi

Mani-Varnosfaderani

Taherinia

et al. 2012

SAR and QSAR in Environmental Research

Self Cite

View full text Add to dashboard Cite

The main aim of this work was to assess the ability of Bayesian multivariate adaptive regression splines (BMARS) and Bayesian radial basis function (BRBF) techniques for modelling the gas chromatographic retention indices of volatile components of Artemisia species. A diverse set of molecular descriptors was calculated and used as descriptor pool for modelling the retention indices. The ability of BMARS and BRBF techniques was explored for the selection of the most relevant descriptors and proper basis functions for modelling. The results revealed that BRBF technique is more reproducible than BMARS for modelling the retention indices and can be used as a method for variable selection and modelling in quantitative structure-property relationship (QSPR) studies. It is also concluded that the Markov chain Monte Carlo (MCMC) search engine, implemented in BRBF algorithm, is a suitable method for selecting the most important features from a vast number of them. The values of correlation between the calculated retention indices and the experimental ones for the training and prediction sets (0.935 and 0.902, respectively) revealed the prediction power of the BRBF model in estimating the retention index of volatile components of Artemisia species.

show abstract

Shuffling multivariate adaptive regression splines and adaptive neuro-fuzzy inference system as tools for QSAR study of SARS inhibitors

Cited by 41 publications

References 25 publications

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Molecular docking, molecular dynamics simulation, and QSAR model on potent thiazolidine-4-carboxylic acid inhibitors of influenza neuraminidase

The use of Bayesian nonlinear regression techniques for the modelling of the retention behaviour of volatile components ofArtemisiaspecies

Contact Info

Product

Resources

About