摩根大通-中国医疗：AI驱动药物发现及其对行业的潜在影响-China Healthcare AI-driven drug discovery and its potential impact on the sector-20260306

AsiaPacificE

hqtutiys:R/e/swewr.ch

06March2026

ThismaterialisneitherintendedtobedistributedtoMainlandChinainvestorsnortoprovidesecuritiesinvestmentconsultancyserviceswithinthe

territoryofMainlandChina.Thismaterialoranyportionhereofmaynotbereprinted,soldorredistributedwithoutthewrittenconsentofJ.P.Morgan.

ChinaHealthcare

AI-drivendrugdiscoveryanditspotentialimpactonthesector

AI-drivendrugdiscovery(AIDD)isenteringapivotalphase,movingfromtheoreticalpromisetoclinicalproofandinstitutionalscale:Insilico’srentosertibdeliveredthefirstpublishedPh2aefficacyforafullyAI‑designeddrug,andanAI‑engineeredanti‑TSLPantibodyGB-0895byGenerate:BiomedicineswillinitiatetwoglobalPh3trials,alongsidebig‑ticketAIDDpartnershiparoundtheglobeandCEOguidancefromlargepharmathatframesAIDDaslong‑horizonR&Dinfrastructure.ThoughmanyAI-drivenbiotechstockshavebeenunderpressurerecently(Figure7),wethinkAIDDrepresentsakeydirectionforfuturedrugR&Dwithpotentialclinicalcatalystsin2026.However,keyquestionsonAIDDremainaswewilllikelyseePh3dataofAI-discovereddrugsin2028+.Overall,webelievetheAIDDtrendispositiveforChinahealthcare.Amongbiotechandpharmas,weseeHengrui,HansohandInnoventaspotentialearlybeneficiariesasAIDDscalesthroughtheirinternalAIDDteamsandpartnerships.AmongCXOs,wepreferCDMOsoverCROsandhighlightWuXiAppTec,WuXiBiologicsandWuXiXDCaspotentialbeneficiaries.

•AIDDbreakthroughsarereshapingdrugdiscoveryanddevelopmentbyenhancingprecision,efficiency,andinnovationacrossalmostallstagesoftheR&Dprocess.Forexample,GraphNeuralNetworkshavebecometheindustrystandardformolecularpropertyprediction,whileAlphaFold3extendsaccuratestructurepredictiontoprotein–DNA/RNA/ligandcomplexes,andLLMs,includingspeciallytrainedmodelsandgeneral-purposemodels,enhancepredictivepoweracrossdrugR&Dworkflows.SomerecentlyemergingtoolssuchasisoDDEandBioNeMocouldfurtherimproveR&D.Thesetoolsnowinformtargetidentification,insilicodesign,ADMETprediction,andclinicaloperations,compressingcycletimesandelevatingdecisionqualityacrossstages.

•AIDDhasrecentlyreachedanewlevelofcredibilityandscale,catalyzedbyrecentclinicalvalidationandunprecedentedinstitutionalinvestment.Pharmaintegrationhasacceleratedviamulti‑billion‑dollaralliances(e.g.,IsomorphicLabswithLillyandNovartis),withCEOsguidingforfutureproductivitygainsratherthanimmediatestep‑changesinsuccessrates,underscoringalong‑horizon,infrastructure‑likeroleforAIDD.Frost&SullivanprojectstheAI‑empoweredpharmaR&DmarkettogrowfromUS$11.9bnin2023toUS$74.6bnby2032(22.6%CAGR),with>111AIDDcompaniesinChinaandleadersInsilico(pipeline‑first)andXtalPi(platform‑first)pursuingdistinctstrategiesasout‑licensingreached13AI‑relateddealsin2025,includingtop‑20MNCs.

•WebelieveAIDDprovidesopportunitiesforChinahealthcarewithriskstocertainsubsectors.WehaveanalyzedthepotentialAIDDimpactonChinabiotech,pharmaandCXOs.WepreferHengrui,Hansoh,andInnoventfortheirongoingAIintegration,robustpipelines,andcompoundingdataadvantages.

Healthcare

YangHuangAC

(852)28003812

yang.huang@

J.P.MorganSecurities(AsiaPacific)Limited/J.P.MorganBroking(HongKong)Limited

DeniseYan,CFA

(852)28008195

denise.yan@

J.P.MorganSecurities(AsiaPacific)Limited/J.P.MorganBroking(HongKong)Limited

DerekChoi

(852)2800-8744

derek.c.choi@

J.P.MorganSecurities(AsiaPacific)Limited/J.P.MorganBroking(HongKong)Limited

EricZhao,CFA

(86-21)61066256

eric.zhao@

SACRegistrationNumber:S1730524050001

J.P.MorganSecurities(China)CompanyLimited

Seepage19foranalystcertificationandimportantdisclosures,includingnon-USanalystdisclosures.

J.P.Morgandoesandseekstodobusinesswithcompaniescoveredinitsresearchreports.Asaresult,investorsshouldbeawarethatthefirmmayhaveaconflictofinterestthatcouldaffecttheobjectivityofthisreport.Investorsshouldconsiderthisreportasonlyasinglefactorinmakingtheirinvestmentdecision.

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06March2026

WekeepourneutralstanceontraditionalCROs,whichfacelong-termriskunlesstheyinvestinproprietaryAIplatformsandhigh-complexityservices.CDMOsseembetterpositioned,withWuXiAppTec,WuXiBio,andWuXiXDClikelytobenefitfromincreasedvolumeandcomplexityofassetsenteringclinicaltrials.PushbackcentersonwhetherAIDDcandeliversuperiordrugsandimprovetheprobabilityofsuccess.Weanticipatethemarketcouldre-rateAI-drivenbiotechcompaniesasmoreclinicaldataandreal-worldevidenceemerges.

3

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yang.huang@

AsiaPacificEquityResearch

06March2026

AIistransformingdrugdiscoveryanddevelopment,reducingcostanddevelopmenttime.Butkeyquestionsremain

AIisrevolutionizingdrugdiscoveryanddevelopmentbyenhancingprecision,

efficiency,andinnovationacrossallstagesoftheprocess,fromtargetidentificationtopost-marketmonitoring(Figure1).Traditionally,drugdevelopmentiscostly,lengthy,andhasalowsuccessrate,butAI-drivenmethods—suchasmachinelearningand

predictiveanalytics—streamlinetargetidentification,leadoptimization,andclinical

trialdesign.AIenablesrapidanalysisoflargedatasets,predictspharmacological

properties,andimprovespatientselection,makingdrugdevelopmentfaster,more

accurate,andcost-effective.However,wethinktwokeyquestionsremain:WhetheranAI-discovereddrugcandemonstratebetterclinicaldataoverhuman-discovereddrugs,andwhetherAIcansignificantlyimprovethesuccessratefordrugdiscoveryand

development.GivenwewilllikelyonlyseePh3dataofAI-discoveredordesigneddrugsin2028+,webelievetheabovequestionswillcontinuetobedebated.

TargetDiscovery:

•AIanalyzeslargevolumesofomicsanddisease-specificdatatoidentifynovelbiologicaltargets.

•AIusespatternrecognitionandbiologicalnetworkmappingtofindtargetsthattraditionalmethodsmaymiss.

LeadCompoundScreening:

•AIpredictswhichchemicalcompoundsaremostlikelytobindeffectivelytoselectedtargets.

•AIfiltersoutcompoundswithpooractivityorunfavorablesafetyprofiles,allowingresearcherstoprioritizepromisingcandidates.

PreclinicalDevelopment:

•AIforecastscriticalpharmacokineticandtoxicologicalproperties(ADMET:

absorption,distribution,metabolism,excretion,toxicity).

•Earlypredictionshelpoptimizedrugcandidatesandreducetheriskoflate-stagefailures.

ClinicalTrials:

•AIimprovespatientselectionandenablesreal-timemonitoring.

•AIfacilitatesadaptivetrialdesignsthatadjustprotocolsbasedonemergingdata.

•AIcreatessyntheticcontrolgroupsfromhistoricaldata,reducingrelianceontraditionalplacebogroupsandimprovingtrialefficiencyandethics.

IntegrationwithExpertKnowledge:

•AI-drivengenerativealgorithmsdesignnewdrug-likemolecules.

•Smartlaboratoryplatformssynthesizeandtestlargenumbersofcompoundsinparallel.

•AImodelscontinuouslylearnfromexperimentaloutcomes,acceleratingdiscoveryandrefinement.

•AIguidesexperimentaldesignbypredictingoptimalconditions,reducinghuman

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06March2026

errorandunnecessarytrials.

Figure1:Drugdevelopmentisacomplexprocessencompassingvariouscriticalstages,nowbeingreshapedbyAI

Source:JournalofMedicinalChemistry.

Figure2:AIDDofferssignificantbenefitsovertraditionaldrugdiscovery

Source:Frost&Sullivan.

EvolutionofAImodelsindrugdesignanddiscovery

Giventhevarietyoftasksinvolvedinthedrugdesignanddevelopmentprocess,there

arealargenumberofAImodelsavailableservingdifferentfunctions.Thefieldhas

evolvedfromsimplefeature-basedmachinelearningtocomplexGraphNeural

Networksand,mostrecently,GenerativeAIsuchasLargeLanguageModels.Giventhescopeofthisreport,wewillonlydiscussafewAIapplicationsindrugdiscovery.

InterestedreaderscouldrefertoreviewarticlessuchasAI-DrivenDrugDiscovery:AComprehensiveReviewandLargelanguagemodelsfordrugdiscoveryand

development.

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YangHuangAC

(852)28003812

yang.huang@

AsiaPacificEquityResearch

06March2026

GraphNeuralNetworks(GNNs),thecurrentstandardformolecularpropertyprediction

Moleculesarenaturallyrepresentedasgraphs,i.e.,atomsasnodes,bondsasedges,

whichmakesGNNsapowerfultoolinmolecularmodelingthankstotheirabilityto

directlylearnfromthegraph-basedstructureofmolecules.ThefirstwaveofGNN

models,suchasSchNet,launchedin2017,introducedcontinuous-filterconvolutions

thatallowedneuralnetworkstomodelquantuminteractionsbyaccountingforthe

spatialrelationshipsbetweenatoms,ratherthanjusttheirconnectivity.Thismarkedthetransitionfromsimple2Dgraphanalysisto3Dspatialawareness,enablingthe

predictionofmolecularenergyandforcefieldswithmuchhigheraccuracy.

Asthefieldmatured,thefocusshiftedtowardimprovinghowinformationflowed

betweenatomsinthesegraphs.In2019,theChemprop(D-MPNN)architecturepassedmessagesalongthebondsthemselves,insteadofpassingmessagesjustbetweenatoms.Thisstepsignificantlyreducedthelossofinformationduringthelearningprocess.

Halicin,anantibioticthatwasdiscoveredbyChemprop,provedthatGNNscould

identifypotentdrugsthatlookednothinglikeexistingmedicines.In2020,DimeNetfurtherpushedtheboundariesofphysicalaccuracybyincorporating“directional

messagepassing”.Itconsiderednotjustthedistancebetweenatoms,buttheanglesformedbychemicalbonds,allowingthemodeltocapturethesubtlegeometric

constraintsthatdictatehowadrugbindstoaprotein.

ThemostrecentdevelopmentofGNNhasfocusedongraphtransformertocapture

long-rangedependencies.Wehaveseenanumberofgraphtransformerbasedmodels

suchasGraphormer,GraphGPS,Exphormer,GRIT,andGROVER.Thosemodelsandothersapplypositionalencodingtoembedlong-rangedependenciesinthetrainingdata.

ProteinStructurePredictionModels,the“biologysolvers”

Proteinstructurepredictionisacornerstoneofmodernstructure-baseddrugdesign,

whichallowsscientiststoidentifythespecificregiontotargetontheprotein.Earlier

modelsreliedonhomologymodeling,whichoperatesontheprinciplethatiftwo

proteinssharesimilaraminoacidsequences,theylikelysharesimilar3Dstructures;andphysics-basedmodeling,whichusesphysics-basedsimulationstofoldtheproteinfromscratchbyminimizingenergyfunctions.

Abreakthroughcamein2018withAlphaFold1,whichusesadeepConvolutional

NeuralNetwork(CNN)topredictdistograms,aprobabilitymapofdistancesbetweenaminoacidpairsfromthemultiplesequencealignment(MSA)features,with

unprecedentedaccuracy.Itthenusesagradientdescentalgorithmtophysicallyfoldtheproteinchaintosatisfythepredicteddistances.AlphaFold1wontheCASP(Critical

AssessmentofStructurePrediction)competitionin2018withamedianscoreof58.9,provingthatAIcouldoutperformtraditionalphysics-basedmodels.

In2020,AlphaFold2wasreleased,whicheffectivelyaddressesthesingle-chainproteinstructurepredictionproblemformostpracticalpurposes.AlphaFold2utilizesa

transformer-basedarchitectureandanattentionmechanism,allowingthemodelto

processtheproteinsequenceandtheMSAasagraph,dynamicallylearningwhichpartsofthesequencewererelevanttoeachotherregardlessofhowfaraparttheywereinthelinearchain.Inaddition,AlphaFold2tookasequenceasinputandoutput3D

coordinatesdirectly.ThesebreakthroughsenabledAlphaFold2toscore92.4atCASP14in2020,markingasignificantimprovementinperformancefromAlphaFold1.

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AsiaPacificEquityResearch

06March2026

FollowingAlphaFold2’srelease,therewasaproliferationofvariationsincludingRoseTTAFold,ESMFoldandMultimerPrediction.

AlphaFold3wasintroducedin2024,whichdemonstratesmuchhigheraccuracyover

previouslydevelopedspecializedtoolsandAlphaFold2.Betterpredictionisachievedbytheevolutionofpriorarchitectureandtrainingprocedure.Forexample,itreplacesthe

“structuremodule”ofAlphaFold2,whichisbasedonamino-acidspecificinformationandside-chaintorsionangles,withadiffusionmodelforthepredictionoftherawatomcoordinates.Inaddition,AlphaFold3isnotjustforproteins,butcanpredictthe

structuresofproteinsboundtoDNA,RNA,andligandswithhighaccuracy.

WearewaitingtoseeAlphaFold4,whichmightbereleasedthisyear.WeexpectAlphaFold4,iflaunched,couldoffersuperioraccuracyandinferencespeedvs.

AlphaFold3,dynamicconformationalmodeling,modelingformorecomplexmolecules,andpotentiallylowercomputationalrequirements.

Table1:ComparisonofAlphaFoldmodels

Source:ProteinStructurePredictionCenter,AlphaFold.

Applicationoflargelanguagemodelsisthenewfrontier

BeforemodernLLMs,RecurrentNeuralNetworkswereusedtogeneratevalidSMILES(SimplifiedMolecularInputLineEntrySystem)strings,whichreaddatastrictlyfrom

lefttoright.Whiletheylearnedthe“grammar”ofchemistry,theyoften“forgot”the

beginningofalongmoleculebythetimetheyreachedtheend.BERTtransformedthelandscapeasitreadstextbidirectionallyandusedatechniquecalledMaskedLanguageModeling.In2019,SMILES-BERTwasreleased.Itprovedthatamodelcouldlearn

chemicalruleswithoutbeingexplicitlyprogrammedwiththelawsofphysics.In2020,ChemBERTawasreleasedandhasestablisheditselfasoneofthemostadopted

chemicalfoundationmodelsinpharmaresearch,with300,000monthlydownloads

acrossmodelvariantsasofDecember2025,accordingtoQuantumrun.TherearetwolearningparadigmsofLLMsinthefieldofdrugdiscovery:Oneisspecializedlanguagemodels,whicharetrainedondomain-specificlanguageanddata,andtheotheris

general-purposelanguagemodels,suchaschatGPTandGeminiPro.Asforhowto

applyLLM,therecouldbethreetypesofmethods:(1)LM-basedmethods:These

leveragesmallerlanguagemodels(LMs),usuallyoflessthan100millionparameters;

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YangHuangAC

(852)28003812

yang.huang@

AsiaPacificEquityResearch

06March2026

(2)LLM-basedmethods:Theseleveragelarge(morethan100millionparameters)LMs;and(3)hybridLM/LLMmethods:Thesearchitecturesusebothlargelanguagemodels

anddedicatedcomputationalmodules.LLM-basedmethodsandhybridLM/LLM

methodshavebeenusedinmanystepsofdrugdiscovery,includingtargetidentification(proteintargetidentificationandgenomicanalysis),chemistry,insilicosimulation,leadoptimization,ADMET(absorption,distribution,metabolism,excretion,andtoxicity)

prediction,andclinicaltrialplanningandprediction.

TheevolutionofAIindrugdiscoveryreflectsacleartrajectoryfromspecialized

architecturestoincreasinglygeneralizableandpowerfulmodels.Together,these

advancesrepresentafundamentalshiftincomputationaldrugdiscovery—fromhand-engineeredfeaturesandphysicsconstraintstowarddata-drivenmodelsthatlearntheunderlyingpatternsofmolecularbehavioratscale,promisingtoacceleratethe

identificationofnoveltherapeuticswhilereducingdevelopmentcostsandtimelines.

RecentlyemergingAIDDtools

AsAIdevelopmentisataveryfastpace,weconcludeourtechnicaldiscussionwitha

fewrecentlyemergingAIDDtools.Pleaseseethefigurebelow.Lastbutnotleast,we

continuetoseeAIsupportingbasicresearchinlifesciencewithtoolssuchas

AlphaGenomeforgenomicanalysis,Phyloforintegratedbiologyresearchenvironment,GPT-5drivenautonomouslab,andmanymore.

Table2:AfewAIDDtoolsrecentlyemergingwithpotentialtofurtherboostdrugdiscoverycapabilities

Source:Companyreports.

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(852)28003812

yang.huang@

AsiaPacificEquityResearch

06March2026

ThevalidationofAIDDhascontinued,sparkinginstitutionalpartnershipsrecently

AIDDhasenteredanewphaseofcredibilityandscale,asrecentclinicalvalidationhascatalyzedunprecedentedinstitutionalinvestment.InJune2025,InsilicoMedicine’s

rentosertibbecamethefirstfullyAI-designeddrugtodemonstratePhaseIIaefficacyinhumans,confirmingthatAIcandelivernotjustspeedandinnovation,butreal

therapeuticimpact.Thisbreakthroughhasacceleratedtheshiftfrompilotprojectsto

multi-billion-dollarpartnerships,withmajorpharmaceuticalcompaniessuchasEli

Lilly,Novartis,andRochecommittingsignificantresourcestoAIplatformsand

infrastructure.Astheindustrymovesbeyondrapidmoleculegeneration,thefocusis

nowoncomprehensivetranslationalvalidation—integratingpatientdata,real-world

evidence,andadvancedsimulationstoenhancesafetyandefficacy.ThemomentumoftheseinstitutionalcollaborationssignalsaneweraforAIDD,wherecomputational

innovationandclinicalrigorconvergetoredefinethefutureofdrugdevelopment.

However,atthecurrentstage,itremainsunclearwhetherAIDDwillidentifysuperiordrugthanhuman-baseddrugdiscovery,andweawaitmorelate-stageclinicaltrialdata.

•FromConcepttoClinicalTrials(2014-2023):From2014to2019,pioneerssuchasAtomwise,Exscientia,andInsilicoMedicinelaidthetechnicalgroundworkforAIDD,focusingondevelopingdeeplearningplatformsfortargetidentificationandmoleculardesignratherthanclinicalproducts.Thefield’spotentialwasvalidated

between2020and2021,whenMITresearchersrapidlydiscoveredthenovel

antibiotichalicinusingneuralnetworks,andDeepMind’sAlphaFoldsolvedthe

longstandingproteinstructurepredictionchallenge,revolutionizingstructure-baseddrugdesign.Exscientia’sDSP-1181becamethefirstAI-designeddrugtoreach

PhaseItrialsinrecordtime,provingAImoleculescouldadvancetohumantesting.From2021to2023,AIDDmaturedascompaniessuchasInsilicoMedicineand

Recursion(coveredatJPMbyUSbiotechanalystDr.PriyankaGrover)

demonstratedaccelerateddrugdevelopmentandtargetdiscovery,witharound75

AI-designeddrugsenteringclinicaltrialsacrossmorethan200companiesby2023.

•PharmaIntegrationBoom(2024-2025):MajorpharmacompaniesvalidatedAIDDwithmulti-billion-dollarpartnerships.InJan-2024,IsomorphicLabsannounced

partnershipsworth$3bnincombinedpotentialvalue—$1.7bnmilestoneswithEli

Lillyand$1.2bnwithNovartis—signalinginstitutionalconfidencefromtheworld’slargestpharmaceuticalcompanies.Recursion’sacquisitionofExscientiafor$688mninAug-2024consolidatedleadingAIplatforms,whileventurecapitaldeployed

$3.3bninAIDDfunding,includingarecord$1bnSeriesAforXairaTherapeutics.ByMarch2025,IsomorphicLabsraised$600mninSeriesAfunding,thelargestearly-stageAIDDfinancingonrecord.

•Clinicaldatavalidation(2025/+):InJune2025,Insilico’srentosertibbecamethe

firstfullyAI-designeddrugtoshowPhaseIIaclinicalefficacyinNatureMedicine,withpositivelungfunctionoutcomes,validatingAIDD’spotential—butalso

revealingsomesafetysignal(liverinjuryin22%/17%ofpatientsinthe30mg

BID/60mgQDgroup).WebelievethattheclinicalvalidationofrentosertibmarksapivotalinflectionpointforAI-drivendrugdiscovery,demonstratingthatalgorithmscandelivernotonlyspeedandnoveltybutalsorealtherapeuticbenefitinhumans.

AsAIDDentersthisnewera,thefocusisshiftingfromrapidmoleculegenerationtocomprehensivetranslationalvalidation,integratingmulti-modalpatientdata,real-

worldevidence,andadvancedsimulationtechniquestoimprovesafetyandefficacypredictions.

9

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AsiaPacificEquityResearch

06March2026

MNCPharmaCEOsnowframeAIDDasthefutureofR&D

MultinationalpharmacompaniesnowtalkaboutAIdrugdiscoveryasmandatory,

long‑horizonR&Dinfrastructureratherthanaspeculativesidebet,withCEOsof

companiessuchasEliLillyandSanofipubliclycommittingtobillion‑dollarcomputebuildsandflagshippartnershipswhileopenlyguidinginvestorstoexpectpayoffmainlyaslate‑2020sproductivitygains—fastercycletimes,richerbiologicalmaps,andbettertrialdesign—ratherthananimminentstep‑changeinclinicalsuccessrates.We

summarizerecentmilestonesfrommanagementofseveralMNCpharma:

Table3:MNCPharma:MilestonesonAIDD

Source:Companyreports.

Thiswasechoedbycompaniesatthe2026J.P.MorganGlobalHealthcareConference

Morerecently,corporatesattendingtheJ.P.MorganGlobalHealthcareConference

emphasizedtheimportanceofincorporatingAIintheirdrugdevelopmentprocess.LifesciencetoolscompaniesframedAIasaworkflowandproductivitylayerthatimprovesthequalityandthroughputofexperimentaldata(supportingfasteriteration),while

MedTechandclinical-dataplayershighlightedAI’sabilitytostandardizemeasurementsandendpoints,reducingsite-to-sitevariabilityandothersourcesoftrialnoise.Atthe

sametime,AI-nativebiotechsemphasizedthattheinvestmentcasenowdependson

clinicalproof-of-conceptandexecutiongains(e.g.,improvedpatientselectionand

enrollment),notjustplatformscale.Overall,themessagewasthatAI’smostdefensiblevalueisemergingwhereitisembeddedintorealworkflowsandtiedtotime,cost,andprobability-of-successimprovements.

AIisalsomovingdownstreamtoimproveclinicaldevelopmentbyreducingnoise

intrials.GEHealthCare(coveredatJPMbyRobbieMarcus)andSiemensHealthineers(coveredatJPMbyDavidAdlington)emphasizedAIthatisembeddedinimaging

devicestospeedscans,improveimagequality,andautomatereporting,andtheyare

buildingsoftwareplatformstodeployandmanageAIacrossworkflows.Inpractice,thiscanreducetrial“noise”bymakingmeasurementsmoreconsistentacrosssitesand

timepoints(standardizedacquisitionandanalysis),andbyenablingmoreobjective,

repeatableimaging-basedendpoints—bothofwhichcanmakedrugeffectseasierto

detect.ThermoFisher’s(coveredatJPMbyCaseyWoodring)plannedClario

acquisitionreinforcesthisdirection:Clarioispositionedaroundendpointdatasolutions

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AsiaPacificEquityResearch

06March2026

andAIforclinicaltrials,whichpointstotighterendpointcollection,QC,andharmonizationacrossmodalities.

AIisbecomingapracticaltooltospeeduplabworkandshortendiscovery

timelines.LifesciencetoolscompaniesdescribeAIassomethingthatimproveshow

dataiscreatedandanalyzed,ratherthanastandalone“target-finding”solution.Better

instruments(e.g.,higher-sensitivitymassspectrometryandadvancedCryo-EM)

generatelarger,cleanerdatasetsthatAIcanlearnfrom,helpingresearcherstestideas

fasteranditeratemorequickly.ThermoFisheralsohighlightedAIpartnershipsasawaytodriveproductivityanddifferentiateproducts—suggestingAIvalueishighestwhenitisbuiltintoeverydayworkflowsandreducestimeperexperiment.

AI-nativedrugdiscoverycompaniesarenowjudgedbyclinicalproof,notjust

platformclaims.Recursionpositioneditsadvantagearoundproprietary,large-scalebiologydataplusintegratedwetanddrylabs,aimingtofindnewbiologyandmovefromideatocandidatefasterthantraditionalapproaches.ItalsohighlightedAItoolsthatuselargepatientdatasetstoimproveprotocoldesignandpatientselection,withearlyresultsshowinga10–40%increaseineligiblepatientpopulationsandimprovedenrollmentrates.ThekeytakeawayforinvestorsisthatAIdrugdiscoveryisshiftingfrom“promise”to“proof”:novelinsightsneedtotranslateintohumandata,faster

programs,andclearerdevelopmentsignals.

RiseofChineseAIDDplayers

AccordingtoFrost&Sullivan,theglobalAI-empoweredpharmaceuticalR&DexpensemarketwillexpandfromUS$11.9billionin2023toUS$74.6billionin2032,ata

CAGRof22.6%.InChina,theAIDDsectorhasseensignificantgrowthovertheyears,withAIDD-focusedcompaniesnowexceeding111,asofOct.2025,accordingtonewsreports(link).InsilicoandXtalPiarethemarketleaderswithtwodistinctbusiness

models.XtalPiisaplatform-firstcompanythatfocusesonofferingCRO/CRDMOservicestopartnersusingtheirAI/PhysicsengineswhileInsilicoisapipeline-firstcompanywhichfocusesondevelopingtheirownproprietaryassetstovalidatetheirplatform’scompetitivenessandlicensingtheseassetsoutatlaterstages.

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06March2026

Figure3:TheglobalAI-empoweredPharmaR&DexpensemarketisexpectedtogrowataCAGRof

22.6%from2023to2032

Source:Frost&Sullivan.

InsilicoMedicine(英矽智能)

InsilicoMedicine(NotCovered*)standsasoneoftheprominentAI-drivendrug

discoverycompanieswithstrongtiestoChina,maintainingdualheadquartersinBostonandHongKong.Foundedin2014,thecompanyhaspioneeredtheapplicationof

generativeAIanddeeplearningtodrugdiscovery,targetidentification,andclinicaltrialoutcomeprediction.Thecompany’sproprietaryAIplatform,Pharma.AI,encompasses

threecorecomponents:PandaOmicsfortargetdiscovery,Chemistry42formolecule

generation,andInClinicoforclinicaltrialprediction.Thecompanyisalsoworkingon

LLMforlifescienceandmanyotherAItoolsfordrugdiscovery.Insilico’skeyassets

includerentosertib(ISM001-055),afirst-in-classsmallmoleculeinhibitorofTINK

(TRAF2-andNCK-interactingkinase)forIPF(idiopathicpulmonaryfibrosis),which

becamethefirstAI-discovereddrugforanAI-discoveredtargettoenterPhaseIIclinicaltrials.Thecompanyhasbuiltapipelinespanningoncology,fibrosis,immunology,and

centralnervoussystemdisorders.Thecompanyhasnominated20preclinicalcandidatesfrom2021to2024,withanaveragetimefromprojectinitiationtopreclinicalcandidatenominationinjust12to18months.Intermsofrecentdevelopments,Insilicoentered

intoalan