1 / 18

Design of experiments applied to QSAR

In The Name OF God. Design of experiments applied to QSAR. Chemometrices: Signal processing Classification & pattern reccognation Experimental design Multivariative calibration Quantitative Structure - Activity Relationship(QSAR).

brenden-pope
Download Presentation

Design of experiments applied to QSAR

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. In The Name OF God Design of experiments applied to QSAR

  2. Chemometrices: • Signal processing • Classification & pattern reccognation • Experimental design • Multivariative calibration • Quantitative Structure - Activity Relationship(QSAR)

  3. Quantitative Structure-ActivityRelationship (QSAR) Models Set of molecules  Y parameter Molecular Descriptors (Xi) QSAR Y = f(Xi) Interpretation Prediction

  4. 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

  5. Data Set

  6. Selection of the Training Set of Compounds • well-balanced distribution & contain representative compound • systematically & simultaneously

  7. Drug Design

  8. set of neuropeptides Relative activity against NK1 receptors • 29 full FD 512 structures • 29-4fractional design 32 structures 512-32 = 480 9 of 11 positions

  9. Set of 32 training structures

  10. QSAR:Same molecular set • Samemolecular set • full molecular library Formal Inference-based Recursive Modeling (FIRM) methodology • Samekey points • not preserve exactly the same ordering or magnitude of Importance Second order interactions

  11. 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

  12. Dipeptides (Inhibiting the Angiotensin Converting Enzyme) • Predictive capability of a QSAR model • Strategy used for selecting the compounds in the training set

  13. FFD • Table 1. The 2 4-1FFDfor z1, and z2for a peptide varied at twopositions (I and 2). The design is cornpleinentcd with a centcr point. Dipeptidcs (DP) corresponding approxiniatcly to the settings of the angiotcnsin data are givcn.

  14. FD Table 2. The 24FD 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.

  15. Full Factorial Design(FD) • Fractional Factorial Design (FFD) • change-one-separate-feature-at-a-time (COST) design

More Related