QSAR Statistical Method strictural activity relationship.pptx

structural activity relationship methods

1. QSAR STATISTICAL METHODS BY, Shravya Prabhakar Acharya NU24PHPY14 1 st M. Pharm , 2 nd Semester Dept. Of Pharmacology

2. Contents:- Introduction QSAR Statistical Methods Regression analysis Application of Regression analysis Partial Least Square Analysis (PLS) Application of PLS Other Methods References

3. Quantitative structure activity relationship (QSAR) is a strategy of the essential importance for chemistry and pharmacy, based on the idea that when we change a structure of a molecule then also the activity or property of the substance will be modified. QSAR are mathematical relationships between the physicochemical properties and pharmacological/biological activity in a quantitative manner for a series of compound. Biological activity=f(physicochemical properties and/or structure properties) Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation INTRODUCTION

4. QSAR STATISTICAL METHODS Regression analysis Partial least square Other methods Simple Regression Analysis Multiple Regression Cluster Analysis Principle Component Analyis Regression Based Analyisis Ordinary least square regression Generalized linear models

5. I . INTRODUCTION REGRESSION ANALYSIS In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. Regression analysis relates independent X variables with dependent Y variables. If two variables are involved, the variable that is basis of estimation is called the independent variable and the variable whose value is to be estimated is called as dependent variable. For any given values of X, the Y values are independent and follow a normal distribution curve.

6. DEFINITION OF REGRESSION ANALYSIS Regression analysis is a technique of studying the dependence of one variable (called dependent Y variable e.g. biological data) on one or more variables (called independent X variable e.g. physicochemical parameters) with a view to estimate or predict the average value of dependent variable in terms of known or fixed values of the independent variable. The dependent variable is also called as->>Explained>>Response>>Endogenous The independent variable is also called as->>Explanatory>>Regressor>>Exogenous

7. REGRESSION MODELS:- Regression models involve the following parameters and variables. The unknown parameter known as β , which may be a scalar or vector A regression model relates Y to a function of X and β . where; f = function β = unknown parameter X = independent variable Y = dependent variable Y≈ f(X, β)

8. Assume now that the vector of unknown parameters β is of length K, In order to perform a regression analysis the user must provide information about the dependent variable Y If N data points of the form (Y, X) are observed, where N < K, most classical approaches to regression analysis cannot be done If N = K data points are observed, and the function f is linear, the equations Y≈ f (X, β ) can be solved exactly rather than approximately. If N > K data points are observed, there is enough information in the data to estimate the unique value for β

9. REGRESSION ANALYSIS NON- LINEAR LINEAR MULTIPLE REGRESSION ANALYSIS SIMPLE REGRESSION ANALYSIS NON- LINEAR LINEAR

10. 1 . SIMPLE LINEAR REGRESSION MODEL:- In simple linear regression there is only single explanatory variable Simple linear regression is applied when you want to predict the value of one variable by using values of other variables.

11. SIMPLE LINEAR REGRESSION:- Simple linear regression for a derivation of these formulas Y i = β o + β 1 X i + ε i Where, Y i = Dependent variable β o = Population Y intercept β 1 = Population slope coefficient X i = Independent variable ε i = Random error term Linear component Random error component

12. 2. MULTIPLE LINEAR REGRESSION Multiple linear regression is the same idea as simple linear regression, except how you have several independent variables predicting the dependent variables. It is used when we want to predict the value of a variable based on the value of two or more other variables. Where, N = number of variable β o = intercept term β n = Coefficients for independent variable β = unknown parameter Y=β o + β 1 X 1 + β 2 Χ 2 +..........+ β n X n + ε

13. USES OF REGRESSION ANALYSIS- Regression analysis helps in establishing the relationship between two or more variables Regression analysis predicts the value of dependent variables from the values of independent variables Regression analysis is widely used as statistical tool in QSAR .

14. II . PARTIAL LEAST SQUARE ANALYSIS(PLS):- Partial least square analysis (PLS) is a method for constructing predictive models when the factors are many and linear It is a recent technique that generalizes and combines features from principal component analysis and multiple regression Goal-predict set of dependent variables Y from a set of independent variables X describe their common structure Used to Find the fundamental relations between the two variables/matrices (X and Y) COMPACT (computer optimized molecular parametric analysis of chemical toxicity), a PLS approach, is described to predict different forms of toxicity.

15. SOFTWARES USED IN PLS:- Its application depends on the availability of software SIMCA-P UNSCRAMBLER SPM SAS PROC PLS

16. APPLICATIONS OF PLS:- PLS is used to find the fundamental relations between two matrices (X and Y) The PLS model will try to find the maximum multidimensional direction in the X. space and the maximum multidimensional direction in the Y space PLS regression is widely used especially in the case, where the number of independent variables is significantly larger than the number of data points and related areas It is also used in bioinformatics, neuroscience and anthropology etc

17. III . OTHER MULTIVARIABLE STATISTICAL MODELS Cluster analysis Principal component analysis Regression based analysis methods a) Ordinary least square regression b) Generalized linear models

18. CLUSTER ANALYSIS:- Cluster analysis is a group of multivariate techniques whose primary purpose is to group objects based on the properties they possess. In cluster analysis, the grouping is based on the distance (proximity) It is the main task of exploratory data mining, statistical data analysis, pattern recognition, image analysis, bioinformatics, data compression and computer graphics

19. ROLE &APPLICATIONS OF CLUSTER ANALYSIS:- 1. Data reduction 2. Hypotheses generation APPLICATIONS- 1.Medicine 2. Analysis of antimicrobial activity 3. Biology & bioinformatics 4. Climate 5. Crime analysis & transcriptomic etc

20. 2. PRINCIPAL COMPONENT ANALYSIS:- It is an exploratory technique used to reduce the dimensionality of data set to 2D or 3D PCA is a procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components Objective of PCA:- PCA is a dimensionality reduction or data compression method Goal of PCA:- To select a subset of variables from a larger set, based on which original variables have the highest correlations with the principal component

21. APPLICATIONS OF PCA:- Neuroscience: A variant of PCA is used in neuroscience to identify the specific properties of a stimulus that increase a neuron's probability of generating an action potential. This technique is known as spike triggered covariance analysis. In neuroscience, PCA is also used to identify of a neuron from the shape of its action potential. 2. Quantitative finance: PCA can be directly applied to the risk management of interest.

22. 3. REGRESSION BASED ANALYSIS:- Ordinary least squares:- • In statistics, ordinary least squares (OLS) is a type of linear least squares method for estimating the unknown parameters in a linear regression model. • OLS is used in fields as diverse as economics (econometrics), data science, political science, and engineering (control theory and signal processing) etc b) Generalized linear model:- • In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution.

23. REFERENCES:- Patel HM, Noolvi MN, Sharma P, Jaiswal V, Bansal S, Lohan S, Kumar SS, Abbot V, Dhiman S, Bhardwaj V. Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery. Medicinal chemistry research. 2014:4991-5007. Tropsha A. QSAR in drug discovery. Drug design: Structure-and ligand-based approaches. 2010;1. Bastikar V, Bastikar A, Gupta P. Quantitative structure–activity relationship-based computational approaches. InComputational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV-2 Infection 2022 ( pp. 191-205). Academic Press.

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