interactive visualization in decision meetings · groups of selected compounds by color coding . ps...

PS April 2002 carlssonresearch

interactive visualization in interactive visualization in decision meetingsdecision meetings

a way to improve the a way to improve the compound selection processcompound selection process

Peder Svensson

carlssonresearch


the ”standard” drug discovery processthe ”standard” drug discovery processthe ”standard” drug discovery process

Genetics/GenomicsBioinformaticsMolecular Biology

ToxicologyBioanalysis

Medicinal Chemistry/Computational ChemistryBiochemistry & Cell Biology

PharmacologyPharmacokinetics

Lead Discovery

LeadOptimization

CD to INDTarget and ConceptDiscovery

Candidate DrugInvestigational New DrugCandidate DrugInvestigational New Drug

bioinformaticsstructural chemistry/biology

Structure Based Drug Designcombinatorial library design

ADME modellingLigand Based Drug Design

A Continuum of Selections & Decisions

A Continuum of Selections & Decisions

documentation & patenting


philosophyphilosophyphilosophydrug design is a multivariate problem

a drug is often not optimal in all relevant aspects but the best compromise of properties

one should always try to make as informative experiments/compounds as possible

dogmas should be challenged once in a while

interdisciplinary discussions are very important for success


En Variabel I Taget - EVITE(one variable at the time – ETERNITY)

En n VVariabel ariabel II TTaget aget -- EVITEVIT(one variable at the time (one variable at the time –– ETERNITY)ETERNITY)

?


statistical designstatistical designstatistical design


multivariate analysis multivariate analysis

chemical properties biological response

Des

crip

tor

2

Descriptor 1

Descri

ptor 3

PC2

PCA

Res

pons

e 2

Response 1

Response

3

PC2

compoundcompound or building blockPC1 PC1

PLS


molecular descriptorsmolecular descriptorsmolecular descriptors

lipophilicity (LogP)size and shapeatom & fragment countsmolecular surface based descriptorselectronic properties, atomic charges, dipole moments , ionization potentialstopological indicesgeometries – distances and anglesrotational barriers around bondsvibrational frequencies, NMR chemical shifts, etc.

.... in principle, any physical observable!


experimental design & QSARexperimental design & QSARexperimental design & QSAR

Principal Component Analysis, PCA

selection of a representative subsetusing filtering & experimental design methods

synthesis/testing

data table

QSAR


visualization and interactivity for compound selection & experimental designvisualization and interactivity visualization and interactivity for compound selection & experimental designfor compound selection & experimental design

low barriers for testing and examining different alternativesany type of information can be included in the process:

chemical structureschemical and biological propertiesresults from simulations – e.g. in silico dockingcosts, logistics, etc.

all aspects can be considered at once:synthetic feasibilitytoxicitycoverage & balancenovelty & patent situation

well suited for group sessions using a screen projector

improves chance to reach the best compromise


prepare the optimal dataset for visualization

prepare the optimal dataset for prepare the optimal dataset for visualizationvisualization

as rich as possible – include all available information that could become useful as basis for decisioncombine different types of data

model results – principal components, model diagnostics, scoring results, ...raw data – descriptors, experimental propertiesclassification and labeling information – chemical types, source, ...additional filtering information - cost, availability, ...

make links to other types of informationchemical structuresgraphs from experimentsexternal information on the webgraphical docking results


historyhistoryhistory


the starting point ~96property based selection using dummy 3D molecule in ISIS and lists on

paper with structures for design of small combinatorial libraries

the starting point ~96the starting point ~96property based selection using dummy 3D molecule in ISIS and lisproperty based selection using dummy 3D molecule in ISIS and lists on ts on

paper with structures for design of small combinatorial librariepaper with structures for design of small combinatorial librariess

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rotation – additional distance and cluster information

keeping track of selections

enables efficient modifications to the selected set

distinguish between groups of selected compounds by color coding


the c3-project at the cthe c33--project at project at objective:

improve the quality of combinatorial library designs andcompound selections:

overall workflowtools for filtering, selection, enumeration, visualization and knowledge managementusability (user friendliness)


SaSA tool for combinatorial SaSA tool for combinatorial library generationlibrary generation

SStructuretructure aandnd SSynthesisynthesis AAdministratordministrator

generates new structures to be synthesized or analyzed virtually from reaction descriptions and lists of starting materials

registers structures and synthesis documentation

analyzes possible starting materials and virtual libraries with respect to a number of different descriptors, e.g. molecular weight, lipophilicity, hydrogen bonding….

selects a subset of compounds for synthesis, based on diversity (or similarity)

filters out compounds with undesired properties (MW, lipophilicity, etc. as above)

exports the results for further processing using Excel or Spotfire...

very useful but not interactive ...very useful but not interactive ...


c3-vision cc33--vision vision reagents productsvisualization

of structures and property space

chemical filtersto accommodate restrictions

imposed by reaction conditions

property and/or structure based filters

to introduce the desired bias

chemGPSto generate the property space and enable a straight forward comparison with previously

positioned reagents

reagent selectionproperty based selection using D-optimal and/or space-filling

design

enumeration

chemical filters

p&s based filters

scoring functionsto score lead and/or drug

likeness or oral bioavailability

save&reuse

chemGPS

designed libraryby selecting final products


the present set of toolsthe present set of toolsthe present set of tools


the visualization environmentthe visualization environmentthe visualization environment

SpotfireSpotfire

Spotfire Structure Visualizer

connected to ISIS

molecular databases

Spotfire Structure Visualizer

connected to ISIS

molecular databases

Structure spaceStructure space

property spaceproperty space

3 dim3 dim

12 dim12 dim


multidimensional distance filtersmultidimensional distance filters

filtering in 8 PC dimensionsfiltering in 8 PC dimensions

3D neighbourhood zooming3D neighbourhood zooming

Labels only on selected molecules Labels only on selected molecules


grouping by substructure searches grouping by substructure searches

substructure filteringsubstructure filtering


property based selection property based selection

original library

selected subsetexclusion sphere


ISSS an Interactive SubSet Selection toolISSSISSS an an IInteractive nteractive SSububSSetet SSelection toolelection tool

very fast space filling selectionon large datasets over many dimensions in just a few seconds

supports a flexible and constructive compound/experiment selection workflow

works on any property space modeleasy to include preselected compoundseasy to filter out compounds based on any property criteria

designed for interactive use e.g. in group sessionsfast and easy to reiterate with new preselections and filtersgenerates query devices for selection status and inclusion orderautomatic color coding of selection status

density biased selection ability to select a truly representative subsetability to avoid regions with only outliers


ISSS-Interactive SubSet SelectionISSSISSS--IInteractive nteractive SSububSSet et SSelectionelection

.jx

hS

1−hS

Complexity: ( )nNΟ

A fast MaxMin algorithm

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d ist.)

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N r o f m o le cu le s in su b se t

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W ith o u t a d d in gsu b se t in c. N rco lu m n

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visualization of the selectionvisualization of the selectionvisualization of the selection

inspection of selectioninspection of selection


density biased space filling selectiondensity biased space filling selectiondensity biased space filling selection

space fillingselection

space fillingselection

density biasedselection

density biasedselection

all compoundsall compounds

exampleselection of 100

compounds out of 2900 reference compounds with CNS activity from MDDR based on a 3 dimensional property

space

exampleexampleselection of 100

compounds out of 2900 reference compounds with CNS activity from MDDR based on a 3 dimensional property

spacedensity bins density bins


database docking – in silicoscreening

database docking database docking –– in silicoin silicoscreeningscreening

3600 commercially available small basic compounds docked into the “basic pocket” of an protein receptor of interest

two different docking algorithms were used:

Slide (Volker Schnecke, AZ-M & Leslie Kuhn, MSU)Fast!Protein flexibilityLimited ligand flexibility

FlexX (Thomas Lengauer & Matthias Rarey, GMD)Moderately fastRigid proteinFull ligand flexibility


filtering based on database filtering based on database docking resultsdocking results

protein structure based filtersprotein structure based filters

inspection of docking results inspection of docking results


enumeration exampleenumeration example20 sulphonyl chlorides x 20 benzylic amines20 sulphonyl chlorides x 20 benzylic amines

filtering and cherry picking (1 plate filtering and cherry picking (1 plate -- 80 cmpds)80 cmpds)

R1S

Cl

OONH2

R2 NH

SR1

OO

R2

+

20 x 20 400


selection on productsselection on productsselection on productsR-group presentation with activity colour codingR-group presentation with activity colour coding

R-group colour codingR-group colour coding

property space with selection status colour coded

property space with selection status colour coded


QSAR Example Observed vs. PredictedQSAR Example Observed vs. Predicted

filteringfiltering

inspection of outliersinspection of outlierscolor codingcolor coding


QSAR example QSAR example -- PLSPLS--loadingsloadings

responses & origo in redresponses & origo in red

filtering by variable importancefiltering by variable importance


conclusionsconclusionsconclusionsmaking design decisions in meetings using interactive visualization leads to:

increased probability of finding “the optimal compromise”

a much faster drug design process

the combination of property and protein structure based combinatorial library design adds value


thanks!thanks!Andreas Stansvik, presently at SpotfireThomas Olsson, AZ MölndalFredrik Rosell, SpotfireVolker Schnecke, AZ MölndalVladimir Sherbukhin, AZ -> Serono

Discovery GenevaJohan Gottfries, AZ MölndalTudor Oprea, AZ MölndalBo Nordén, AZ LundMarie Berghult, AZ MölndalThomas Kühler, Swedish MPA

Andreas Stansvik, presently at SpotfireThomas Olsson, AZ MölndalFredrik Rosell, SpotfireVolker Schnecke, AZ MölndalVladimir Sherbukhin, AZ -> Serono

Discovery GenevaJohan Gottfries, AZ MölndalTudor Oprea, AZ MölndalBo Nordén, AZ LundMarie Berghult, AZ MölndalThomas Kühler, Swedish MPA

interactive visualization in decision meetings · groups of selected compounds by color coding . ps...

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