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1、ThisisanopenaccessarticlepublishedunderanACSAuthorChoiceLicense,whichpermitscopyingandredistributionofthearticleoranyadaptationsfornon-commercialpurposes.ACSEftxsJournaldfCHEMICALINFORMATIONandMODELINGCiteThis:J.Chern.Inf.Model2017,57,2911-2937RelativeBindingFreeEnergyCalculationsinDrugDiscovery：Adv
2、ancesandPracticalConsiderationsPerspectivepubs.acs.org/jcimRecentZoeCournia,*,+BryceAllen/andWoodySherman*tBiomedicalResearchFoundation,AcademyofAthens,4SoranouEphessiou,11527Athens,GreecetSiliconTherapeutics,30()AStreet,Boston,Massachusetts()221(),UnitedStatesfreeenergy (RBFE) calculations, which r
3、ely on physics-based niolecjpromise in reliably generating accurate predictions in the context oaccumulating developments in the underlying scientific mathods ialgorithms) coupled with vast increases in computational resdyi图Mounting evidence from retrospective validation studies, 碰新suggests that RBF
4、E simulations can now predict the affinity diMM)mrulatiolfcand statistical mecABSTRACT:Accurateinsilicopredictionofprotein-ligandbindingaffinisha$heen;歌“螃Uobjectiveofstructurcbaseddrugdesignfordecadesduetotheputativevalueitwouldbringtothedr6sSiscovcryprocess.However,computationalmethodshavehistorica
5、llyfailedtodelivervalueinial-wdtlddr耐discoveixapplicationsduetoavarietyofscientific,technical,andpracticalchallenges.Recently,afapiilyo碗叫tU)|srelativebindinghies,hvTshownugiscoveryprojeciades of research onisadvancearisesifromrcefieldsandjthroughputtodeliverconsiderablevalueinhit-to-leadandleadoptim
6、izationefforts.Here,wepresentanoverviewofcurrentRBFEimplementations,highlightingrecentadvancesandremainingchallenges,alongwithexamplesthatemphasizepracticalconsiderationsforobtainingreliableRBFEresults.Wefocusspecificallyonrelativebindingfreeenergiesbecausethecalculationsarelesscomputationallyintens
7、ivethanabsolutebindingfreeenergy(ABFE)calculationsandmapdirectlyontothehit-to-leadandleadoptimizationprocesses,wherethepredictionofrelativebindingenergiesbetweenareferencemoleculeandnewideas(virtualmolecules)canbeusedtoprioritizemoleculesforsynthesis.WedescribethecriticalaspectsofrunningRBFEcalculat
8、ions,fromboththeoreticalandappliedperspectives,usingacombinationofretrospectiveliteratureexamplesandprospectivestudiesfromdrugdiscoveryprojects.Thisworkisintendedtoprovideacontemporaryoverviewofthescientific,technical,andpracticalissuesassociatedwithrunningrelativebindingfreeenergysimulations,withaf
9、ocusonreal-worlddrugdiscoveryapplications.WeofferguidelinesforimprovingtheaccuracyofRBFEsimulations,especiallyforchallengingcases,andemphasizeunresolvedissuesthatcouldbeimprovedbyfurtherresearchinthefield.Journal of Chemical Information and Modelingsu-aEBpuqs = qnd UJEqs Aollnu三3。一 oi MOL- uo suopdo
10、 sQ.sQp-5MMU-zBqs/el)JOsosqnd、/一 sdlzQQS05 8寸V-ZI急 zzoz zroJE 工 SON-G-soz BP3PBOCMOCI氐ofofin回INTRODUCTIONGeneralOverview.Optimizationofbindingaffinity,selectivity,andotheroff-targetinteractionsisacriticalpartofhit-to-leadandleadoptimizationeffortsindrugdiscovery.Relativebindingfreeenergy(RBFE)calcul
11、ationsofferanattractiveapproachtopredictprotein-ligandbindingaffinitiesinsilicousingmolecularsimulationsandstatisticalmechanicsasawaytocomputefreeenergydifferencesbetweencongenericmolecules.Fromacomputationalperspective,RBFEsimulationsareofparticularinterestduetotheirrigorousstatisticalmechanicalfra
12、mework,accuratemodelingofbiologicalsystems(eg,proteinflexibility,explicitsolvent,cofactors,ions,concertedmotions,andentropy,tonameafew),anddirecttranslationtoreal-worldproblems(e.g,，hit-to-leadandleadoptimization).However,historicalchallengeshavelimitedthesuccessoffreeenergysimulationsindrugdiscover
13、y?hamely,thehighcomputationalcosts,limitedforcefieldaccuracy,andtechnicalchallengestosetup/run/analyzefreeenergysimulations.ThehighcomputationaldemandshavebeenovercomeACSPublications5r2017AmericanChemicalSocietytoalargeextentbytherecentadvancesingraphicsprocessingunits(GPUs)1-5andmassivecomputeresou
14、rcesavailableonthecloud.Inaddition,decadesofworkfromacademicandindustryresearchlaboratoriesaroundtheworldhaveproducedforcefieldsandsamplingalgorithmsthatarecapableofpredictingrelativebindingfreeenergiesatalevelofaccuracynecessarytobeusefulindrugdiscovery.6-9Finally,automationtoolshaveaddressedmanyof
15、thetime-consuminganderroipronestepsassociatedwithrunningRBFEsimulations.Thesefundamentaladvances,coupledwithworkflowautomation,l0J1haveenabledfreeenergysimulationstobeperformedinarigorous,high-throughputmodethatcanbereadilydeployedwithinstructurallyenableddrugdiscoveryprojects.12Furthermore,withthebroadavailabilityofopen-source(eg,OpenMM13andGromacs143),academic(e.g.,AMBER,16CHARMM,1andNAMDIS),andcommercialcodes(AceMD2Received:September19,2017Published:December15,2017andDesmond19),itispossibletomaximallyleveragethelargenumberofgraphicalprocessingunits(GPUs)t
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