This document discusses quantitative structure-activity relationships (QSAR) modeling techniques. It introduces 2D-QSAR which uses molecular descriptors to correlate structure and activity. It also discusses 3D-QSAR techniques like CoMFA and CoMSIA which use 3D molecular fields/properties and statistical methods like PLS to model activity. These techniques are useful for drug design, virtual screening, and predicting absorption, distribution, metabolism, excretion properties.

1. Presented By
Rani T. Bhagat
M. Pharmacy
(Pharmaceutical Chemistry)

2. INTRODUCTION
 Quantitative Structure Activity Relationship (QSAR) are
mathematical relationship between chemical structure and
pharmacological activity in a quantitative manner for series of
compound. The fundamental principle involved is difference in structural
properties is responsible for variations in biological activities of the
compound.
Biological activity=Functions(parameters)
 Physico -chemical parameters:
Hydrophobicity of substituents
Electronic properties of substituents
Hydrophobicity of the molecule
Steric Properties of substituents

4. 2D- QSAR
 It is powerful tool for explaining the relationship between
chemical structure and experimental observation .
 The numerical descriptors used to translate a chemical
structure into mathematical .
 2D QSAR models are used routinely during the process of
optimization of a chemical series towards a candidate for clinical
trials.
 It can be classified based on parameters and description as:
1)2D vs 3D and Classical vs Non Classical
2)QSAR-QSPR-QSMR-QSTR
3)2D QSAR for drug design

5. Quantitative regression technique
Qualitative pattern recognition technique
Hammet relationship as linear free
energy relationship
Statistical parameters : Craig plot
Simple linear regression
Multiple Linear Regression (MLR)
Ordinary Least Squares (OLS)
Partial Least Squares (PLS)
Adaptive Least Squares (ALS)
Principle Component Analysis (PCA)
METHODS

6. 3D-QSAR
 In 3D QSAR ,3D properties of a molecule are considered as whole rather
than consideration individual substituents.
 3D-QSAR involve the analysis of the quantitative relationship between
the biological activity of a set of compound and their three – dimensional
properties using statistical correlation methods.
 3D QSAR revolve around the important features of a molecule , its
overall size and shape ,and its electronic properties .
 3D QSAR is an extension of classical QSAR which exploits the 3
dimensional properties of the the ligands to predict their biological
activity using robust stastical analysis like PLS.

7. 3D-QSAR
 3D-QSAR uses probe – based sampling within a molecular lattices
to determine three-dimentional properties of molecules and can
then correlate these 3D descriptors with biological activity.
 No QSAR model can replace the experimental technique are also
free from errors.
 Some of the major factors like desolvation energetics, temperature,
diffusion, transport ,pH ,salt concentration etc.which contribute to
all overall free energy of binding are difficult to handle ,and thus
usually ignored.
 Regardless of all such problems, QSAR become a useful alternative
approach.

9. CLASSIFICATION

10. CoMFA
 CoMFA (Comparative Molecular Field Analysis)
 In 1987, Cramer developed the predecessor of 3D approaches called Dynamic
Lattice – Oriented Molecular Modelling System (DYLOMMS) that involve the
use of PCA to extract vectors from the molecular interaction fields ,which are then
correlate with biological activity.
 Soon after be modified it by combining the two existing technique , partial
least square method used to develop a powerful 3D-QSAR methodology, CoMFA.
 CoMFA is that difference in target property
eg . Biological activity, are often closely related to equivalent change in shape
and strenght of non-covalent interaction fields surrounding the molecule.
 The molecule placed in cubic grid and the interaction energies between the
molecule and a defined probe are calculated for each grid point.

11. LIMITATION OF CoMFA
 Too many adjustable parameters like overall orientation ,Lattice placement,
step size ,probe atom type.
 Uncertainty in selection of compounds and variable .
 Fragmented contour maps with variables selection procedure .
 Hydrophobicity not well quantified.
 Cut-off limit used.
 Imperfections in potential energy function.
 Various practicle problem with PLS .
 Applicable only to in vitro data.

12. CoMSIA
 Comparative Molecular Similarity Indices Analysis (CoMSIA) was
developed to overcome certain limitation of CoMFA.
 In CoMFA ,molecular similarity indices calculated from modified
Steric , electrostatic, hydrophobic and hydrogen bonding properties.
 These indices are estimated indirectly by compairing the similarity
of each molecule in the dataset with a common probe atom
positioned at the interaction of a surrounding gridlattice.
 For computing similarity at all grid points,the mutual distance
between the probe atom and the atoms of the molecule in the
aligned dataset are also taken into account.

13. DIFFERENCE BETWEEN
CoMFA CoMSIA
Function type Lennard-Jomes
potential,
Coulomb potential
Gaussian
Description Interaction energy Similarity indices
Cut-off Required Not required
Field Steric , Electrostatic Steric , Electrostatic,
Hydrophobic
Contour map Not contiguous Contiguous
Model reproducibility Poor Good

14. APPLICATION
QSAR in Chromatography: Quantitative Structure-Retension
Relationship(QSRR).
 Used for predict the ADME Properties.
Used in drug designing, virtual screening.
 Used for prediction of Harmful Human Health Effects of
chemicals from Structures.
The role of QSAR methodology in the Regulatory
Assessment of Chemicals.
Used for optimize the properties of lead compounds