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June , 200 7

Introducing Parameter-Free Linear Relationship for 3D QSAR. Ödön Farkas farkas@chem.elte.hu Group members: Imre Jákli, Adrián Kalászi and Gábor Imre Eötvös Loránd University, Institute of Chemistry, Laboratory of Chemical Informatics. June , 200 7. Contents. PFLR for 3D QSAR

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June , 200 7

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

  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 Pulay’s 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 Dx change in the descriptor space implies the corresponding Dy change in activity

  5. PFLR’s descriptor interpolation x-x0 r Dx=P(x-x0) x* The x descriptor can be approximated via interpolation in the subspace of desciptor changes: The Dx change, the projection of the descriptor change, in the descriptor space implies the corresponding Dy change in activity s x0 x x1

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

  7. PFLR equations Coefficients, cj, 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 R2 is 1.0, only LOO Q2 is meaningful Automatic overlay of molecules SpatialSearch algorithm Automatic generation of 3D structures and conformers Marvin’s conformers plugin Automatic selection of conformers Automatic selection of training molecules based on extrapolation ratio Other features

  12. J. J. Sutherland, L. A. O’Brien and D.F. Weaver, “A Comparison of Methods for Modeling Quantitative Structure-Activity Relationships” J. Med. Chem. 2004, 47, 5541-5554 Validation – CoMFA/ComSIA results

  13. Validation – PFLR results

  14. Validation – PFLR results with automatic ovarlay

  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 researchgrant • GVOP-3.1.1.-2004-05-0451/3.0 • Infopark Fundation grants • Öveges fellowships

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