2025年数据和AI驱动的生物设计报告（英文版）--Rand兰德

June2025

ExpertInsights

PERSPECTIVEONATIMELYPOLICYISSUE

ALLISONBERKE,FORRESTW.CRAWFORD,TOBYWEBSTER,JAMESSMITH,SANAZAKARIA,SELLANEVO

DataandAI-

EnabledBiological

Design

RisksRelatedtoBiologicalTrainingDataand

OpportunitiesforGovernance

PE-A3886-1

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AboutThisPaper

Inthispaper,weassesscurrentknowledgeaboutthelinkbetweenbiologicaldataandthe

capabilitiesofartificialintelligencemodelstrainedonlargevolumesofbiologicaldata(AI-biomodels),describetheanticipatedimpactsofnewbiologicaldatasources,andoutlinepotentiallydangerous

capabilitiesthatcouldcomefrombroadavailabilityofcertaintypesofbiologicaldata+Wethen

recommendstrategiestolimitthepotentiallydangerouscapabilitiesarisingfrombiologicaldata,includingoptionsforgovernanceofexperimentsanddatacreation,governanceofcurationand

aggregationsofdata,controlsonaccesstocollectionsofdata,andgovernanceoftheuseofdataformodeltraining+

TheaudienceforthispaperincludesAIsafetyandsecurityinstitutesintheUnitedStatesandabroad,developersofAImodels,organizationsthatcompileandmanagelargebiologicaldatasets(particularlythoseavailablepubliclythatmaybeusedtotrainAImodels),andpolicymakers

interestedinbiosecurity+

MeselsonCenter

RANDGlobalandEmergingRisksisadivisionofRANDthatdeliversrigorousandobjectivepublicpolicyresearchonthemostconsequentialchallengestocivilizationandglobalsecurity+Thisworkwasundertakenbythedivision’sMeselsonCenter,whichisdedicatedtoreducingrisksfrombiologicalthreatsandemergingtechnologies+Thecentercombinespolicyresearchwithtechnicalresearchtoprovidepolicymakerswiththeinformationneededtoprevent,preparefor,andmitigatelarge-scalecatastrophes+Formoreinformation,contact

meselson@rand+org+

Funding

ThisresearchwasindependentlyinitiatedandconductedwithintheMeselsonCenterusinggiftsforresearchatRAND’sdiscretionfromphilanthropicsupporterOpenPhilanthropy,aswellasgiftsfromotherRANDsupportersandincomefromoperations+RANDdonorsandgrantorshaveno

influenceoverresearchfindingsorrecommendations+

Acknowledgments

WearegratefultoRogerBrent,GeraldEpstein,JosephFair,AlisonHottes,JeffreyLee,Greg

McKelvey,BriaPersaud,AdelineWilliams,peerreviewersSarahCarterandTaylorFrey,andHenryWillisforhelpfulcomments+WethankEpochforsharingdataonAI-biomodelcapabilities,aswellasresearchpartners,supportstaff,andpublishingstaff+

iv

Summary

Modernartificialintelligence(AI)modelstrainedonlargevolumesofbiologicaldata(AI-bio

models)exhibitstrikingnewcapabilities,includingpredictionofproteinfoldingandbindingbehavior,sequencegeneration,andpredictionofhigher-orderfunctionalproperties+Thereisarecenttrendofcapabilityimprovementthatisalsolikelytocontinue+Decreasingcostsofgenomicsequencingand

ongoingeffortstoexpandenvironmentalbiosurveillancecoulddramaticallyexpandtheamountof

biologicaldatathatareusedtotrainadvancedAImodels+Similarly,decreasingcostsofcomputationalresources,newAImodelarchitectures,andnewmodeltrainingalgorithmscouldpermitAImodelstobetrainedonmuchlargerdatasets+

AI-biomodelscouldhavemanypositiveimpactsonscientificresearchandhealth,including

assistingindiscoveryofnewtherapeuticsorpredictingcomplexmolecularfoldingandbinding

behaviorinsupportofbasicscientificresearchgoals+ButsomeAI-biomodelsmaybedualuse,

providingbothbeneficialandpotentiallydangerouscapabilities+Potentiallydangerouscapabilities

includetheabilitiestodesigntoxins,modifyexistingpathogensforincreasedvirulence,ordenovo

designavirus,asexploredinarecentreportfromtheNationalAcademiesofSciences,Engineering,andMedicine(2025a)+AnefariousactorwithaccesstoafrontierAI-biomodelmightbeabletouseittodesignapathogenwithharmfulphenotypiccharacteristicsthatenhancetransmissibility+1

ResearchershaveassessedthecurrentgenerationofAI-biomodelsfordangerouscapabilitiesandprovidedrecommendationsthatmaypreventAI-biomodelsfrombeingusedformalignpurposes+Butmodelcapabilitiesarecloselylinkedtothedatausedtotrainthem,andmuchlessattentionhasbeendevotedtotherelationshipbetweendangerouscapabilitiesandbiologicaltrainingdata+Thedatathatareincluded(orexcluded)inmodeltrainingheavilyinfluencesthemodels’capabilitiesand

limitations.Trainingondiversebiologicaldatasets—sequence,structure,andfunctionaldata,suchasviralgenomesorproteinthree-dimensional(3D)structures—canexpandamodel’sbiological

capabilities,whereasmissingorincompletedatacancreateblindspots+GovernanceofdatausedtotrainAI-biomodelscouldbeausefulwaytoallowbeneficialscientificresearchwhilesafeguardingagainstpotentiallydangerouscapabilities+Potentiallydangerouscapabilitiesincludetheabilitytodesigntoxins,modifyexistingpathogensforincreasedvirulence,ordenovodesignavirus,

Inthispaper,weassesscurrentknowledgeaboutthelinkbetweenbiologicaldataandAI-bio

modelcapabilities,describetheanticipatedimpactsofnewbiologicaldatasources,andoutline

potentiallydangerouscapabilitiesthatcouldcomefrombroadavailabilityofcertaintypesofbiologicaldata+Wethenrecommendstrategiestolimitthepotentiallydangerouscapabilitiesarisingfrom

biologicaldata,includingoptionsforgovernanceofexperimentsanddatacreation,governanceof

1AfrontierAI–biomodelisastate-of-the-artfoundationalandgeneralpurposemodel,asopposedtoamodelnarrowlytunedtoaccomplishaparticulartasklikesequencealignment+

v

curationandaggregationsofdata,controlsonaccesstocollectionsofdata,andgovernanceoftheuseofdataformodeltraining+

TheaudienceforthispaperincludesAIsafetyandsecurityinstitutesintheUnitedStatesandabroad,developersofAImodels,organizationsthatcompileandmanagelargebiologicaldatasets(particularlythoseavailablepubliclythatmaybeusedtotrainAImodels),andpolicymakers

interestedinbiosecurity+

KeyRecommendations

1+AImodeldevelopersshouldexploreandcharacterizetherelationshipbetweentrainingdataquantityandtypeandmodelcapabilities+Potentialimplementationmeasuresinclude

a+producingbetterperformancemetricsandbenchmarksfordangerouscapabilitiestohelpmeasuretheevolutionofAI-biomodelcapabilities

b+validatinglinksbetweenbiologicaldatatypeandvolumeanddangerouscapabilitiestopredictfuturetrendsincapabilities

c+conductingteststodeterminehowrestrictingsubsetsoftrainingdataaffectsmodel

capabilities,includingbeneficialandconcerningorpotentiallydangerouscapabilities

d+consideringtheeffectsoflarge-scaledatacollectionfromnewmetagenomicbiosurveillance

programsandpotentialdataaggregationsolutionsandaccesscontrolsfortheseprograms+

2+AImodeldevelopersshouldconsiderimplementingdata-focusedmitigationsaspartofaportfolioofapproachestoreducethepotentialmisuseofAI-enabledbiology+Potentialimplementationmeasuresinclude

a+monitoringcollectionandaggregationofpathogensequence,structure,andfunctional

datatoprovidesituationalawarenessaboutthevolumeandtypeofdatathatcouldbeusedtotraindual-useAI-biomodels

b+monitoringdatasourcesusedtotraingeneral-purposeanddedicatedmodelsthatcanpredictordesignfunctionalpropertiesrelatedtopathogenicity(e+g+,activity,binding)+

3+PolicymakersshoulddevelopusageguidelinesforanypersonorentitythatusesU+S+

government–fundedbiologicaldatasetsthatcouldbeusedtotrainAImodels+Potentialimplementationmeasuresinclude

a+evaluatingthecostsandbenefitsofimplementingmeasurestocontroltheuseoffederallyfundeddataandensuringtheirsecurityandintegrity

b+advisingtheresearchersusingfederallyfundeddatatotrainAImodelsaboutusage

guidelinesforthecapabilitiesoftheresultantAImodels,includingadviceonavoiding

dual-usecapabilitiesandavoidingcreatingthepotentialformisusebytrainingmodelsonpathogendata

c+consideringdevelopingguidanceforfederalagenciestoimplementstewardshipof

biologicaldatasetsthatprotectstheuseandqualityofthosedatasetswithregardtoAItraining+

vi

4+AIdevelopersandpolicymakersshouldconductacapabilityassessmentwhencollectingandaggregatingpathogendataandwhentrainingmodelsonpathogendata+Thiscapability

assessmentcouldincludepredictedmodelcapabilitiesandanassessmentoftheconsequencesofmakingfunctionalpathogendatapublicandwidelyavailable+Potentialimplementation

measuresinclude

a+developingguidelinesforaccesscontrolmeasuresforfutureconcerningorpotentiallydangerousdatasets;theseguidelinescouldincludereviewsofplansforanyAImodeltrainingpriortoaccess

b+conductingriskassessmentswhencollectingandaggregatingpathogendataorwhenusingthosedatatotrainAImodels,includingthedegreetowhichsuchdatasetsoraggregationsmayalreadyhavebeenmirroredinrepositoriesthatareoutsidedirectcontrol,and

monitoringdatasourcesthatcanpredictordesignpropertiesrelatedtopathogenicity+

vii

Contents

AboutThisPaper+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++iiiSummary++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ivTables+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++viii

CHAPTER1 1

Introduction+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++1

DangerousCapabilitiesEnabledbyAI-EnabledBiology++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++2

CHAPTER2 4

BiologicalDataandAI-BioModels++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++4TheDevelopmentandIdentificationofDangerousCapabilitiesinAI-BioModels+++++++++++++++++++++++++++++++++++++++++++9BiosecurityConcernsfromDataUsedtoTrainLeadingModels+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++10CharacteristicsContributingtoDataThatConferDangerousCapabilities++++++++++++++++++++++++++++++++++++++++++++++++++++++15

CHAPTER319

DataGovernanceOptionsforDangerousCapabilityReduction+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++19GovernanceofExperimentsandDataCreation+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++20GovernanceofAccess,Curation,andUseofExistingData+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++22

CHAPTER425

Recommendations+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++25RecommendationsforDevelopers+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++25RecommendationsforPolicymakers++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++26RecommendationforBothDevelopersandPolicymakers++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++27

Abbreviations+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++29References+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++30

viii

Tables

Table2.1.CommonBiologicalDatasetsandTheirDevelopment,byDataType 5

Table2.2.CharacteristicsofBiologicalAIModelsTrainedonLargeDatasets 6

Table2.3.Dual-UseAI-EnabledBiologicalDesignCapabilities 12

Table2.4.Dual-UseDataandItsUseinTrainingAIModels 13

Table2.5.ExamplesofDualUseJustificationsforIncludingPotentiallyHazardousPathogenDataand

ExampleSources 16

1

Chapter1

Introduction

Artificialintelligence(AI)modelsarebeingtrainedonlarge-scalebiologicaldata,including

sequences,structure,andannotations,mostofwhicharederivedfromphysicalexperiments+AnAI-biomodelisanAImodelthathasaspecificpurposeinthebiologicalsciencesandthathasbeentrainedonbiologicaldata+2Examplesofmodelstrainedbylarge-scalebiologicaldataincludeAlphaFold,

ESM-2,Evo,andProGen2,asdescribedinaMarch2025NationalAcademiesofSciences,

Engineering,andMedicine(NASEM)report(2025a)+DatasourcesusedtotraintheseAI-biomodelsincludetheNationalCenterforBiotechnologyInformation(NCBI)9s(undated)GenBank,the

ProteinDataBank(PDB)(Sussmanetal+,1998),andtheSwissBioinformaticsResourcePortal

Expasy(Gasteigeretal+,2003),andmanyothersthatspansequences,structures,andfunctionaldata(NASEM,2025a)+Therehasbeenmajorgrowthinbiologicaldataoverthepastfewdecades,andthegrowthrateremainsrapid(GohandWong,2020)+Abroadrangeofpubliclyavailabledataand

privatedataareusedformodeltraining+Thisincludespathogendatabases,dataontoxins,andotherdataonpotentiallyconcerningordangerousorganismsorbiologicalmaterials+Forthepurposesofthiswork,weconsiderconcerningordangerousorganismsormaterialstobecontrolledpathogens(e+g+,

pathogenswhosepossessioniscontrolledbytheFederalSelectAgentProgramorbyexportcontrols)andneworengineered“transmissiblebiologicalagentswithepidemicorpandemicpotential”

(NASEM,2025a,p+2)+

ThecurrentcohortofAI-biomodels,beginningwithAlphaFoldin2018(Shi,2024),has

demonstratedexcellentperformanceatsuchtasksasproteinfoldingandstructureprediction(for

example,in2018and2020,AlphaFoldwontheCriticalAssessmentofproteinStructurePrediction[CASP],aninternationalprotein-foldingpredictioncompetition,andin2022themajorityofentrantsusedAlphaFold[Jumperetal+,2021;Callaway,2023])+Modelscanalsodesignandpredictsequences,mutationalfunctions,andbindingrelationships+Itcouldbethecasethatsmallermodelstrainedwithlesscomputecandisplaycapabilitiesthataremoreconcerningthanthoseoflargermodels+In

2TheU+S+governmentprovidesthefollowingrelevantdefinitionsinExecutiveOrder(EO)14110:

“[A]rtificialintelligence”or“AI”hasthemeaningsetforthin15U+S+C+9401(3):amachine-basedsystem

thatcan,foragivensetofhuman-definedobjectives,makepredictions,recommendations,ordecisions

influencingrealorvirtualenvironments+Artificialintelligencesystemsusemachine-andhuman-basedinputstoperceiverealandvirtualenvironments;abstractsuchperceptionsintomodelsthroughanalysisinan

automatedmanner;andusemodelinferencetoformulateoptionsforinformationoraction”(EO14110,2023,Section3)+

“AImodel”meansacomponentofaninformationsystemthatimplementsAItechnologyanduses

computational,statistical,ormachine-learningtechniquestoproduceoutputsfromagivensetofinputs(EO14110,2023,Section3)+

2

particular,thiscouldbethecaseifthesmallermodelsaregivenspecifictraining,includingaccesstotrainingdatadirectlyrelatedtodual-usecapabilities+

Modelcapabilitiesareprimarilymeasuredthroughperformanceonbenchmarksandevaluations,suchasCASP(Kryshtafovychetal+,2023)(forproteinfolding),BioLP-bench(Ivanov,2024)(for

biologicalprotocolgeneration),LAB-bench(Laurentetal+,2024)(forpracticalscientificknowledge),orGUANinE(RobsonandIoannidis,2024)(forfunctionalpredictionfromsequencedata)+3

Accuracycomparedwithknowndataorexperimentalstandards,lackofhallucinationsanderrors,efficiency,andspecificityarecomponentsofmodelperformanceontheseevaluations+

IncreasingthequantityoftrainingdatausedbyAI-biomodelsislikelytoimprovethecapabilitiesofthesemodels,buttherelationshipbetweenamountortypeoftrainingdataandcapability

developmentisnotwellunderstood+Wedonotcurrentlyhaveevidencedemonstratinghowbiologicalmodels’performanceimproveswithincreasedtrainingdata+Insteadofstrictlyfocusingontraining

dataquantity,trainingdatadiversitymayalsocontributetothecapabilitiesofAI-biomodels+Asa

resultofshotgunsequencingandothermassivelyparalleltechniquesthatspeedtheprocessof

sequencinglargequantitiesofnucleicacids(Satametal+,2023),sequencedataaregrowingmore

rapidlythanstructureorannotationdata+Annotationdata,separatefromsequencedata,include

informationaboutthemolecularproperties,transcriptomicproperties,andepigenomicpropertiesofagivensequenceorstructure+Thesepropertiescanincludefunctionalinformation,suchasexpressionandactivitydataforaparticularsequence+However,themajorityoftheapplicationsofAI-biomodelsinvolvepredictingstructureandfunction(asacomponentofannotation),meaningthatadditional

sequencedatamaynotdirectlycorrelatetoadditionalfunctionalityforAI-biomodels+

Inthispaper,weassesscurrentknowledgeaboutthelinkbetweenbiologicaldataandAI-bio

modelcapabilities,describetheanticipatedimpactsofnewsourcesofbiologicaldata,andoutline

dangerousAImodelcapabilitiesthatcouldarisefrombroadavailabilityofcertaintypesofbiologicaldata+Werecommendstrategiestolimitthepotentiallydangerouscapabilitiesarisingfrombiologicaldata,includingoptionsforgovernanceofexperimentsanddatacreation,governanceofcurationandaggregationsofdata,controlsonaccesstocollectionsofdata,andgovernanceoftheuseofdatafor

modeltraining+Thispaperdoesnotidentifyspecifictypesofpathogendatathatshouldberestricted,nordoesitdevelopquantitativerelationshipsbetweentypesandquantitiesoftrainingdataandmodelcapabilities+

DangerousCapabilitiesMadePossiblebyAI-EnabledBiology

AI-biomodelsenablepeoplewithbiologicalexpertiseandeducationtoperformbiological

predictionanddesigntasksmorequicklyandefficiently,andtheycouldallowpeoplewithlittleornobiologicaleducationtoperformbiologicalanalysisandmodelingthatwouldotherwiselikelyrequiregreaterexpertise(Pannuetal+,2024;Bloomfieldetal+,2024)+AI-biomodelshavegreatlyimprovedthestateofproteinfoldingandstructurepredictionabilitiessincethedevelopmentofAlphaFold,aprogramthatenablestheinsilicodesignofproteinsandpeptideswithdesiredstructuralorsequence

3BioLP-bench=BiologicalLabProtocolbenchmark;LAB-bench=LanguageAgentBiologyBenchmark;GUANinE=GenomeUnderstandingandANnotationinsilicoEvaluation+

3

characteristics(Jumperetal+,2021;EuropeanMolecularBiologyLaboratory–European

BioinformaticsInstitute[EMBL-EBI],2025)+TheimprovementinAIbiotoolcapabilitieshasbeenhypothesizedtoenablethedesignofnovelorenhancedpathogens,biotoxins,andotherpotentiallydangerousbiologicalmaterials(Hunter,2024)+TheuseofAI-biomodelstoperformphenotypic

predictionfromasequencecanallowfortheefficientdesignofbiologicalmaterialsthatpossessa

certainphenotype,enablingthedesignofmoleculesthatevadeimmuneorantibioticbinding,orhavepredictedbindingcapabilities(Luetal+,2023;Sandbrinketal+,2023;Abramsonetal+,2024)+AI-biomodelshavesubstantialandexcitingbeneficialuses,suchasindrugdiscoveryorbasicresearch,buttheirpotentialforharmshouldnotgounexplored(Wheeler,2025)+

Inthispaper,weprovidecapabilityassessmentguidance+Thisguidanceisintendedtobeappliedtotypesofbiologicaldatathatcouldbeusedtotraindual-useAI-biomodels+Thisworkdrawsontheauthors’experienceandexpertiseinbiology,biotechnology,andgovernanceofdual-useresearch+Inaddition,weconductedareviewofrelevantresearchandgrayliterature(includingthinktankand

industryreports,commentaries,orrecommendations)relatedtoAI-enabledbiology,biologicaldata,andgovernanceofdata+

Wefocusonprovidingoptionsandrecommendationsongovernanceofbiologicaldatausedto

trainAI-biomodelstopreventdeliberatemisuseofAI-biomodelsinthedesignofbiologicalweapons+Inthispaper,wedonotconsiderwaysthatAI-biomodelscouldbeusedtoassistdevelopersof

bioweaponsacrossthebioweapondevelopmentprocess,includinginstagesearlierthanthedesignofaweapon(suchasexperimentationaroundboththedevelopmentandinclusionoffunctional

capabilitiesinanon-weaponizedbiologicalconstruct)(Mouton,Lucas,andGuest,2023)+

4

Chapter2

BiologicalDataandAI-BioModels

Therearemanywaystocategorizebiologicaldata(WooleyandLin,2005),andseveralare

relevanttoourpurposes+Wehavechosentousethreesimplecategories(sequence,structure,and

functionaldata)thatwefeelbestreflecthowdataareusedtotrainAImodelsandhowthatrelatestothebiologicalcapabilitiesofthesemodels,especiallythosemodelsthatarerelatedtopathogens+Table2+1describescommonsourcesofsequence,structure,andfunctionaldatausedtotrainAI-biomodels+Manyofthesedatasourcesarepublicandgrowing,therebyprovidingadditionaltrainingdataforAI-biomodels+

Modelcapabilitiesareunderstoodtoincreaseasthequantityoftrainingdataincreases,butthe

relationshipisnotdirectlylinear,andthisisthoughttoholdtrueforbothbeneficialmodelcapabilitiesanddangerouscapabilities(Maug,O’Gara,andBesiroglu,2024)+Itwouldbeusefultobeableto

predicthowthecapabilitiesofAImodelswillchangeovertime+Forlargelanguagemodels,whicharetrainedondatathatarenotexclusivelybiologicaldataandhavemoregeneralfunctionalitytorespondtonatural-languagequeriesandgeneratetextorimages,wecanobserveandcharacterizerelationshipsbetweenmodelperformanceonagiventask;amountofcomputeusedfortraining;andthetype,

quality,andvolumeofdatausedtotrainit+High-levelrelationshipshavebeenobservedinthiswayforlargelanguagemodelsandareoftenreferredtoas“scalinglaws”(Kaplanetal+,2020)+Forlanguage

models,increasingtrainingcompute,datavolume,andmodelsizegenerallyimprovesperformance+

Thereisearlyevidenceshowingthatsomebiologicalmodels’performancesimprovewithincreased

trainingcompute(Workman,2024)andmodelsize(Hesslow,2022)+Asofthetimeofwriting,thereisnoevidencedemonstratinghowbiologicalmodels’performancesimprovewithincreasedtraining

data+Ifoneobservesthatbiologicalmodels’performancesdoscalewithincreasedtrainingdataorthattrainingdatavolumebecomesalimitingfactorwhenscalingcomputeormodelsize,theninterventionsfocusedonthegeneration,availability,oruseofdatamayinfluencemodeldevelopment+

5

Table2.1.CommonBiologicalDatasetsandTheirDevelopment,byDataType

DataType

ExampleDatasets

Numberof

Entries(Asof

February

2025)

Annual

GrowthRate

(Asof2025)

MethodofDataGeneration

Accessibility

DNA

sequence

Genbank,NCBI,USA(NCBI,undated),est.1982

3.4billionsequences

31.3%

DNAsequencing

Public

EuropeanNucleotide

Archive,Europe(EuropeanNucleotideArchive,

undated),est.1982

5billion

sequences

23%

DNAsequencing

Public

DNADataBankofJapan,Japan(BioinformationandDNADataBankofJapan

Center,undated),est.1986

193trillion

sequences

9.3%

DNAsequencing

Public

Protein

seq