Chapter 2 Molecular modeling
Case Study:Dopamine D3 Receptor AnthagonistsChapter 3 Molecular Modeling1Todays lecture2Dopamine D3 Receptor AnthagonistsBuilding a pharmacophore model3D QSAR analysisDopamine Receptor35 different subtypes: D1, D2, D3, D4, D5 Defects is related to several diseasesParkinsons disease, schizophrenia etc. Medical treatmentLimited by side effects from drugs binding to various subreceptorsNeed selectivity! 3Building a pharmacophore model45 ligands (D3 receptor antagonists)High affinityKnown steric and electrostatic information
Structure:
Highly potent
4Building a pharmacophore model5StrategyDecompose molecule into fragmentsMolecular allignment using FlexSOne treated flexibleOne treated rigid
Building a pharmacophore model6Rigid partSYBYL: Simulated annealingLow T conformationTwo clusters (conformation family)
rigid
Building a pharmacophore model7Flexible part:Fit onto rigid partFlexS
flexible
Building a pharmacophore model8The spacerGenerally flexible
Examined in detail:
quite rigid
overlapBuilding a pharmacophore model9Simulated annealing on bicyclic ring system3 conformations
Building a pharmacophore model10Aromatic/Amidic residueAssumed planarInclude this restriction in previous examination
planar
Building a pharmacophore model11Systematic search10 degree incrementTripos force field 992 conformations
Building a pharmacophore model12Compound 1 fitted on all 992 conformations with FlexSHighest rated = binding conformation of these fragments
Compound 1
Building a pharmacophore model13Now we have the conformation of all fragmentsRecombine fragmentsPharmacophore model!
Building a pharmacophore model14Molecular interaction fields with GRID
C=ON-H
ST-127ST-84ST-205ST-86H-bond acceptorBasic nitrogenBuilding a pharmacophore model15
ST-127ST-84ST-205ST-86Building a pharmacophore model16
Building a pharmacophore model17
3D QSAR Analysis18With a pharmacophore modelArrange potent molecules or fragments in their bioactive conformationGuideline for designing next-gen. enhanced compounds
3D QSAR Analysis1940 D3 antagonistsFitted to the pharmacophoric conformation (model)Superimposed onto each other (FlexS)Refined with SYBYL (steepest decent)
3D QSAR Analysis20Calculate GRID interaction fields for all 40 ligandsNow with alot of probes14580 probe-ligand interactions per compound!14580: Too many variables! Will introduce noise3D QSAR Analysis21To overcome the problemFilter out variables with only few valuesFilter out variables with low change (1000 variables)Fractional Factorial Design (FFD)3D QSAR Analysis23Each time:Cross validate with Leave One Out (LOO)Make a model with all the compounds except onePredict its activityDo it with all compounds3D QSAR Analysis24A Fractional Factorial Design (FFD) method determines the predictivity of each variableEach variable is classified as eitherHelpful for predictivityDestructive for predictivityUncertainOnly helpful variables are included in the PLS modelGood to use after D-optimal has reduced the variables to a few thousand3D QSAR Analysis25
High cross validation value
3D QSAR Analysis26LOO cross validation in final model
3D QSAR Analysis27Validation of the 3D QSAR methodMany variables were treatedChance correlation? Test with scrample setRandomly assign the binding affinities of the ligandsGenerate PLS model and reduce variables as beforeCross validate with LOO
3D QSAR Analysis28Prediction of External ligandsTry with some different type of structures that also shows reasonable binding activity towards the receptor
Lies within 0.5SDEP = 0.57
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