This document provides an overview of QSAR (Quantitative Structure-Activity Relationship) modeling, which establishes mathematical relationships between chemical structure descriptors of molecules and their biological activity. It discusses how QSAR analysis involves statistically analyzing properties of known active molecules to develop models that can then predict activity of new compounds. Examples of using QSAR to optimize ligand structures and select candidates for further testing are also provided.

1. QSAR
Abhijeet Kadam
TSEC
BioTechnology

2. Overview of Drug Design
QSAR
Pharmacophore Database Receptor
Mapping Docking Denova
Score
Bioavailability & tocicity checking
Hits

3. 3 Approaches
● Ligand based
● Structure based
● Denova

4. Ligand Based Approach
● Ligand Unknown
– 3D str generation
– Combinotorial chemistry
● Ligand Known
– Pharmacophore
– Database search
– QSAR

5. Ligand Based Approach
● Tries to identify characteristics common to known
ligands
– To use in screening for new or improved drugs
● If set of active ligand molecules is known for the
marcomolecule target, but littile or NO info of
structure of target, LBA is used
● 2 ways
– QSAR
– Pharmacophore

6. QSAR
● Developed by Hansch and co workers in sixties
● Started as a simple comparision between 2 or
more molecules using numbers
● QSAR establishes relationship between
– Ability of a molecule to perform its desired function
– And Properties of the molecule

7. QSAR
● Involves stastistical analysis of a set of
properties or DESCRIPTORS for a series of
biologically active molecules
● Drugs can be from highly active to inactive
● QSAR helps us to quantify the relationship
between structure and biological data
– This data is used optimize the groups that modulate
the activeness of molecule

8. QSAR
● Example
– Dimensionality – 1D, 2D, 3D
– Physio-chemical – Steric, electrical, hydrophobic
– Chemical
● Stastical model is developed
● And used to predict the activity of additional
compounds against the target

9. QSAR
● Example
– QSAR is used to predict which member of a series of
proposeed compounds are likely to be active
– And be selected for testing
– New data is received
– Used to refine the model
● QSAR methods
– POMONA college
– CLOGP program

10. QSAR
● Example
– QSAR is used to predict which member of a series of
proposeed compounds are likely to be active
– And be selected for testing
– New data is received
– Used to refine the model
● QSAR methods
– POMONA college
– CLOGP program