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Chapter 3 – Molecular Modeling

Chapter 3 – Molecular Modeling. Case Study: Dopamine D 3 Receptor Anthagonists. Today’s lecture. Dopamine D 3 Receptor Anthagonists Building a pharmacophore model 3D QSAR analysis. Dopamine Receptor. 5 different subtypes : D 1 , D 2 , D 3 , D 4 , D 5

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Chapter 3 – Molecular Modeling

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  1. Chapter 3 – Molecular Modeling Case Study: Dopamine D3 Receptor Anthagonists

  2. Today’slecture • Dopamine D3 Receptor Anthagonists • Building a pharmacophore model • 3D QSAR analysis

  3. Dopamine Receptor • 5 differentsubtypes: D1, D2, D3, D4, D5 • Defects is related to severaldiseases • Parkinson’sdisease, schizophrenia etc. • Medical treatment • Limited by side effects from drugs binding to varioussubreceptors • Needselectivity!

  4. Building a pharmacophore model • 5 ligands (D3 receptor antagonists) • Highaffinity • Knownsteric and electrostatic information • Structure: Highly potent

  5. Building a pharmacophore model • Strategy • Decomposemoleculeinto fragments • MolecularallignmentusingFlexS • One treatedflexible • One treated rigid

  6. Building a pharmacophore model • Rigid part • SYBYL: Simulatedannealing • Low T conformation • Twoclusters (conformationfamily) rigid

  7. Building a pharmacophore model • Flexible part: • Fitonto rigid part • FlexS flexible

  8. Building a pharmacophore model • The spacer • Generallyflexible • Examined in detail: quite rigid overlap

  9. Building a pharmacophore model • Simulatedannealingonbicyclic ring system • 3 conformations

  10. Building a pharmacophore model • Aromatic/Amidicresidue • Assumedplanar • Includethisrestriction in previousexamination planar

  11. Building a pharmacophore model • Systematicsearch • 10 degreeincrement • Tripos force field • → 992 conformations

  12. Building a pharmacophore model • Compound 1 fittedon all 992 conformationswithFlexS • Highestrated • = binding conformation of these fragments Compound 1

  13. Building a pharmacophore model • Nowwe have the conformation of all fragments • Recombine fragments • Pharmacophore model!

  14. Building a pharmacophore model • Molecularinteractionfieldswith GRID N-H C=O Basic nitrogen H-bondacceptor ST-127 ST-86 ST-205 ST-84

  15. Building a pharmacophore model ST-127 ST-86 ST-205 ST-84

  16. Building a pharmacophore model

  17. Building a pharmacophore model

  18. 3D QSAR Analysis • With a pharmacophore model • Arrange potent moleculesor fragments in theirbioactiveconformation • Guideline for designingnext-gen. enhancedcompounds

  19. 3D QSAR Analysis • 40 D3 antagonists • Fitted to the pharmacophoricconformation (model) • Superimposedontoeachother (FlexS) • Refinedwith SYBYL (steepest decent)

  20. 3D QSAR Analysis • CalculateGRID interactionfields for all 40 ligands • Nowwithalot of probes • 14580 probe-ligandinteractions per compound! • 14580: Toomany variables! • Will introducenoise

  21. 3D QSAR Analysis • To overcome the problem • Filter out variables withonlyfewvalues • Filter out variables withlowchange (<10-7kcal/mol) • If they all lie in a small interval theycanbedisregarded

  22. 3D QSAR Analysis • Next: Set up a PLS model (PartialLeast Square) • It can handle a statistical model with more energyvaluesthancompounds • The energyvaluesarecorrelatedwitheachother • Many of themare not important for the biologicalactivity • Wecanuse a fewdifferentalgorithms in the problem GOLPE to reduce the number of variables • D-optimal (good >1000 variables) • FractionalFactorial Design (FFD)

  23. 3D QSAR Analysis • Each time: • Cross validatewithLeave One Out (LOO) • Make a model with all the compoundsexceptone • Predictitsactivity • Do it with all compounds

  24. 3D QSAR Analysis • A FractionalFactorial Design (FFD) methoddetermines the predictivity of each variable • Each variable is classified as either • Helpful for predictivity • Destructive for predictivity • Uncertain • Onlyhelpful variables areincluded in the PLS model • Good to useafter D-optimal has reduced the variables to a few thousand

  25. 3D QSAR Analysis Highcrossvalidationvalue

  26. 3D QSAR Analysis • LOO crossvalidation in final model

  27. 3D QSAR Analysis • Validation of the 3D QSAR method • Many variables weretreated • Chance correlation? • Test withscrample set • Randomlyassign the binding affinities of the ligands • Generate PLS model and reduce variables as before • Cross validatewith LOO

  28. 3D QSAR Analysis • Prediction of Externalligands • Trywithsomedifferent type of structuresthatalso shows reasonable binding activitytowards the receptor Lies within±0.5 SDEP = 0.57

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