用于药物关系学习的上下文感知分层融合 Context-Aware Hierarchical Fusion for Drug Relational Learning

Context-AwareHierarchicalFusion

forDrugRelationalLearning

YijingxiuLu,YinhuaPiao,SangseonLee,SunKimSeoulNationalUniversity

Outline

•Background

•Motivation

•Method

•Experiments

•Summary

Background

DrugRelationalLearning

oCo-administrationofdrugsisacommonpracticeintreatingdiseases.

oChemicalandphysicalreactionsbetweendrugscanaltertheintendedfunctionalityofdrugs.

oComplexbiochemicalmechanismswithinthehumanbodycouldfurtherleadtoadversedrugreactions.

oDiscoveringallpossibledrugcombinationsusing

traditionallaboratory-basedmethodsischallenging.

Synergeticeffect

ondestroyinga

specifictypeof

lungcancercells

Unwanted

chemicalUnexpected

interactionspolypharmacy

sideeffects

Background

DrugRelationalLearning

1.Drug-druginteractionsarecontext-dependent

oE.g.TheconcomitantintakeofTylenolandalcoholcanleadtoliver

damageduetocompetitionforthesamemetabolicenzyme.Tylenol(acetaminophen)Alcohol(Ethanol)

CYP2E1

compete

CYP2E1

NAPQI(toxic)

!

Glutathione

cysteineandmercapturic

acidconjugates

(nontoxic)

Acetaldehyde

insufficient

Unexpectedpolypharmacy

sideeffects

Background

DrugRelationalLearning

2.Drugrelationshipscanchangewithcontext

oE.g.Cabazitaxalandzoledronicacidexhibitsynergyinlungcancercelllinesbutactantagonisticallyinbreastcancertreatment.

Complexmechanismsaffectedbycontextchanges:

oTumormicroenvironments.

oAntagonisminbreastcancer.

oDrugtransportandmetabolism.

Synergeticeffect

ondestroyinga

specifictypeof

9lungcancercells

BackgroundCurrentWorks

Currentworksondrugrelationallearningcanbecategorizedintonetwork-basedandstructure-based.

oNetwork-basedmethods:

oIntegratemulti-omicsdatatoconstructheterogeneousnetworksforinferringDDI.

oStructure-basedmethods:

oDirectlylearnchemicalpropertiesandbiologicalactivitiesfrommolecularstructure.

Network-basedMethods

》

Structure-basedMethods

BackgroundCurrentWorks

Currentworksondrugrelationallearningcanbecategorizedintonetwork-basedandstructure-based.

oNetwork-basedmethods:

oIntegratemulti-omicsdatatoconstructheterogeneousnetworksforinferringDDI.

oStructure-basedmethods:

oDirectlylearnchemicalpropertiesandbiologicalactivitiesfrommolecularstructure.

Howtocombinetheadvantagesofbothandbuildamodelsuitablefornewdrugs?

Network-basedMethodsStructure-basedMethods

Method

HierarchicalInformationFusion

Context-awaredrug-drugrelationallearning:

oInformationfusionbetweendrugs.

oInformationfusionbetweendrug-context.

oDrugfeatureencoderlearnscontext-awarerelationknowledge.

oInferunknownrelationship.

drugi

contextc

drugj

Rdi→c

HiⅡRdj→di

⃞--------->

Lsup

HjⅡRdi→dj

Rdj→c

Hi

Hc

Hj

Method

ProblemDefinition

Context

oConsiderasetofannotateddrug-drug-contexttriplet

drugi

tuples(di,dj,c,y),wheredi,dj∈D,c∈C,andyisthetargetvariablebelongingtoY.

drugj

oD={d1,d2,...,dn}representacollectionofndrugs,andC={c1,c2,...,cm}denoteasetofmcontexts.

drugk

oHere,yisascalarvalue,rangingfromnegativetopositiveinfinityinregressiontasks,andtakingbinaryvalues(0or1)inclassificationtasks.

relation(e.g.whethertwodrugsi,jexhibitsynergyinaspecificcelllinec)

drugi

drugj

contextc

Method

Context-AwareHierarchicalFusion

1.DrugEncoderandContextEncoder

Weemploy:

oGraphIsomorphismNetwork(GIN)asgraphencoder.

ℎ=MLP(ℎ−1+ℎ−1)

u∈N(v)

oMulti-LayerPerceptron(MLP)ascontextencoder.

ℎc=MLP(xc)

contextc

Hc

Method

2.Drug-DrugCrossFusion

Context-AwareHierarchicalFusion

oweemployanatom-wiseinteractionmaptocalculatethe

Hi,Hj

directionalrelationshipRdi→djbetweenapairofdrugsiandj.

Iij=sim

Rdi→dj=I·Hj

Hi∥Rdi→dj

oweupdatetherepresentationofdrugias:

H=concat

3.Drug-ContextCrossFusion

oSimilarly,wecomputetherelationshipsbetweendrugsandcontext:

Iic=simH,HcRdi→c=I·H

Rdi→c

Method

Context-AwareHierarchicalFusion

4.TripletRelationPredictor

oWefeedthefinalhiddenrepresentationofthedrug-drug-contexttripletintoMLPforrelationprediction:

hdi,dj,c=concat(HcⅡRdi→cⅡRdj→c)di,dj,c=MLP(hdi,dj,c)

drugi

c

context

drugj

Rdi→c

HiⅡRdj→di

__--------->

Rdj→c

HjⅡRdi→dj

Hi

Hc

Hj

Lsup

Outline

•Background

•Motivation

•Method

•Experiments

•Summary

Results

BenchmarkDatasets

weconsiderthethreemostpopulartasksindiseasetreatment:

oDrug-DrugSynergytask:

opredictswhetherapairofdrugsdi,djexhibitsynergyinaspecificcelllinec.

oDrug-DrugPolypharmacySideEffecttask:

opredictswhetherapairofdrugsdi,djleadstoaspecificadverseeventc.

oDrug-DrugInteractiontask:

opredictswhetherapairofdrugsdi,djleadstoaparticularreactionc.

Results

Performance

oOurmodelsconsistentlyoutperformthebaselinesacrossalltasks,underscoringtheeffectivenessofourarchitectureinlearningcomplexdrugrelationsacrossdiversetasks.

Results

AblationStudy

Oneofthemostnoteworthydistinctionsbetweenourmodelandotherbaselinesisthatourmodelexplicitlylearnsdrugrelationshierarchicallythroughthedrug-drug-contexttriplet.

Thereisasignificantdropwhenrelationsarenotexplicitlymodeled.

Withouthierarchy,themodel’sperformancedropsbyaround3.3%inAUROC.

suggestingthatthehierarchicalarchitectureeffectivelyfiltersoutfeaturesthatareirrelevanttomodelprediction.

Removingeithersideofthefusionresultsinadropinperformance.

Results

Performanceundercold-drugsetting

Toassessthegeneralizationabilityofourmodelinpredictingrelationshipsbetweenunknowndrugpairs,weadoptedacold-drugsettingbypartitioningasmallsubsetofdrugsfromtheoriginaldataset.

oOurmodeloutperformedothermodelsbyasignificantmarginonDrugBankDDI,andachievecomparableperformancetothebestbaselineonDrugComb.

oInsuchacontext-richenvironment,theabilityofmodelstolearncontextualinformationismorecriticalforperformance.

Summary

MainchallengesinDrugRelationalL