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