Ligand based drug desighning

Ligand based drug designing

1. Ligand based drug design : Structure Activity Relationship- Molecular finger print and similarity searches, Pharmacophore modeling, and QSAR (Quantitative Structure Activity Relationship) Submitted by : Miss Priyal Sethiya M.Sc. Ⅲ Sem Biotechnology Enrollment no.: 19LSC5bit1004 http://www.free-powerpoint-templates-design.com Submitted to: Dr. Ashish Warghne Assistant professor Faculty of Life Sciences Mandsaur University, Mandsaur

2. Contents 02 Pharmacophore Contents 03 QSAR (Quantitative structure activity relationship) Contents 04 Introduction of ligand based drug design Contents 01 Molecular finger print and similarity searches

3. A basic introduction to drugs, drug targets, and molecular interactions. https://youtu.be/u49k72rUdyc

4. Ligand Based drug design Ligand based drug design is an approach used in the absence of the receptor 3D information and it relies on knowledge of molecules that bind to the biological target of interest. 3D quantitative structure activity relationships (3D QSAR) and pharmacophore modeling are the most important and widely used tools in ligand based drug design. They can provide predictive models suitable for lead identification and optimization  Molecular fingerprint and structure search Pharmacophore QSAR

5. Molecular Descriptors Molecule, walk count, electro negativities, polarizabilities, symmetry, atom distribution, topological charge indices, functional group composition, aromaticity indices, solvation properties, Molecular weight, geometry, volume, surface area, ring content, rotatable bonds, intra-atomic distances, bond distances, atom type, planar and non planar systems Measurable and Calculated properties

6. Molecular Descriptors LBDD Examples Molecule, walk count, electro negativities, polarizabilities, symmetry, atom distribution, topological charge indices, functional group composition, aromaticity indices, solvation properties Examples Molecular weight, geometry, volume, surface area, ring content, rotatable bonds, intra-atomic distances, bond distances, atom type, planar and non planar systems LBDD techniques use different methods for describing features of small molecules using computational algorithms that balance efficiency and information content . Molecular descriptors can be structural as well as phytochemical. Generated through knowledge based, MM, and QM based tools. Classified based on the ‘dimensionality “of the chemical representation from which they are computed, 1D, 2D, 3D and 4D. LIGAND-BASED DRUG DESIGN

7. Ligand-based drug design Initial two steps involving the process called ‘Model Building’ while the final two steps are known as ‘database screening’ Few new compound that match this description Compound that match the description will also be active. Begin with biologically active compounds. Describe what chemistry those compounds have in common

8. Ligand-based drug design approaches Molecular finger print and similarity searches Quantitative structure activity relationship (QSAR) Pharmacophore modeling

9. Molecular finger print-based techniques attempt to represent molecules in such a way as allow rapid structural comparison in an effort to identify structurally similar molecules or to cluster collection based on structural similarity. Less computationally expensive than pharmacophore mapping or QSAR models. Considers all parts of the molecule equally and avoid focusing only on part of a molecule that are thought to be important for activity Are hypothesis derived Qualitative approach Rely entirely on chemical structure and omit compound known biologic activity, Molecular finger print and similarity searches

10. Molecular finger print and similarity searches

11. Pharmacophore modeling The rationale behind the pharmacophore model is that the inhibitors against a specific target may possess a set of common physicochemical properties responsible for their mode of binding The typical types of interaction sites recognized by pharmacophore software include: Hydrogen bond acceptor (A) Hydrogen bond donor (D) Hydrophobic (H) Negative ionizable (N) Aromatic rings (R) Positive ionizable (P)

12. Pharmacophore mapping/ modeling involves three processes These methods use activity for the development of pharmacophore models Finding the features required for particular biological activity. Determining the molecular conformation required (i.e., the bioactive conformation); and Developing a superposition or alignment rules for the series of compounds.

13. Divers set of compound covering wide range of activities and can predict binding affinity from the model. QUANTITATIVE MODEL Set highly active compounds and to identify features necessary for biological action. QUALITATIVE MODEL Pharmacophore modeling

14. QSAR ( Quantitative Structure Activity Relationship) 25% The QSAR aims to identify and quantify the physiochemical properties of the drug and to see whether these properties have any effect on the biological activity. QSAR- Ligand Based Drug Design Introduction The quantitative structure activity relationship quantifies the relationship between physiochemical properties and biological activities. It is said to be the mathematical expression between the biological activity and the measurable physiological parameters. A QSAR model is a mathematical formalization of relationship. Learning a target function (f) that best map input variables (X) to an output variable (Y). All QSAR models assume that :- Activity=f(molecular structure) Where activity is dependent (Y) and molecular structure is independent variable (X). Examples of QSAR are: CoMFA, COMSIA, MSI Catalyst, Serias.

15. Various parameters of QSAR Lipophitic parameters like partition coefficient Electronic parameters Like dipole moment Steric parameters Like molar refractivity

16. Activity of drug is often related to P Only 2D-Structure considered: Unavailability of appropriate physicochemical parameter Describing drug receptor interaction No representation of stereochemistry, Higher risk of chance correlations, No graphical output, Require considerable knowledge of perform deployment X axis- log(1/C) Y axis- log (1/C)=m.log(P+C) 2-D QSAR 1-D QSAR X axis- binary fingerprint, Pharmacophore, connectivity indices, e.t.c. Y axis- biological or property Binding increases as log increases Binding is greater for hydrophobic drugs Kernal based QSAR

17. Types- Atom based QSAR Field based QSAR 3D-based QSAR:- In aQSAR , molecules atoms are treated as sphere, and categorisized as one of the following: H-bond donor Hydrophobic/non polar Positive ionic Electron withdrawing/h-bond acceptor Misc Field based QSAR ( fQSAR) :- Two slightly different methods are implemented for fQSAR: Force field (i.e., CoMA): steric and electrostatic field are evaluated at grid point surrounding the ligand. Gaussian (i.e., CoMSIA): property based field weighted as a Gaussian function of the distance between the grid point and the atom. 3D- Based QSAR- Methods

18. 4D-QSAR Additionally including ensemble if ligand configurations in 3D-QSAR 5D-QSAR Representing different induce- fit models in 4D-QSAR Dimen sionality LQTA-QSAR: A New 4D-QSAR Methodology (João Paulo A. Martins et. all.) . 6D-QSAR Further incorporation solvation models in 5D-QSAR

19. QSAR Work flow QSAR Generation of descriptors (Padel descriptor, ChEMBL database) Feature selection Construction of model (multiple regression analysis) Validation of model Collection of ligand (ChEMBL, TIMBAL databases) QSAR QSAR QSAR QSAR QSAR QSAR QSAR QSAR QSAR Ligand with known experimental data Objective feature selection Subjective feature selection 770 properties

20. Advantages of QSAR Quantifying the relationship between drug structure and biological activity provides an understanding of the effect of structure on activity, which may not be straightforward when large amounts of data are generated. QSAR allow calculation in advance, what is the biological activity of the novel drug analogs maybe. The cutting down the number of analogs that have to be made by the chemists. Thus it help the medical chemists in perdition of the result The results can be used to help understand interaction between functional groups in the molecules of the greatest activity, with those of their target.

21. Software MSI- Catalyst, serius Tripos- CoMFA, VoLSurf Dock- Kuntz Flex – lengauer LigandFit- MSI Catalyst COMPUTER DOCKING SOFTWARE . DOCKING . QSAR and 3D-QSAR

22. Data processing To obtain reliable QSAR models it is important to handle the data with great care. Data pre-processing: ensure the integrity of the data set before proceeding further with the analysis. It includes: Data cleaning Errors of inconsistencies such as missing data, incomplete data etc. Data transformation There exists a great deal of variability in the range and distribution of each variable in the data set. Feature selection

23. Ligand based drug design Applications Relies on knowledge of other molecules that bind to the biological target of interest. These other molecule may be used to derive a pharmacophore model. Alternatively , a QSAR relationship, in which a correlation between calculate properties of molecules and there is experimentally determined biological activity, may be derived. QSAR may be used to predict the activity of new analogues.

24. Summary 01 CONTENTS Molecular descriptor 02 CONTENTS Ligand based drug design 03 CONTENTS Molecular fingerprints and structure searches 04 CONTENTS Quantitative structural activity relationship 05 CONTENTS Data processing Molecular descriptors are numerical values that characterize properties of molecules An approach used in the absence of the receptor 3D information and it relies on knowledge of molecules that bind to the biological target of interest. Molecular fingerprints are a way of encoding the structure of a molecule. The most common type of fingerprint is a series of binary digits (bits) that represent the presence or absence of particular substructures in the molecule. The process of studying a series of molecules of different structure and properties and attempting to find empirical relationship between structure and property To obtain reliable QSAR models it is important to handle the data with great care

25. Reference Computer aided drug design- ppt download slide player Drug design by malla reddy college of pharmacy slide share QSAR- by Sarmatia de chakravarti slide share Quantitative structure activity relationship by eswaran murugesan slide share Basic of ligand based drug design: 3D QSAR by JSS academy , you tube QSAR -1 by M.Michael gromiha , NPTEL bioinformatics- ITTM YouTube Structure and ligand based drug design by polamarasetty et al NCBI Structure based drug design slide share Current medical chemistry, ligand based drug design, by ELC VOH YouTube

26. THANK YOU #stay home and stay safe By: Priyal Sethiya