Sharif Digital Repository

Regarding the importance of biological systems in daily life and the complexity of these systems, this project is concerned with this problem and especially with applications of chemometrics in proteomics. In this respect, specific importance of peptides has been taken into account in the process of construction of especial and necessary proteins for human body. Due to the risks involved in some experimental investigations, it is quite preferable to utilize modeling approaches using different sets of data. Achieving a number of specific descriptors, a powerful can be established. This model could be quiet comprehensive for the prediction of the electrophoretic mobility of peptides. This... 

QSAR studies play an important role in drug design and lead optimization by developing a mathematical relationship between the chemical structures of compounds and their biological activities.By using such an approach one could predict the activities of newly designed compounds before a decision is made whether these compounds should be really synthesized or tested. The main aim of the present work was to develop a QSAR model for predicting the inhibitory activity of acetyl CoA carboxylase (ACC)derivatives as anti-diabetic inhibitors. Since variable selection is a critical step in every QSAR study, four different algorithms, based on Monte Carlo cross-validation techniques, were... 

The main aim of this study is developing a robust QSAR model for describing and predicting the inhibitory activities of O-(2-phthalimidoethyl)-N-substituted thiocarbamates derivatives as novel HIV-1 non-nucleoside reverse transcriptase (HIV-1 NNRTIs) inhibitors. These drugs change the active site of the reverse transcriptase enzyme, and finally halter the HIV reproduction cycle. As the first step of this study, a multiple linear regression (MLR) model was built but it has no satisfied prediction ability. As a next step, the nonlinear correlation of the molecular descriptors and activities has been investigated by using artificial neural networks (ANN). In this section the effects of variable... 

Progress in analysis of multicomponent mixtures and processes focused on the interpretation of the multivariate responses, rely on the combination of sophisticated instrumental techniques and suitable chemometric methods. Among the present chemometric method, curve resolution techniques have a wide application in the analysis of multicomponent mixtures and processes. Chemometric resolution techniques are used in the present work. At the first, Essential oil of green and black cardamoms was extracted by hydrodistillation and was analyzed by gas chromatography–mass spectrometry (GC-MS). Due to the fact that the essential oils are complex mixtures, strong overlap is observed between the peaks.... 

In the present work we report a quantitative structure-activity relationship (QSAR) study on S1P1 receptor’s agonists that have therapeutic potential for autoimmune disorders such as Multiple Sclerosis (MS). Such studies play an important role in drug design and lead optimization by developing a mathematical relationship between the chemical structures of compounds and their biological activities.
We used both linear and nonlinear techniques such as MLR and ANN respectively to model these compounds together with techniques such as Stepwise-MLR, GA-MLR and GA-ANN in the variable selection step as it is an important step in every QSAR study. Since topological descriptors are well... 

The derivatives of 5-Hydroxy-3(2H)-pyridazinone show inhibitory effects on hepatitis C virus. The aim of the present work was modeling and prediction of inhibitory effects of these derivatives (Log(1/EC50)) on this disease. In this research, a data set of 119 molecules of 5-Hydroxy-3(2H)-pyridazinone derivatives that have inhibitory effect on hepatitis C virus was selected. The MLR model was generated using SPSS package. Five important descriptors were selected applying stepwise variable selection technique. These descriptors selected through 1207 descriptors that were calculated for all molecules in data set. Best model with high R2 and F values and low RMSE was selected for the... 

Chemometrics is an interdisciplinary subject which has many applications among science and industrial process. Environmental chemist, Food chemist, biologist and so on depend on good analytical chemistry measurement and so need chemometrics to interpret their data. In this project, we have developed different chemometrics and machine learning methods for considering the quantitative structure-activity relationship between different drug-like molecules. Also some chemometrics techniques were applied for the pattern recognition of NIR data on human plasma for discriminating between healthy and infected HIV-1 patients. At first, Quantitative structure-activity relationship (QSAR) models for... 

The present research devoted to the application, development and implementation of clustering, classification and regression techniques for modeling of the biological activity of different drug and drug-like molecules. At first, the prediction ability of Bayesian regression techniques was evaluated for describing and predicting the inhibition behavior of Integrin antagonists. As a next step, the complementary local search techniques have been used for improving the performances of Bayesian regularized genetic neural network (BRGNN) algorithm. The results indicated that the pattern search algorithm has a great potential to be used as a feature selection method in Chemoinformatics. In line... 

Prevalence of Multiple Drug Resistant Tuberculosis and use of Yersinia-Pestis in bioterrorism warrants synthesis of new antimicrobial agents. Although Pyrazoline derivatives were first synthesized as antimicrobial drugs, but they also had Mono Amine Oxidase inhibitory effects. In this Quantitative Structure – Activity Relationship (QSAR) study, MAO-I activity of pyrazoline derivatives were evaluated. By applying semi-empirical quantum calculations at AM1 level, optimum 3D geometry of 32 molecules were obtained.After descriptor generation, Principal Component Analysis (PCA) was performed to fiind out 5 outliers. After omitting outliers and sorting molecules according to IC50 values, Stepwise... 

The optimum wave functions and calculation method were obtained using a 24 factorial design. Based on preliminary experiences, the following four factors at two level was selected: electron correlation, triple-ξ valence shell, diffuse function and polarization function.
The wave functions for calculating gas phase 1H chemical shifts of primary and secondary alcohols were optimized using factorial design as multivariate technique. Gas-phase experimental 1H chemical shifts of 18 alcohols were used to establish the best levels of theory for obtaining 1H chemical shift, among them the new experimental values of 1H chemical shifts of 10 alcohols were obtained in our laboratory. HF/6-31G(d,p)... 

Factor analysis based methods are one of the applied methods in the Chemometrics. In this regard, some modifications on one of the factor analysis based method named fixed size moving window evolving factor analysis were done and two new methods named variable size moving window evolving factor analysis and preselected variable size moving window evolving factor analysis were introduced. The performance of the new introduced methods were examined and compared by the fixed size moving window evolving factor analysis.
Pure variable selection methods are useful for curve resolution methods which need to define an initial estimate from chromatographic profile or spectra. In this aspect the...