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Design of experiments applied to QSAR
In The Name OF God
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2Chemometrices:
Signal processing
Classification & pattern reccognation
Experimental design
Multivariative calibration
Quantitative Structure - Activity Relationship(QSAR)
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Quantitative Structure-Activity Relationship (QSAR) Models
Set of molecules
Y parameterMolecular Descriptors (Xi)
QSARY = f(Xi)
InterpretationPrediction
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Step1: Formulation of Classes of Similar Compounds
Step 2: Structural Description and Definition of Design Variables
Step 3 :Selection of the Training Set of Compounds
Step 4:Biological Testing
Step 5 :QSAR Development
Step6 :Validation and Predictions for Non-Tested Compounds
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Data set
Test Set
External
Internal
Training Set
Data Set
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well-balanced distribution & contain representative compound
systematically & simultaneously
Selection of the Training Set of Compounds
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Drug Design
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9set of neuropeptides
Relative activity against NK1 receptors
o 29 full FD 512 structures o 29-4 fractional design 32 structures
512-32 = 480
9 of 11 positions
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Set of 32 training structures
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Same molecular set full molecular library
Formal Inference-based Recursive Modeling (FIRM) methodology
Same key points
not preserve exactly the same ordering or magnitude of Importance
Second order interactions
QSAR:Same molecular set
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Y = 25.094 + 8.031 [Leu] + 8.094 [Phe-2] + 5.781 [Leu] [Phe-2] + 11.593 [Phe-1] + 9.094 [Gln-2] + 7.844 [Phe-1] [Gln-2] + 5.031 [Gln-2] [Gln-1] + 7.031 [Pro-2] [Phe-1]
Interaction effect important
Experimental Response Variability = 5%
Variation ► Least a change of 5% in the molecular activity
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Predictive capability of a QSAR model
Strategy used for selecting the compounds in the training set
Dipeptides (Inhibiting the Angiotensin Converting Enzyme)
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FFD
Table 1. The 2 4-1 FFD for z1 , and z2 for a peptide varied at two positions (I and 2). The design is cornpleinentcd with a centcr point. Dipeptidcs (DP) corresponding approxiniatcly to the settings of the angiotcnsin data are givcn.
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Table 2. The 24 FD for z1 , and z2 at position 1 and 2. Peptide analogs, approximatcly corresponding to thc design matrix, were selected from the set of 48 bitter dipcptidcs.
FD
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Full Factorial Design(FD)
Fractional Factorial Design (FFD)
change-one-separate-feature-at-a-time (COST) design
Training Test R2 Q2
FD 2 4 16 42 0.78 0.68
FFD 2 4-1 + 1
9 49 0.97 0.53
COST 34 34 24 0.64 0.52