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  3. QSAR study on pyrrolo[3,2-d]pyrimidine-7-carbonitrile derivatives for antidiabetic drugs

QSAR study on pyrrolo[3,2-d]pyrimidine-7-carbonitrile derivatives for antidiabetic drugs

Number of pages: 124 File Format: word File Code: 31844
Year: Not Specified University Degree: Master's degree Category: Chemical - Petrochemical Engineering
Tags/Keywords: ANN - Antidiabetic drugs - Artificial neural networks - Computational chemistry - GA - Genetic algorithm - Multiple linear regression - QSAR study
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  • Summary of QSAR study on pyrrolo[3,2-d]pyrimidine-7-carbonitrile derivatives for antidiabetic drugs

    Academic Thesis for Master's Degree

    Field of Chemistry

    Foreword

    Computational chemistry is a new approach to known and familiar physical and chemical phenomena that can lead to a better understanding of the world around us. Nowadays, with the increasing progress of computers, we are able to study various phenomena in very complex matrices such as biological systems and nanotechnology, and it is obvious that conducting such studies first of all requires a broad understanding of physical and chemical phenomena, the invention and innovation of new study methods and documented and targeted analysis. and also with this method, the results of various tests that are conducted experimentally can be predicted to a large extent and save money and time to a large extent.

    In this thesis, using QSAR studies on the derivatives of suitable structures for the treatment of diabetes, it is suggested to the esteemed pharmaceutical manufacturer to select and manufacture drugs from the most suitable structures. It should be noted that in this research, new and combined statistical methods have been used to analyze and predict the structures.

    Abstract

    In this research, the quantitative structure-activity relationship (QSAR) in pyrrolo[3,2-d]pyrimidine-7-carbonitrile derivatives has been studied. Genetic algorithm (GA), artificial neural networks (ANN) and stepwise multiple linear regression (MLR) were developed and used for linear and non-linear QSAR models. Using DFT method (B3LYP) and basis series 6-31G, we obtained the optimal structures of these derivatives. Hyperchem, ChemOffice, Gaussian 03W and Dragon software have been used to optimize molecules and calculate quantum chemistry descriptors. Finally, Unscrambler software was used for data analysis. Train RMSE and test RMSE with GA-ANN model were 0.1406 and 0.3519, respectively, and R2 parameter was 0.81. Also, the values ??of R and R2 with the GA-stepwise MLR model were obtained as 0.79 and 0.58, respectively. The GA-ANN model was found to be the most favorable method compared to other statistical methods.

    Generally, with the tests performed with GA-PLS, GA-PCR and Jackknife methods in different layers and different goals, the following compounds have the least possible deviation and are predicted as the best compounds for drug manufacturing

    1-1- Introduction

    Computational chemistry is a branch of chemistry that tries to solve chemistry problems with the help of computers. In this field, computers are used to predict molecular structure, molecular properties, and chemical reactions. In this field, the results of pure chemistry that have come in the form of effective computer programs are used to calculate the structure and properties of molecules, while their results usually complement the information obtained from chemical experiments, but in some cases it can lead to the prediction of unseen chemical phenomena. Therefore, computational chemistry can help laboratory chemistry and compete with experimental chemistry in finding new chemical topics. Computational chemistry includes molecular modeling, methods computing and designing molecules with the help of computers, as well as chemical data and designing organic syntheses, this field is also widely used for the design of drugs, catalysts and new materials [1]. and other fields of chemistry, there is still no ability to solve problems completely, and in order to implement some problems, very complex systems are needed, the implementation of which depends on spending a lot of money and extensive studies. To solve this problem, chemometrics calculation methods can be useful.. The statistical and mathematical analysis of chemical data is usually referred to as chemometrics. In other words, chemometrics is an efficient method for summarizing useful information from a specific data series and predicting other data series. In fact, the goal of chemometrics is to improve measurement processes and extract more useful chemical information from physical and chemical measured data. Chemometrics was first used by a Swedish scientist named Wold [2] in 1972 and developed by Kowalski [3], and in 1974 the International Association of Chemometrics [4] was established. In 1974 in Italy, two groups of scientists named Forina [5] and Clementi [6] started to work in this field and since 1980 the knowledge of chemometrics has developed very fast [2]. Several definitions have been given for chemometrics that are often used in analytical texts. One of the most comprehensive definitions is as follows:

    Chemometrics is a branch of chemistry that uses mathematics, statistics and logic to achieve the following results:

    a) design and select optimal experimental processes.

    b) provide maximum chemical information obtainable from the analysis of chemical information.

    C) ??get more information about chemical systems.

    1-2-1- Applications of chemometrics

    Chemometrics is used in various branches of chemistry, some of these applications include controlling processes, analyzing and recognizing patterns, processing signals and optimizing conditions. One of the important fields of application of chemometrics is in studies that relate the properties of molecules to their structural characteristics. One of the most important applications of chemometrics is the quantitative relationship of activity structure [7], with which mathematical models, chemical structure, biological activity, electronic and so on. calculated and determined by this method. The purpose of QSAR is to create a logical relationship between the quantities or properties of compounds (activity) and their chemical structure, and this law is used for new molecules. In this work, qsar study has been done on pyrrolo[3,2-d]pyrimidine-7-carbonitrile derivatives for anti-diabetic drugs. Genetic algorithm (ga), artificial neural network (ann), stepwise multiple linear regression (stepwise-mlr) were used to create non-linear and linear qsar models. For this purpose, ab initio geometry optimization was performed at b3lyp level with a known basis set (6-31G). Hyperchem, chemoffice and gaussian 03W softwares were used for geometry optimization of the molecules and calculation of the quantum chemical descriptors. Finally, unscrambler program was used for analysis of data. The root-mean square errors of the training set and the test set for ga-ann model using jack-knife method, were 0.1425, 0.3519 and r2 was 0.81. Also, the r and r2 values ??in the gas phase were obtained 0.79, 0.58 from ga-stepwise-mlr model. According to the obtained results, we find out ga-ann model is the most favorable method towards the other statistical methods. General studies with ga-pcr methods and ga-pls and jack-knife in different layers and different goals following compounds have the lowest deviation from the best ingredients to make the drug are advised.

  • Contents & References of QSAR study on pyrrolo[3,2-d]pyrimidine-7-carbonitrile derivatives for antidiabetic drugs

    List:

    Preface 1

    Abstract. 2

    Chapter One: Generalities

    1-1- Introduction. 5

    1-2- Chemometrics. 5

    1-2-1- Applications of chemometrics. 6

    1-3- The advantages of computational methods compared to laboratory methods. 6

    1-4- QSAR. 7

    1-5- Regression. 7

    1-6- Parametric methods. 8

    1-6-1- Single-variable and multi-variable calibration. 9

    1-6-2- Classical Least Squares (CLS) 9

    1-6-3- Inverse Least Squares (ILS) 9

    1-6-4- Multiple Linear Regression (MLR) 9

    1-6-5- Partial Least Squares (PLS) 10

    1-6-6- Principal Component Analysis (PCA) 10

    1-6-7-Principal component regression (PCR) 11

    1-6-8- Non-linear multivariate regression (MNR) 12

    1-6-9- Fuzzy logic. 12

    1-6-9-1-Applications of fuzzy logic. 13

    1-6-10- Artificial Neural Networks (ANN) 13

    1-6-10-1- Features of neural network. 14

    1-6-10-2- Advantages of neural network. 16

    1-6-10-3- Neural network applications. 16

    1-6-11- Genetic algorithm (GA) 17

    1-6-11-1- Darwin's laws. 18

    1-6-11-2- Features of genetic algorithm. 18

    1-6-11-3- Strengths of genetic algorithms. 20

    1-6-11-4- Limitations of genetic algorithms. 21

    1-6-11-5- selection methods for genetic algorithm. 21

    1-7- Diabetes. 22

    1-7-1- Classification and etiology of diabetes. 23

    1-7-1-1- Type one diabetes. 24

    1-7-1-2- type two diabetes. 25

    1-7-1-3- Gestational diabetes. 25

    1-7-1-4- other types of diabetes. 26

    1-8- Status of diabetes in the world. 29

    1-9- Death caused by diabetes. 29

    1-10- The costs of diabetes. 29

    1-11- Diabetes prevention and control. 30

    1-12- Medicines 30

    1-12-1-Sulfonylureas 31

    1-12-2- Biguanides 32

    1-12-3- Acarbose 32

    1-12-4- Thiazolidinediones (TZD) 33

    1-12-5- Meglitinides 33

    Chapter Two: Methodology

    2-1- Drawing derivatives. 36

    2-1-1- Add method and optimize derivatives. 36

    2-1-2- Adding molecular descriptors to derivatives. 36

    2-1-3- Making matrix and screening descriptors for derivatives. 37

    2-1-4- GA calculations. 38

    2-1-5- GA-ANN calculations. 39

    2-1-6- Jackknife calculations. 39

    2-1-7- GA-MLR calculations. 39

    2-1-8- Analysis with PLS, PCR and MLR methods. 40

    2-1-9- Analysis with GA-MCR, GA-PLS, GA-PCR, GA-MLR and GA-RS methods. 40

    2-1-10- Predicting the structure 40

    Second part: discussion and conclusion

    2-2- Discussion and conclusion. 42

    Suggestions for future work. 124

    Sources and sources. 125

    Source:

     

    Abdi H, Williams L.J. "Principal component analysis". Wiley Interdisciplinary Reviews: Computational Statistics, 2 (2010) 433–459.

    Ajmani S, Jadhav K, Kulkarni S, A. "Group-Based QSAR (G-QSAR): Mitigating Interpretation Challenges in QSAR". QSAR & Combinatorial Science, 28 (2008) 36–51.

    Albert K. J, Lewis N. S, Schauer C. L, Sotzing G. A, Stitzel S. E, Vaid T. P, Walt D. R. Chem Rev. 100 (2000) 2595-2626.

    Andersson C. A, Bro R. Journal Chemometrics, 14 (2000) 103.

    Bertsekas D. P, Tsitsiklis J. N. Neuro-dynamic programming. Athena Scientific, 8 (1996) 512.

    Bordogna G, Pasi G. Handling vagueness in information retrieval systems. In: Proceedings of the Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, 20-23 (1995) 110-114.

    Cano J. R, Herrera F, Lozano M. Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study. IEEE Transactions on Evolutionary Computation, 7 (2003) 561-575.

    Christopher J. Cramer Essentials of Computational Chemistry, John Wiley & Sons, 45 (2002) 23.

    Cox D. R. Principles of Statistical Inference, Cambridge University Press, 2 (2006) 521.

    Diabetes Blue Circle Symbol. . International Diabetes Federation, 17 March 2006

    Diagnosis and Classification of Diabetes Mellitus". Diabetes Care 33Diabetes Care 33 (Supplement_1): S62–S69. 2009. DOI:10.2337/dc10-S062.  ISSN 0149-5992.

    E.A.M. Gale. "Drug-induced diabetes". European Association for the Study of Diabetes.  

    Eiben E, Hinterding R, Michalewicz Z. "Parameter Control in Evolutionary Algorithms", IEEE Transactions on Evolutionary Computation, IEEE, 3 (1999) 124-141

    Fraser A. "Simulation of genetic systems by automatic digital computers. I. Introduction". Aust. J. Biol. Sci, 10 (1957) 484–491.

    Galleri, L.; Sebastiani, G.; Vendrame, F.; Grieco, FA.; Spagnuolo, I.; Dotta, F. (2012). "Viral infections and diabetes." Adv Exp Med Biol 771: 252-71. PMID 23393684

    Galton F. "Kinship and Correlation (reprinted 1989)". Statistical Science (Institute of Mathematical Statistics), 4 (1989) 80–86.

    Geladi P, Esbensen K. The start and early history of chemometrics: Journal Chemometrics, 4 (2005) 337–354.

    Henzen C (2012). "Monogenic diabetes mellitus due to defects in insulin secretion". Swiss Med Wkly 142: w13690. DOI:10.4414/smw.2012.13690. PMID 23037711

    Hopfield J. Neural networks and physical systems with emergent collective computational abilities. Proc. NatL Acad. Sci. USA Vol. 79 (1982) 2554-2558.

    Lambert, Paul; Bingley, Polly J (2002). "What is Type 1 Diabetes?". Medicine 30 (1): 1–5. DOI: 10.1383/medc.30.1.1.28264. ISSN 13573039

    Lee, A. J.; Hiscock, R. J.; Wein, P.; Walker, S. P.; Permezel, M. (2007). "Gestational Diabetes Mellitus: Clinical Predictors and Long-Term Risk of Developing Type 2 Diabetes: A retrospective cohort study using survival analysis". Diabetes Care 30(4):878–883. DOI:10.2337/dc06-1816. ISSN 0149-5992

    Lin S. Computer solutions of the traveling salesman problem. Bell Systems Tech, 44 (1965) 2245–2269.

    Maraschin, Jde F. (2012). "Classification of diabetes." Adv Exp Med Biol 771: 12-9. PMID 23393666

    Massy W. F. Principal components regression in exploratory statistical research. J. Amer. Stat. Assoc, 60 (1965) 234-256.

    McMinn P. Search-based Software Test Data Generation: A Survey. Journal of Software Testing Verification and Reliability, 14 (2004) 105-156.

    Melanitou, Evie; Fain, Pam; Eisenbarth, George S (2003). "Genetics of Type 1A (immune mediated) diabetes". Journal of Autoimmunity 21 (2): 93–98. DOI:10.1016/S0896-8411(03)00097-0. ISSN 08968411

    Metzger, B. E.; Buchanan, T. A.; Coustan, D.R.; de Leiva, A.; Dunger, D.B.; Hadden, D.R.; Hod, M.; Kitzmiller, J.L. et al (2007). "Summary and Recommendations of the Fifth International Workshop-Conference on Gestational Diabetes Mellitus". Diabetes Care 30 (Supplement_2): S251–S260. DOI:10.2337/dc07-s225. ISSN 0149-5992

    Neubig R. R. International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification.

    Northcutt W. The Darwin Awards: Evolution in Action. New York City: PLUME (The Penguin Group). 5 (2000) 2-6.

    Resmini, Eugenia; Minuto, Francesco; Colao, Annamaria; Ferrone, Diego (2009). "Secondary diabetes associated with principal endocrinopathies: the impact of new treatment modalities". Acta Diabetologica 46 (2): 85–95. DOI:10.1007/s00592-009-0112-9. ISSN 0940-5429

    Ross G (June 2006). "Gestational diabetes". Aust Fam Physician 35 (6): 392–6. PMID 16751853

    Roy P. P, Leonard J. T, Roy K. "Exploring the impact of size of training sets for the development of predictive QSAR models". Chemometrics and Intelligent Laboratory Systems, 90 (2008) 31–42.

    Sampson UK, Linton MF, Fazio S (July 2011). "Are statins diabetogenic?". Curr. Opin. Cardiol. 26 (4): 342–7. DOI:10.1097/HCO.0b013e3283470359. PMC 3341610. PMID 21499090

    Schafer, Andrew I. ; Lee A. Fleisher MD (2011). Goldman's Cecil Medicine: Expert Consult Premium Edition - Enhanced Online Features and Print, Single Volume (Cecil Textbook of Medicine). Philadelphia: Saunders. pp. e237-1. ISBN 1-4377-1604-0

    Schafer, Andrew I

QSAR study on pyrrolo[3,2-d]pyrimidine-7-carbonitrile derivatives for antidiabetic drugs
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