contoh materi kul qsar

1. Introduction to QSAR

2. QSAR
Application:
• Used to understand how molecular structure influences the mode of
action, or the biological mechanism, of studied chemicals.
• Extensively used in the process of drug invention  hit to lead
optimization and identification
• The statistical model is developed using correlation studies
and finally, the biological activities of the new compounds is
predicted

4. Model
A dataset consisting of molecules, structurally similar,
whose QSAR model needs to be developed are to be
prepared for the QSAR study.
Once the molecules are finalized, the parameters of the
molecules known as the descriptors are calculated, which
can be the overall structural properties of the molecules, or
the different conformational properties of the molecules.
Biological activity (IC50, EC50, etc.): definite and known
biological activity value that can be correlated with the
molecular descriptors generated, to develop a good and
reliable QSAR model.
Chemometrics can be used to develop a mathematical
correlation between the biological activity and the
descriptors calculated.
Generate the data to predict the activity of any unknown
compound belonging to the same class of molecules as the
data set in terms of the same disease, the same type of
biological activity, same scaffold, same pharmacophore, etc.

5. Resume
QSAR
 helps finding the compounds with desired properties
 chemical information and its association with biological
activity.
It involves statistic and mathematical methods.
The generated QSAR models
 used to predict and classify the biological activities of new chemical
compounds.

6. • The physicochemical properties such as partition coefficient and
presence or absence of certain chemical features are taken into
consideration.

7. Steps:
1. Modelling the existing data,
2. Fitting structure as best as possible  by using molecular descriptors
 understand the correlation/causality in terms of mechanism

8. • [126]. [126]. The best QSAR modeling depends upon the
increased number of data sets with pharmacological
activities assessed from the experiments, the test set, and
training ligands must be selected properly. There should be no
correlation of the physicochemical features of the compounds
which may leads to overfitting data, and finally, the applicability
as well as predictivity of the final must be assessed using
internal and external standards [125]. Depending upon the
derived descriptor, the QSAR modeling is classified into six
types which are as follows [127].

9. “predictive QSAR” / “statistical QSAR”
If you are focus on the development of
• more heterogeneous compounds and
• focused mainly on validation
QSAR can be usefully  to screen and rank more diversified
compounds
• This approach is very useful for big data sets, in prioritizing, for
experimental tests, those compounds that are in silico highlighted as
potentially more dangerous.