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Design of experiments applied to QSAR

In The Name OF God

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