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June, 2007 Ödön Farkas Group members: Imre Jákli, Adrián Kalászi and Gábor Imre Eötvös Loránd University, Institute of Chemistry, Laboratory.

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Presentation on theme: "June, 2007 Ödön Farkas Group members: Imre Jákli, Adrián Kalászi and Gábor Imre Eötvös Loránd University, Institute of Chemistry, Laboratory."— Presentation transcript:

1 June, 2007 Ödön Farkas Group members: Imre Jákli, Adrián Kalászi and Gábor Imre Eötvös Loránd University, Institute of Chemistry, Laboratory of Chemical Informatics Introducing Parameter-Free Linear Relationship for 3D QSAR

2 Contents PFLR for 3D QSAR Method introduction –PFLR –Descriptors –Descriptor representaion –Other features Validation against CoMFA/CoMSIA Future plans

3 Were PFLR came from? Related methods –Peter Pulays DIIS (Direct Inversion in the Itrative Subspace) for SCF convergence acceleration –GDIIS (Geometry DIIS) for geometry optimization –Perczel-Tusnády CCA (Convex Constraint Analysis) for decomposing spectra –…

4 PFLR in a nutshell PFLR only assumes a linear relationship between two spaces, like descriptors or scores, x, and activities, y : The x change in the descriptor space implies the corresponding y change in activity

5 The x descriptor can be approximated via interpolation in the subspace of desciptor changes: The x change, the projection of the descriptor change, in the descriptor space implies the corresponding y change in activity PFLRs descriptor interpolation x0x0 x1x1 s x x=P(x-x 0 ) x-x 0 x*x* r

6 PFLR equations The projector can be calculated as: where matrix X collects the descriptor changes, - stands for generalized inverse. The interpolated desciptor, x * is: where vector k collects the i coefficients.

7 PFLR equations Coefficients, c j, for the descriptors can be given as: The same combination of activities gives the prediction

8 Descriptors Current implementation uses 3 descriptors and separate interpolation –Steric (atomic positions) - 25% –Charge (calculated using Marvin) - 50% –6 pharmacophore types (assigned using JChem) - 25% Hydrogene bond donor and acceptor Positive and negative charge Hydrophobe Aromatic

9 Descriptor representation The descriptors are represented using a linear combination of 3 atom-centered Gaussians:

10 No need for computing linear combination of real vectors The only requirement is the definition of scalar product, calculated via analytic functions No need for grid representation of features Abstract vectorspace

11 Partial interpolation using closest descriptors Training R 2 is 1.0, only LOO Q 2 is meaningful Automatic overlay of molecules –SpatialSearch algorithm Automatic generation of 3D structures and conformers –Marvins conformers plugin Automatic selection of conformers Automatic selection of training molecules –based on extrapolation ratio Other features

12 J. J. Sutherland, L. A. OBrien and D.F. Weaver, A Comparison of Methods for Modeling Quantitative Structure-Activity Relationships J. Med. Chem. 2004, 47, Validation – CoMFA/ComSIA results CoMFACoMSIA basicCoMSIA extra traintestr 2 tests testr 2 tests testr 2 tests test ACE AchE BZR COX DHFR GPB THER THR

13 Validation – PFLR results CoMFA/ComSIAPFLR-3 r 2 tests testr 2 tests test ACE AchE BZR COX DHFR GPB THER THR

14 Validation – PFLR results with automatic ovarlay CoMFA/ComSIAPFLR-3PFLR-3-overlay r 2 tests testr 2 tests testr 2 tests test ACE AchE BZR COX DHFR GPB THER THR

15 Future plans Further testing in real-world environment First release during this summer –Instant JChem integration –command line tool –API PFLR for 2D descriptors –problems with the vector space Checking applicability for predicting toxicity –Seeking partners for joint EU grant application

16 Acknowledgements GVOP applied research grant –GVOP /3.0 Infopark Fundation grants Öveges fellowships


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