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1、Artificial Intelligence for Drug DiscoveryLandscape OverviewQ3 2022www.deep-pharma.tech2Deep Pharma IntelligenceAI for Drug Discovery Infographic Summary and Mind Maps3Executive Summary17Application of AI for Advanced R&D23Business Activity:Key Trends28Industry developments:Challenges and Forecasts3
2、1Business Activity:Overview33Leading Companies by Amount and Stage of Funding3450 Leading Investors in Pharmaceutical AI 40Big Pharmas Focus on AI48AI in Pharma Publicly Traded Companies57Top Publicly Traded Companies Related to AI-Pharma79AI for Advanced R&D:Applications and Use Cases89Top AI Break
3、throughs Computational Methods Used by the Most Advanced AI Companies9615 Notable R&D Use Cases of AI Application in Biopharma104Industry Developments Key Takeaways148Appendix:List of Entities154Overview of Proprietary Analytics by Deep Pharma Intelligence185Disclaimer190This
4、135-page Artificial Intelligence for Drug Discovery Landscape Overview Q3 2022 report represents the eleventh issue of market analytics focused on the Artificial Intelligence(AI)application in the pharmaceutical research industry.The primary goal of this series of reports is to give a complete pictu
5、re of the industry environment in terms of AI usage in drug discovery,clinical research,and other elements of pharmaceutical research and development.This overview highlights recent trends and insights in the form of helpful mind maps and infographics and gauges the performance of prominent players
6、who shape the industrys space and relationships.It can help the reader comprehend what is going on in the sector and potentially predict what will happen next.Since the last edition,data has been significantly updated to reflect the fast-paced market dynamics and an overall increase in pharmaceutica
7、l AI investment and business development activities.The listings of AI-biotech businesses,biotech investors,and pharmaceutical organizations have been expanded to reflect the pharmaceutical industrys rising interest in sophisticated data analytics technology.Alongside investment and business trends,
8、the report also provides technical insights into some of the latest AI applications and research achievements.2Deep Pharma IntelligenceTable of ContentsIntroductionClinical DevelopmentData ProcessingEarly Drug DevelopmentEnd-to-end Drug DevelopmentInvestorsAI CompaniesCorporationsDEEP PHARMA INTELLI
9、GENCEPharmaTechCROPreclinical DevelopmentDrug RepurposingArtificial Intelligence for Drug DiscoveryLandscape Overview Q3 2022AI Companies-600Investors-1200Corporations-100Selected Pharma AI Deals AI Companies Pharma CorporationsAI Companies 44Deep Pharma IntelligencePharma orporationsNote:the centra
10、l column(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.Selected Pharma AI Deals 5Deep Pharma IntelligenceAI Companies Pharma CorporationsAI Companies Pharma orporationsNote:the central col
11、umn(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.40 Leading Companies in AI for Drug Discovery SectorDeep Pharma IntelligenceAtomwiseAbCelleraAI TherapeuticsBeacon Biosignals3BIGS1A2APhar
12、ma 234AnimaBiotech5Ardigen6AriaPharmaceuticals7Auransa8910Benevolent AlBergBioage Labs1112Berkeley Lights1314 Biovista15 Black Diamond Therapeutics16 ConcertAl17 Cyclica18 CytoReason19 Deargen20EnvisagenicsExscientiaNuritasDeepGenomics21DeepMindHealth222324Healx25Insillico Medicine26Insitro27Lantern
13、 Pharma28Neumora2930PharnextRecursionSchrodinger 3132ReviveMed3334 SensyneHealth35 Silicon36 Standigm37 Turbine38 Valo39 XtalPi40Deep Pharma Intelligence6Comparison of Top-40 Leading AI for Drug Discovery Companies Expertise in Drug Discovery R&DAdvanced AI tools for specific Use CasesAdvanced AI sy
14、stems with multiple models End-to-end AIExpertise in Drug DiscoveryExpertise in AI7Deep Pharma IntelligenceClinical pipeline(phase 1-2)Validated R&D Use casesand preclinical pipeline8Deep Pharma IntelligenceAlexandria Venture InvestmentsSOSVNational Science FoundationRA Capital ManagementCasdin Capi
15、tal1Creative Destruction Lab(CDL)234GV5Y Combinator6Perceptive Advisors7Sequoia Capital China8910Merck Global HealthAlumni VenturesForesite Capital1112Khosla Ventures13148VC15DCVC Bio16National Institute of Health17EASME-EU Executive Agency18MassChallenge19T.Rowe Price20DeerfieldF-Prime CapitalSurve
16、yor CapitalSoftBank Vision Fund21Invus222324Redmile Group25DCVC Bio26Founders Fund27IndieBio28Fidelity Management2930Temasek HoldingCormorant Asset ManagementNorthpond Ventures31325Y Capital3334 Obvious Ventures35Andreessen Horowitz36Section 3237Lux Capital38AME Cloud Ventures39Eight Roads Ventures4
17、0BlackRockForesite CapitalBiotechnology Value FundLifeforce Capital41Felicis Ventures4344Janus Henderson Investors45Tencent46ARCH Venture Partners47Novo Holdings48Flagship Pioneering49504250 Leading Investors in AI for Drug Discovery Sector600 AI Companies:Regional ProportionThe US is still firmly i
18、n the lead regarding its proportion of AI for Drug Discovery companies.Interestingly,Asia and the Middle East continue to expand usage of AI technologies in the Pharmaceutical Industry.The ratio of companies that use AI for Drug Development in the UK and European countries is decreasing compared to
19、the Asian market.The Asia-Pacific region continues to aggressively increase the number of AI for Drug Discovery Companies,particularly in China,and this tendency will probably maintain.9Deep Pharma IntelligenceUS57.74%Canada4.83%UK8.40%China4.20%Asia5.46%Australia0.42%EU25.00%Middle East7.35%1120 In
20、vestors:Regional ProportionThe United States continues to lead the rest of the world in terms of artificial intelligence for companies and funds that invest in Drug Discovery.This is reasonable,given that more than a half of the worlds AI for Drug Discovery companies have their headquarters in USA.C
21、omparing with previous periods of 2021,we can observe significant growth of the number of investors in China,as well as in US as Europe.Thus,together with UK these regions are leaders by the number of investors in AI in Drug Discovery companies.10Deep Pharma IntelligenceCanada4.06%US52.78%UK6.00%EU1
22、8.71%China5.65%Asia&Middle East6.89%Australia1.06%50 Leading Investors:Regional ProportionThe United States continues to lead the rest of the world in terms of artificial intelligence for companies and funds that invest in Drug Discovery.This is reasonable,given that more than a half of the worlds A
23、I for Drug Discovery companies have their headquarters in USA.During 2021 we can observe significant growth of the number of investors in Asia,mainly in China,Hong Kong and Singapore.The USA,the UK,and EU remain to be leaders by the number of investors in AI in Pharma companies.11Deep Pharma Intelli
24、genceCanada1.85%UK5.56%EU5.56%US79.63%China3.70%Singapore3.70%Sources:Investment Digest AI in PharmaPharmas“AlphaGo Moment”12Deep Pharma Intelligence1990201220-21Practical ValueFundamental breakthroughs in AI theory (DL,NLP,etc)ImageNet and the rise of practical DLGANs and other advance
25、d NN structures“AlphaGo Moment”in pharma:practical validation in drug design,biotech R&DMature technology.Strategic competition for AI startups,a rising wave of investments/M&A deals2022-23Widespread adoption of AI in pharma (VR,digital twin,automated data analysis etc.),prioritisation of AI in R&D
26、Experimental ResultComputational PredictionNotable Breakthroughs in AI for Pharma13Deep Pharma IntelligenceDeep Genomics AI-driven platform predicted novel target and oligonucleotide candidate for Wilson disease in under 18 months.Insilico Medicine applied generative adversarial network-based system
27、 GENTRL for rapid identification of potent DDR1 Kinase inhibitors within 21 days.DeepMinds AlfaFold learns to predict proteins 3D shape from its amino-acid sequence,a 50 year-old grand challenge in biology.The University of Washington has developed a deep learning model,“RoseTTAFold”,that calculates
28、 protein structure on a single gaming computer within 10 minutes.Model201920202021ExperimentTechnological Advancements Defining the Market14Deep Pharma IntelligenceInsilico Medicine achieved industry-first fully AI-based Preclinical Candidate.Initial hypothesis was build via DNN analysis of omics an
29、d clinical datasets of patients.After that company used its AI PandaOmics engine for target discovery,analyzing all relevant data,including patents and research publications with NLP algorithms.In the next step Insilico has applied its generative chemistry module(Chemistry42)in order to design a lib
30、rary of small molecules that bind to the novel intracellular target revealed by PandaOmics.The series of novel small molecules generated by Chemistry42 showed promising on target inhibition.One particular hit ISM001 demonstrated activity with nanomolar(nM)IC50 values.When optimizing ISM001,Insilico
31、managed to achieve increased solubility,good ADME properties,and no sign of CYP inhibition with retained nanomolar potency.Interestingly,the optimized compounds also showed nanomolar potency against nine other targets related to fibrosis.The efficacy and a good safety of the molecule led to its nomi
32、nation as a pre-clinical drug candidate in December 2020 for IND-enabling studies.The phase I clinical trial for the novel drug candidate is planned for December 2021.1 week2 months4 months11 monthsPhase 1Phase 2Phase 3Submission to launchDisease Hypothesis&Target IdentificationTarget-to-hitHit-to-l
33、eadLead optimization and Candidate ValidationPreclinical candidate Selected(PCC)Up to Decades1 year1.5 years2 yearsPhase 1Phase 2Phase 3Submission to launchInsilico$50kN/A$200k$94M$400k$166M$200k$414MTraditional ApproachSource:Insilico Medicine 2021Executive SummaryDEEP PHARMA INTELLIGENCEThis 135-p
34、age“Artificial Intelligence for Drug Discovery Landscape Overview,Q3 2022”report marks the installment in a series of reports on the topic of the Artificial Intelligence(AI)application in pharmaceutical research industry that DPI have been producing since 2017.The main aim of this series of reports
35、is to provide a comprehensive overview of the industry landscape in what pertains adoption of AI in drug discovery,clinical research and other aspects of pharmaceutical R&D.This overview highlights trends and insights in a form of informative mind maps and infographics as well as benchmarks the perf
36、ormance of key players that form the space and relations within the industry.This is an overview analysis to help the reader understand what is happening in the industry nowadays and possibly give an idea of what is coming next.Alongside investment and business trends,the report also provides techni
37、cal insights into some of the latest achievements in the AI application and research.Report at a Glance16Deep Pharma IntelligenceLatest AchievementsNotable Case StudiesAI-Pharma CollaborationsBusiness TrendsTechnical InsightsCROsTech Companies600 AI BioTech Companies1200 InvestorsPharma CompaniesPha
38、rma Efficiency:Challenges17Deep Pharma Intelligence10 years+$2.6 bln=1 new drugIt takes on average over 10 years to bring a new drug to market.As of 2014,according to Tufts Center for the Study of Drug Development(CSDD),the cost of developing a new prescription drug that gains market approval is app
39、roximately$2.6 billion.This is a 145%increase,correcting for inflation,compared to the same report made in 2003.The pharmaceutical industry is in a terminal decline,and the returns on new drugs that do get to market do not justify the massive investments that Pharma currently puts into R&D anymore.T
40、he solution to this problem comes from three key strategies:evolution of business models towards more collaboration and pipeline diversification early implementation of AI as a universal shift towards data-centric drug discovery discovery of new therapeutic modalities(biologics,therapies,etc.)0-Effe
41、ct on bodyI-Safety in humansII-Effectiveness at treating diseasesIII-Larger scale safety and effectivenessIV-Long term safety1 approved drug5,000,000 compounds500 compounds5FDAPre-clinical developmentClinical developmentRegulatory approvalResultDrug discoverySource:Conflict of Interest in Medical Re
42、search,Education,and Practice,Computer-aided Drug Design18Deep Pharma IntelligenceScreeningModellingDe novo designTodays task for the pharma industry is to create a cheap and effective solution for drug development,companies apply various computational methods to reach that goal.Computer-aided drug
43、design(CADD)is a modern computational technique used in the drug discovery process to identify and develop a potential lead.CADD includes computational chemistry,molecular modeling,molecular design and rational drug design.Sources:Advantages of Structure-Based Drug Design Approaches in Neurological
44、Disorders.CADDDiscoveryMolecule SelectionOptimizationDatabasesValidationTarget Identification Homology Modelling Molecular Modelling Structure based Shape based Pharmacophore based Druggability pocket Compound identification QSAR Lead Optimization Docking and scoring Library design Affinity evaluati
45、on ADME estimation Small molecule databases 3D structure database Molecular fragment databaseComputer-aided Drug Design19Deep Pharma IntelligenceDatabasesSmall molecule databases3D structure databasesMolecular fragment databasesStructure-based virtual screeningBinding energy analysisScoringDockingCh
46、emical intuitionMolecular dynamic simulationAnalyze the interaction of target structure and lead candidateModellingHomology ModellingMolecular ModellingFunctional GenomicsTarget protein identificationBinding site predictionSources:Advantages of Structure-Based Drug Design Approaches in Neurological
47、Disorders.Modern computational structure-based drug design has established novel platforms that mostly have a similar structure for testing drug candidates.The usage of AI can simplify and facilitate the drug design from filtering datasets for appropriate compounds to advanced lead modification and
48、in silico testings.Big Pharmas AI-focused partnerships till Q3 2022In this report we have profiled 600 actively developing AI-driven biotech companies.A steady growth in the AI for Drug Discovery sector can be observed in terms of substantially increased amount of investment capital pouring into the
49、 AI-driven biotech companies($2.28B in HY 2020 against$2.93B in HY 2021),the increasing number of research partnerships between leading pharma organizations and AI-biotechs,and AI-technology vendors,a continuing pipeline of industry developments,research breakthroughs,and proof of concept studies,as
50、 well as exploding attention of leading media and consulting companies to the topic of AI in Pharma and healthcare.Some of the leading pharma executives increasingly see AI as not only a tool for lead identification,but also a more general tool to boost biology research and identify new biological t
51、argets and develop novel disease models.The main focus of AI research for today is still on small molecules as a therapeutic modality.20Deep Pharma Intelligence31 Deals28-38 Deals27-32 Deals23-27 DealsApplication of AI for Advanced R&D to Address Pharma Efficiency Challenges21Deep Pharma Intelligenc
52、eClinical TrialsAI for Advanced R&DTarget Discovery and Early Drug Discovery Aggregation and Synthesis of InformationRepurposing of Existing DrugsDesign and Processing of Preclinical ExperimentsAccelerated development of new drugs and targets identification Identify novel drug candidates Analyze dat
53、a from patient samples Predict pharmacological properties Simplify protein designTime-and resources-efficient information managementGenerate insights from thousands of unrelated data sourcesImprove decision-makingEliminate blind spots in researchSearching for new applications of existing drugs at a
54、high scale Rapidly identify new indications Match existing drugs with rare diseases Testing 1000+of compounds in 100+of cellular disease models in parallelOptimization of experiments and data processing Reduce time and cost of planning Decode open-and closed-access data Automate selection,manipulati
55、on,and analysis of cells Automate sample analysis with a robotic cloud laboratoryTargeted towards personalized approach and optimal data handling Optimize clinical trial study design Patient-representative computer models Define best personalized treatment Analyze medical records Improve pathology a
56、nalysisBusiness ActivityInsitro has raised$400M for machine learning-powered drug discovery efforts.The financing was led by the Canada Pension Plan Investment Board with additional backing from Andreessen Horowitz,Casdin CapitalValo Health announced the final closing of its Series B at$300M,includi
57、ng a$110 million investment from Koch Disruptive Technologies(KDT).This brings the overall funding of Valo to over$450M to accelerate the creation of life-changing drugsAmgen Mila partnership that permits Amgen to expand its knowledge of AI and deep learning by interacting and engaging with experts
58、in Milas unique ecosystemExscientia sealed a$5.2B deal(biggest deal of A.I.)to expand an ongoing collaboration agreement with Sanofi to include 15 new molecules.Anumana,Janssen and Mayo Clinic have developed ECG-based Pulmonary Hypertension(PH)Early Detection Algorithm which will help doctors identi
59、fy pulmonary hypertension early,a condition that is progressive and life-threatening.Microsoft and Novo Nordisk signed a contract to expedite the companys drug discovery process.22Deep Pharma IntelligenceThe business activity has been increasing in the pharmaceutical AI space over Q1 2022-Q3 2022,ju
60、dging by an increased number of transactions and partnership announcements in this period.The most significant deals and collaborations in include:Dynamics of Investments in AI in Drug DevelopmentThere has been a substantial increase in the amount of capital invested in AI-driven pharma companies si
61、nce 2014.During the last seven years,the annual amount of investments in 600 companies has increased by almost 52 times(to$115.84B in total as of October 2022).In 2021,the flow of investments increased by 143%compared to the previous year.The estimated amount of investments in the AI in Pharma sub-s
62、ector of the Longevity industry has increased in 2.5 times in 2021 compared to 2020 which identifies strong investors(foremost VCs)interest in this field regardless of risks.23InvestTech Advanced SolutionsDeep Pharma IntelligenceTop 10 AI in Pharma Companies by Total Investments in Q2-Q3 202224Inves
63、tTech Advanced SolutionsDeep Pharma IntelligenceThe chart shows the top 10 AI-driven drug discovery companies sorted by the total funding raised by the end of Q3 2022.XtalPi,an artificial intelligence-powered drug R&D company,is now at the top of the list.Having completed the business combination wi
64、th Excelra,XtalPi has the total funding raised to$791M.Insitro,american company utilizing ML drug discovery,could finance$743M in capital market.Tempus,Insitro and ThoughtSpot are new companies due to late-stage mega-rounds during the 2021.Major Observations for Q2-Q3 2022:Key Business TakeawaysThe
65、segment of pharmaceutical AI continues consolidation with the increasing number of later stage mega-rounds,including XtalPi,Neuromora Therapeutics and Insitro(both$400M),Medable and Biofourmis(both$304M),Insilico Medicine($255M),and DNAnexus and Genuity Science(both$200M).The AI start-up pack is cle
66、ar leaders with significant resources,financial leverage,technical edge,and laggards with fewer finances,technology,and scientific assets.Besides,there is one company that received IPO status recently:Benevolent AI.25Deep Pharma IntelligenceThe pharmaceutical AI business is“heating up”,becoming a pr
67、ofitable area for expert biotech investors as well as investor groups looking to diversify their portfolios with high-risk/high-reward firms.A growing number of proof-of-concept breakthroughs confirm that AI technology has matured enough to provide tangible value to pharma and contract research orga
68、nizations(CROs).Due to quickly growing proof of AI tech feasibility and innovation potential,big pharma and contract research organizations are actively competing for AI collaborations.Amgen and Generate Biomedicines will team together to find and develop protein therapies for five clinical targets
69、using a variety of treatment methods and therapeutic regions.Cyclica has announced 10 new academic partnerships.With its new agreements,Cyclica hopes to equip academia academics with AI-enhanced drug development platforms and hasten the process.Major Observations for Q2-Q3 2022:Key Business Takeaway
70、sThe global COVID-19 pandemic prolongs the rise of the overall biotech and drug discovery sectors.During 2021 we have observed over 100 medium and large funding rounds for biotech and drug design companies,especially those focused on antiviral therapies and vaccines.26Deep Pharma IntelligenceIn Q2-3
71、 2022,1 company that use AI for DD reached IPO status.London-based Benevolent AI closed its IPO in April and raised$292M.The vast majority of companies started gaining IPO status after 2018,marked by a growth of 136.0%during the last four years and we expect this trend growth to continue.When some o
72、f the companies complete IPOs in the nearest future,it will attract a significant number of non-biotech investors to enter the Life Sciences sector.The prospects of this trend are already vivid:big tech companies enter partnerships with both innovative start-ups and pharma companies to consolidate r
73、esources,mainly in personalized medicine,cell and gene therapy,and molecule prediction software.Some of these companies even open subsidiaries harvesting AI in Drug Design(like Isomorphic Labs from Google).The growing industry traction,reflected in the increasing number of R&D partnerships between b
74、ig pharma and CROs with AI startups,is a sign that the market is maturing for rapid increase in M&A activity in the nearest future.Because of the crisis AI-in-Drug Development publicly traded companies fell to$85,7B of cumulative capitalization as of October 3rd,2022.Key Technology Takeaways1.AI is
75、regarded by some top executives at big pharma(GSK and others)as a tool to uncover not only new molecules,but also new targets.Ability of deep neural networks to build ontologies from multimodal data(e.g.“omics”data)is believed to be among the most disruptive areas for AI in drug discovery,alongside
76、with data mining from unstructured data,like text(using natural language processing,NLP).2.There is a considerable trend for“AI democratization”where various machine learning/deep learning technologies become available in pre-trained,pre-configured“of-the-shelf”formats,or in relatively ready-to-use
77、formats via cloud-based models,frameworks,and drag-and-drop AI-pipeline building platforms(for example,KNIME).This is among key factors in the acceleration of AI adoption by the pharmaceutical organizations where a non-AI experts can potentially use fairly advanced data analytics tools for their res
78、earch.3.Proof-of-concept projects keep yielding successful results in research studies,and in the commercial partnerships alike.For example,companies like Recursion Pharmaceuticals,Insilico Medicine,Deep Genomics,and Exscientia achieved important research milestones using their AI-based drug design
79、platforms.27Deep Pharma IntelligenceAI democra-tizationsAI platforms yield successful resultsAI on different steps in DDAI is used not only for drug design,but also target identification.Many AI-designed drugs showed successful results in research studies and even clinical trials.Ready-to-use AI pla
80、tforms for DD became available and can be used by non-AI experts.AI in Biotech ChallengesLack of Quality DataObstacles That Still Remain28Deep Pharma Intelligence1.Global shortage of AI talent continues to be a serious challenge for the biopharma industry,repeating the trend from our previous report
81、s.While big pharmaceutical companies invest substantial capital in recruitment of AI specialists,still the majority of them are acquired by large tech corporations(Google,Amazon,Alibaba,Tencent,Baidu etc.)However,a growing wave of specialized university programs and courses,geared towards data scien
82、ce and AI application,is projected to address this issue to certain extent in the coming years.2.Lack of available quality data is still a challenge for the unleashing full potential of deep learning technologies.Numerous variations of deep learning(DL)are believed to be the most lucrative area of A
83、I for applications such as drug discovery and clinical research.The key challenge is that DL algorithms are“data-greedy”,while big data in biotech is not always well-versed for modeling,or is inaccessible due to privacy reasons.3.Ethical,legal,and regulatory issues for AI adoption in the pharmaceuti
84、cal sciences.This set of challenges is related to the previous point,but also includes other questions AI explainability,patentability of AI-generated results,non-optimal regulations in various countries,slowing down the progress and adoption of AI technologies in general,and in the pharmaceutical i
85、ndustry in particular.Lack of Specialists Ethical,Legal and Regulatory IssuesAI in the Global Context29US is a main player in AI industry In the beginning of AI implementation,US was a pioneer and then the main player with the greatest number of companies using AI to force R&D,research centres and i
86、nstitutes,and investments.China engages in extensive investment activityIn particular,it has promised to invest$5B in AI.Tianjin,one of the biggest municipalities,is going to invest$16B in its local AI industry,and the Beijing authorities will build$2.12B AI development project.China plans to become
87、 the world AI leader by 2030According to the AI Strategic Plan released in July 2017.The analysis of the the Asia-Pacific region has shown that the main forcers of AI implementation include Saama Technologies,Inc.,a leading clinical data analytics company.Europe has traditionally been a strong breed
88、ing ground for biopharma activity The UK and EU activity in the pharmaceutical AI race is mainly boosted by Novartis.UK-based BenevolentAI and AstraZeneca collaborate with novel AI-generated chronic kidney disease target.DEEP PHARMA INTELLIGENCEBusiness Activity:Overview Round A Round B Round C and
89、others IPO$155M$45M$197M$105M$20M$50M$123M$200M$61.2M$10MApollo Hospitals EnterpriseSchrodinger$562M$4.9MApollo Hospitals EnterpriseBioforumis$300M$143MApollo Hospitals EnterpriseRelay Therapeutics$350M$520MApollo Hospitals EnterpriseBerkeley Lights$20M$252MApollo Hospitals EnterpriseBenevolentAI$90
90、M$202MApollo Hospitals EnterpriseExscientia$100M$375MApollo Hospitals EnterpriseInsitro$400M$343MApollo Hospitals EnterpriseXtalPi$400M$386MApollo Hospitals EnterpriseApollo Hospitals EnterpriseAtomwiseValo Health$2.3M$174M$110M$300MFunding amount prior to the last dealFunding amount by the last dea
91、l Round A Round B Round C and Others IPO$155M$45M$197M$105M$20M$50M$123M$200M$61.2MFunding amount prior to the last dealFunding amount by the last deal$10MApollo Hospitals EnterprisePatSnap$52M$300MApollo Hospitals EnterpriseInsilico Medicine$60M$306MApollo Hospitals EnterpriseAetion$110M$94MiCarbon
92、X$20M$252MApollo Hospitals EnterpriseNimbus Therapeutics$105M$197MApollo Hospitals EnterpriseStandigm$10M$61MApollo Hospitals EnterpriseCellarity$123M$50MApollo Hospitals EnterpriseBIOAGE LABS$90M$34MApollo Hospitals EnterpriseIndigenePathAI$165M$90M$200M$155M$45M Round A Round B Round C and others
93、IPO$155M$45M$197M$105M$20M$50M$123M$200M$61.2M$10MApollo Hospitals EnterpriseNeuron23$114M$100MApollo Hospitals EnterpriseStoneWise$100M$10MApollo Hospitals EnterpriseStrateos$56M$46MApollo Hospitals EnterpriseGENFIT$45$48MApollo Hospitals EnterpriseSangamo Therapeutics$145M$377MApollo Hospitals Ent
94、erpriseTurbine AI$7M$88MApollo Hospitals EnterpriseDatavant$40MApollo Hospitals EnterpriseRecursion Pharmaceuticals$239M$225MApollo Hospitals EnterpriseApollo Hospitals EnterpriseDNAnexusNference$200M$2734M$61M$119M$40.5MFunding amount prior to the last dealFunding amount by the last deal Round A Ro
95、und B Round C and others IPO$155M$45M$197M$105M$20M$50M$123M$200M$61.2M$10MApollo Hospitals EnterpriseRoivant Sciences$1 900M$200MApollo Hospitals EnterpriseTempus$200M$850MApollo Hospitals EnterpriseHuman Longevity$30M$300MApollo Hospitals EnterpriseSynergy Pharmaceuticals$300$107MApollo Hospitals
96、EnterpriseGritstone Oncology$55M$341MApollo Hospitals EnterpriseFlatiron Health$11.9M$313MApollo Hospitals EnterpriseErasca$36MApollo Hospitals EnterpriseSOPHiA GENETICS$110M$140MApollo Hospitals EnterpriseApollo Hospitals EnterpriseITeos TherapeuticsNference$125M$125M$11.7M$290M$264MFunding amount
97、prior to the last dealFunding amount by the last deal Round A Round B Round C and others IPO$155M$45M$197M$105M$20M$50M$123M$200M$61.2M$10MApollo Hospitals EnterpriseIDEAYA Biosciences$140M$86MApollo Hospitals EnterpriseNeon Therapeutics$200M$125MApollo Hospitals EnterpriseNeumora Therapeutics$500MA
98、pollo Hospitals EnterpriseProscia$36.6M$34MApollo Hospitals EnterpriseMedable$304M$203Apollo Hospitals EnterpriseFoundation Medicine$13.5M$83MApollo Hospitals EnterpriseOwkin$20MApollo Hospitals EnterpriseAlector$133M$62MApollo Hospitals EnterpriseApollo Hospitals EnterpriseAi TherapeuticsArrakis Th
99、erapeutics$58M$40$75M$38M$254MFunding amount prior to the last dealFunding amount by the last deal$36M50 Leading Investors in Pharmaceutical AIDEEP PHARMA INTELLIGENCE37Deep Pharma IntelligenceAlexandria Venture InvestmentsSOSVNational Science FoundationRA Capital ManagementCasdin Capital1Creative D
100、estruction Lab(CDL)234GV5Y Combinator6Perceptive Advisors7Sequoia Capital China8910Merck Global HealthAlumni VenturesForesite Capital1112Khosla Ventures13148VC15DCVC Bio16National Institute of Health17EASME-EU Executive Agency18MassChallenge19T.Rowe Price20DeerfieldF-Prime CapitalSurveyor CapitalSof
101、tBank Vision Fund21Invus222324Redmile Group25DCVC Bio26Founders Fund27IndieBio28Fidelity Management2930Temasek HoldingCormorant Asset ManagementNorthpond Ventures31325Y Capital3334 Obvious Ventures35Andreessen Horowitz36Section 3237Lux Capital38AME Cloud Ventures39Eight Roads Ventures40BlackRockFore
102、site CapitalBiotechnology Value FundLifeforce Capital41Felicis Ventures4344Janus Henderson Investors45Tencent46ARCH Venture Partners47Novo Holdings48Flagship Pioneering49504250 Leading Investors in AI for Drug Discovery SectorCreative Destruction Lab(CDL)Toronto,CandaTop-50 AI in Pharma Investors38D
103、eep Pharma IntelligenceObvious VenturesSan Francisco,California,USLifeforce CapitalSan Francisco,California,USSan FranciscoAlexandria VentureSan Francisco,California,USForesite CapitalSan Francisco,California,USFounders FundSan Francisco,California,US8VCSan Francisco,California,USDCVC BioSan Francis
104、co,California,USDCVCSan Francisco,California,USNew YorkCasdin CapitalNew York,New York,USInvusNew York,New York,USPerceptive AdvisorsNew York,New York,USBristol-Myers SquibbNew York,New York,USOrbiMedNew York,New York,USMountain ViewY CombinatorMountain View,California,USGVMountain View,California,U
105、SPalo AltoAME CLoud VenturesPalo Alto,California,USLili VenturesIndianapolis,Indiana,USSOSVPrinceton,New Jersey,USNational Institute of HealthMaryland,UST.Rowe PriceBaltimore,Maryland,USNational Science FoundationAlexandria,Virginia,USAltitude Life Science VenturesWashington,USOther StatesManhattan
106、BeachB Capital GroupManhattan Beach,California,USMenlo ParkAndreessen HorowitzMenlo Park,California,USFelicis VenturesMenlo Park,California,USKhosla VenturesMenlo Park,California,USMassachusettsThird Rock VenturesBoston,Massachusetts,USRA Capital ManagementCambridge,Massachusetts,USF-Prime CapitalCa
107、mbridge,Massachusetts,USAlexandria Venture InvestmentsPasadena,California,USIllinoisDeerfield Capital Rosamond Ridge,Illinois,USARCH Venture PartnersChicago,Illinois,USEDBISingapore,Central RegionNovo HoldingsHellerup,Hovedstaden,DenmarkCounterpoint GlobalLondon,England,The UKSoftBank Vision FundLon
108、don,England,The UKBaillie GiffordEdinburgh,Edinburgh,The UKJanus Henderson InvestorsLondon,England,The UKBeijingZhenFundBeijing,ChinaPing An BankShenzhen,ChinaSequoia Capital ChinaBeijing,ChinaShanghaiLilly Asia VenturesShanghai,ChinaGT Healthcare Capital PartnersCentral,Hong Kong Island,Hong Kong5Y
109、 CapitalShanghai,ChinaTencentShenzhen,ChinaTemasekSingapore,Central RegionCormorant Asset ManagementBoston,Massachusetts,USHBM Healthcare Investments AGZug,SwitzerlandRocheBasel,SwitzerlandMarylandNorthpond VenturesMaryland,USMassChallengeBoston,Massachusetts,USTop-50 Investors in AI CompaniesINVEST
110、ORSAI FOR DRUG DISCOVERY COMPANIESHEADQUARTERS LOCATIONINVESTED INCasdin Capital19USAAbsci,Alector,Arzeda,Beacon Biosignals,Celsius Therapeutics,Clover Therapeutics,Exscientia,Gritstone Oncology,Fabric Genomics,Flatiron Health,Foundation Medicine,Lunit,Insitro,Paige,Recursion Pharmaceuticals,Relay T
111、herapeutics,Sema4,ShouTi,SomaLogic,Treeline BiosciencesCreative Destruction Lab(CDL)15CanadaBiotx.ai,DeepCure,DeepLife,Entropica Labs,Epistemic AI,Juvena Therapeutics,Kyndi,Kuano,Menten AI,NetraMark,OrganoTherapeutics,ProteinQure,Winterlight Labs,Valence Discovery SOSV14USAA2A Pharmaceuticals,Gateho
112、use Bio,Guided Clarity,Mendel.ai,Stelvio Therapeutics,Strados,SynthaceNational Science Foundation14USAbioSyntagma,ADM Diagnostic,Bioz,Cloud Pharmaceutical,Data2Doscovery,Strados Labs,VeriSIM Life,TeselaGen,GV13USADNAnexus,Flatiron Health,Foundation Medicine,IDEAYA Bioscience,insitro,Owkin,Schrdinger
113、,Relay Therapeutics,Ultromics,Celsius Therapeutics,Alector,Y Combinator12USAHistoWiz,iLab Service,Menten AI,Reverie Labs,Segmed,Arpeggio Bio,Athelas,Atomwise,CloudMedx,Coral GenomicsPerceptive Advisors11USAAbsci,Alector,Black Diamond Therapeutics,Champions Oncology,DNAnexus,Icosavax,IDEAYA Bioscienc
114、es,Neuron23,Saama,Sema4,Soma Logic,Relay TherapeuticsAlexandria Venture Investments11USAArrakis Therapeutics,Celsius Therapeutics,Deep Genomics,GNS Healthcare,Gritstone Oncology,IDEAYA Biosciences,Immunai,Insitro,Fountain Therapeutics,LEXEO Therapeutics,Neuromora Therapeutics,Veralox TherapeuticsSeq
115、uoia Capital China10 ChinaMETiS Therapeutics,PatSnap,Transcenta,XtalPi,Adagene,Athelas,Biofourmis,Deep Intelligent Pharma,HiFiBiO,Genuity BioRA Capital Management9USANimbus Therapeutics,Wave Life Sciences,Bristol Myers Squibb,Xbiome,Everest Medicines,Freenome,Frontier Medicines,Icosavax39Deep Pharma
116、 IntelligenceTop-50 Investors in AI Companies40Deep Pharma IntelligenceINVESTORSAI FOR DRUG DISCOVERY COMPANIESHEADQUARTERS LOCATION INVESTED INMerck Global Health9 USAOpGen,PathAI,PreciseDx,Strata Oncology,Verge Genomics,Absci,Antidote.me Alumni Ventures9USAEmerald Cloud Lab,Notable Labs,Olaris,Sci
117、pher Medicine,Strateos,Unlearn.AI,Veralox Therapeutics,Verge Genomics Khosla Ventures8USAArpeggio Bio,Atomwise,BIOAGE LABS,Fountain Therapeutics,Deep Genomics,Menten AI,Ochre Bio,Scipher Medicine,ThoughtSpot Foresite Capital8USAAetion,DNAnexus,Insitro,Relay Therapeutics,Wave Life Sciences 8VC8 USABi
118、gHat Biosciences,Coral Genomics,Immunai,Model Medicine,Notable,ProteinQure,Unlearn.AI DCVC Bio8 USAEmpirico,Frontier Medicines,Totus Medicines,Unlearn.AI,X-37 National Institute of Health8USAImaginostics,PostEra,Sangamo Therapeutics,SEngine Precision Medicine,Simulations Plus,Virvio,bioSyntagma,Cora
119、l GenomicsEASME-EU Executive Agency for SMEs8USAQuibim,Acellera,CellPly,Cytox,Genome Biologics,Genialis MassChallenge8USAScailyte,Simply Speak,Strados Labs,Vyasa Analytics,ChemAlive sA,Agamon,OrganoTherapeutics T.Rowe Price7USAArbor Biotechnologies,Generate Biomedicines,Genesis Therapeutics,Insitro,
120、Sema4,SomaLogic,TempusTop-50 Investors in AI Companies41Deep Pharma IntelligenceINVESTORSAI FOR DRUG DISCOVERY COMPANIESHEADQUARTERS LOCATION INVESTED IN SoftBank Vision Fund7UKBiofourmis,Datavant,Deep Genomics,Exscientia,Insitro,PatSnap,Relay Therapeutics,Roivant Sciences,XtalPi Invus7USAValo Healt
121、h,Black Diamond Therapeutics,Engine Biosciences,Erasca,ITeos Therapeutics,Neumora Therapeutics,Schrdinger Deerfield7USASema4,Strata Oncology,Alector,ConcertoCare,Foundation Medicine,Frontier Medicines,Insilico Medicine,Schrdinger F-Prime Capital7USABenchSci,Neumora Therapeutics,Notable,Owkin,Peptone
122、,Adagene Redmile Group7USAFoundation Medicine,Gritstone Oncology,Neuron23,Wave Life Sciences,Absci DCVC Bio7USAEmpirico,Frontier Medicines,Totus Medicines,Unlearn.AI,X-37 Founders Fund7USAAbCellera Biologics,Datavant,Emerald Cloud Lab,Notable Labs,Roivant Sciences,DeepMind IndieBio7USAGatehouse Bio,
123、Guided Clarity,Stelvio Therapeutics,A2A Pharmaceuticals Fidelity Management6USARoivant Sciences,Sema4,Wave Life Sciences,Absci,Deep Genomics,Generate Biomedicines,Surveyor Capital6USAShouTi,Arbor Biotechnologies,Icosavax,Neumora Therapeutics,Neuron23,Nimbus TherapeuticsTop-50 Investors in AI Compani
124、es42Deep Pharma IntelligenceINVESTORSAI FOR DRUG DISCOVERY COMPANIESHEADQUARTERS LOCATION INVESTED INTemasek Holding6SingaporeTranscenta,BenevolentAI,Genuity Science,Glympse Bio,InsitroCormorant Asset Management6 SwitzerlandStrata Oncology,Wave Life Sciences,Biomea Fusion,Erasca,Icosavax 5Y Capital6
125、ChinaXbiome,XtalPi,AliveX Biotech,Galixir,METiS TherapeuticsNorthpond Ventures6USADeep Lens,DNAnexus,Outcomes4Me,Scipher Medicine,Totus Medicines Obvious Ventures6 USALabGenius,Medable,Recursion Pharmaceuticals,ConcertoCare,Inato Andreessen Horowitz6USAAria Pharmaceuticals,Asimov,BigHat Biosciences,
126、BIOAGE LABS,Freenome Section 326USACharacter Biosciences,Glympse Bio,Nucleai,Verge Genomics,Alector Lux Capital6 USAAlife,Auransa,LabGenius,Recursion Pharmaceuticals,Strateos AME Cloud Ventures6 USAAsimov,Atomwise,Auransa,BigHat Biosciences,BIOAGE LABS Eight Roads Ventures6UKOwkin,ShouTi,WuXi AppTec
127、,AdageneTop-50 Investors in AI Companies43Deep Pharma IntelligenceINVESTORSAI FOR DRUG DISCOVERY COMPANIESHEADQUARTERS LOCATION INVESTED IN Lifeforce Capital6USAPostEra,TARA Biosystems,Verge Genomics,Character Bioscience Felicis Ventures6USAJuvena Therapeutics,LabGenius,ProteinQure,Spring Discovery
128、BlackRock5USAVerge Genomics,Cellarity,Exscientia,Insitro,Sema4 Foresite Capital5USAWave Life Sciences,Aetion,DNAnexus,Insitro,Relay Therapeutics Janus Henderson Investors5USAEverest Medicines,LEXEO Therapeutics,ShouTi,SomaLogicTencent5ChinaAtomwise,Brainomix,iCarbonX,PatSnap,XtalPi ARCH Venture Part
129、ners5 USAArbor Biotechnologies,Erasca,Generate Biomedicines,Glympse Bio Novo Holdings5DenmarkKebotix,Tempus,Evotec,Exscientia,Flagship Pioneering5USAValo Health,Cellarity,Generate Biomedicines,Biotechnology Value Fund5USAEvotec,Gritstone Oncology,IDEAYA Biosciences,Nimbus TherapeuticsBig Pharmas Foc
130、us on AIDEEP PHARMA INTELLIGENCEAI and Pharma Collaborations in Q2 2022-Q3 202245Feb 2022Mar 2022Amgen collaborated with Generate Biomedicines to create protein therapeutics for five clinical targets.Amgen will pay potentially up to$1.9 billion in this collaboration for a novel AI driven platform Ba
131、yer,Aalto and HUS expanded collaboration to apply artificial intelligence to support clinical drug trialsApr 2022Jun 2022Aqemia and Sanofi will work together on a number of initiatives in cancer,a major therapeutic area for Sanofi,to design and find new medicines.Takeda and Evozyne will create novel
132、 gene therapies for up to four rare disease targets.The deal worth up to$400 millionAug 2022Sep 2022The AI partnership between Bayer and Exscientia,which saw the two parties search for cardiovascular and cancer targets came to an end.Sanofi focuses on using Atomwises AtomNet platform to conduct smal
133、l molecule research on up to five therapeutic targets.Jan 2022May 2022Elix announced a research partnership with Shionogi on the validating retrosynthetic analysis utilizing data from Shionogi.AstraZeneca obtains a second pulmonary fibrosis target with a partnership with BenevolentAISelected Pharma
134、AI Deals AI Companies Pharma CorporationsAI Companies 4646Deep Pharma IntelligencePharma orporationsNote:the central column(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.Selected Pharma AI
135、 Deals 47Deep Pharma IntelligenceAI Companies Pharma CorporationsAI Companies Pharma orporationsNote:the central column(red)defines the pharmaceutical corporations and side columns(blue)defines AI companies that have collaborations with pharma companies from the central column.A Growing Number of Co
136、llaborations Involving AI for Drug DiscoverySummarizing industry observations over the last five years,we can observe a fundamental shift in perception of top executives at leading pharmaceutical organizations about the need of advanced AI technologies.Since 2015,there has been an obvious shift in t
137、he perception from skepticism and cuasious interest,all the way to a realization of a strategic role AI has to play in the emerging“data-centric”model of innovation.This change in perception was underpinned by a number of factors:a wave of proof-of-concept studies and research breakthroughs in a wid
138、e range of AI application use cases a number of commercial successes and successfully reached milestones,involving AI as a central element of research substantial advances in democratizing AI technology,where machine learning and deep learning algorithms become available at scale to non-AI experts d
139、ecent increase in the overall understanding of AI“mechanics”,due to increasing efforts in the education and professional development with a focus on AI-driven tools and approachesPharmaceutical companies of all sizes start competing for AI-expertise,talent,and partnerships.In this report we summariz
140、e some of the most high-profile such collaborations,involving top-20 pharma giants.Even though,we can see a clear uprising trend in the number of collaborations,focused on AI-drug design,and other aspects of data mining and analytics.Deep Pharma Intelligence48The rising interest of leading pharma an
141、d contract research organizations towards AI-driven biotech startups is a major driver for the area to become more attractive for investors,since the industry is becoming well-suited for successful exit strategies in future.Increasing number of partnerships between Pharma and AI Companies over the l
142、ast 6 yearsCorporation and AI-companies Participating in the Pharma AI DealsPharma PartnersAI and Biotech Partners49Deep Pharma IntelligenceTech PartnersThe leading Pharma players by the amount of major industry partnerships are AstraZeneca and Merck.These companies demonstrate increasing commitment
143、 to probing the grounds in the AI space by investing into internal programs,as well as partnering with external AI vendors to pilot programs in drug discovery and other research areas.The most common type of deals are true partnerships and saving the costs deals.The leading big pharma brands are inc
144、reasingly open to partnerships with AI startups and corporations to getcompetitive edge,and mitigate theproblem of declining R&D efficiency.50Deep Pharma IntelligenceLeading Pharma Corporations by the Number of Pharma AI Deals in Q3 202251Deep Pharma IntelligenceThe leading AI players by the amount
145、of major industry partnerships are Insilico Medicine,IKTOS and Atomwise.The biggest number of AI in Drug Discovery deals was conducted by Insilico Medicine.The company is an end-to-end,AI-driven pharma-technology company that accelerates drug development by proprietary platform across biology,chemis
146、try and clinical development.All of the deals concluded with this company were categorized as the ones aiming at saving costs and increasing operational efficiency due to thecharacter of the services provided.Top-10 AI and Tech Partners by Number of Major Pharma AI Deals in 2021-Q3 2022DEEP PHARMA I
147、NTELLIGENCEAI in Pharma Publicly Traded CompaniesAI in Pharma Publicly Traded CompaniesDespite the crisis and high volatility,AI-in-Pharma publicly traded companies present growth reaching$85,7B of cumulative capitalization as of October 3,2022.About 50 AI in Drug Development companies were taken fo
148、r this analysis,one of them Benelovent AI has closed its IPO in Q3 2022.The largest companies by market capitalization are Evotec,AbCellera and Relay Therapeutics.The smallest market capitalization are in Pharnext SA,Deepmatter Group and OpGen Inc.Its essential to measure the performance of publicly
149、 traded AI in Drug Development companies via comparison with major market benchmarks such as IBB,NBI and S&P 500.Because of the crisis,the cumulative market capitalization dynamics of AI in Pharma corporations are losing to YTD NASDAQ Biotechnology Index(NBI),iShares Biotechnology ETF(IBB),and S&P 5
150、00 gained solid.53Deep Pharma IntelligenceCumulative Capitalization of Publicly Traded AI-in-Drug Development Companies,Q2-Q3 2022,$BillionMarket Capitalization Growth During Q2-Q3 2022$110B$100B$900B$800B$700B1-June-20221-July-20221-August-20221-September-2022Top-10 AI-Driven Publicly Traded Pharma
151、 Companies by Market Capitalization in 202254Deep Pharma IntelligenceThe chart presents the Top-10 AI-driven drug discovery public companies arranged by market capitalization as of end of September 2022.AbCellera,British Columbia-based biotechnology firm that researches and develops human antibodies
152、 holds the first place with$2.8B of market capitalization.$3B$2B$1B$2.8B$2.7B$2.3B$2.3B$1.8B$1.8B$1B$1B$0.9B$0.8BAI in Pharma IPOs in Q2-Q3 202255Deep Pharma IntelligenceIn Q2 2022,BeneloventAI has successfully closed IPO.The IPO took place in the UK.The company has beta smaller than 1(although posi
153、tive),which means that AI in pharma stock prices move following the general market,yet the degree of such“movements”is lower.Major adverse market events in 2020-2022 did not significantly affect AI in pharma sector.The industrys features remain to play a designative role in the overall market volati
154、lity.Benevolents PlatformTM is a powerful computational R&D platform.Scientists may query the data and disease networks inside the graph using Benevolents range of exploratory and predictive AI tools.They can also ask biological queries,generate fresh insights,and prioritize ideas.In order to detect
155、 dysregulated pathways and processes and visualize the major distinctions between health and sickness,this enables researchers to target the most effective therapeutic approaches.The graph on the left depicts a comparative performance of BenevolentAI on Euronext Amsterdam starting 25.04.2022.TickerM
156、ean Daily ReturnVolatility of Daily ReturnsGrowth after IPOCapitalization,$MBAI-0.55%3.27%-30.81%779.9MBenevolentAI StockTop AI in Pharma Best-Promising Companies in Q2-Q3 2022Schrdinger,Recursion Pharmaceuticals and Relay Therapeutics constitute the group of promising companies selected for analysi
157、s.They are new to the market(their IPOs closed in 2020).Therefore,their future might change significantly.Moreover,they have decent multi-target pipelines of novel therapeutics to address unmet medical needs.The companies are expected to translate their proprietary insights and technical solutions i
158、nto effective therapeutics.Currently,the companies have a firm market position and thus receive high expectations from investors.NameCountryFunding Amount,$MIPO DateCapitalization,$BValuation at IPO,$MIPO Share Price,$Current Share Price,$EV/EBITDANet Income,$MSchrdingerUSA562.302.05.20202.2481917.0
159、031.45-15.74-134.800Recursion PharmaceuticalsUSA208.517.07.20201.5151355.219.008.81-4.60-211.74Relay TherapeuticsUSA520.016.07.20202.06173620.0018.95-4.80-383,73456Deep Pharma IntelligenceStock Prices,USDAI in Pharma Corporations Financials57Deep Pharma IntelligenceAI in Pharma corporations tend to
160、be more volatile than average publicly traded company.For most of the corporations,daily returns are positive and abnormal compared to the market.More volatile stocks are usually characterized by higher betas(both calculated for IBB index and for S&P 500).AI in Pharma segment is definitely a segment
161、 of growth stocks with the investors focused on the prospects of the companies rather than on the dividends.CompanyCapitalization,$MMean Daily ReturnVolatility of Daily ReturnsEstimated Monthly Return Actual Monthly ReturnIBB BetaS&P 500 BetaTotal Funding Amount,$MOperating MarginEV/EBITDANet Income
162、,$MGritstone Oncology247.564-0.09%5.87%8,78%24.54%0.519396-713.26%-0.36-111,921Lantern Pharma59.13-0.25%4.31%5.32%-7.05%1.081.3268.700.00%0.67-14.03Alector10780.24%4.18%5.77%-2.66%N/A1.34194.5011.95%-5.06-28.78Relay Therapeutics2144-0.06%5.27%5.67%-3.13%1.481.34520.00-10,056.81%-4.79-383,734Schrding
163、er2391-0.17%4.16%10.51%13.03%1.131.14567.20-79.25%-16.85-134,804Sensyne Health 790-0.83%15.44%2.75%135%1.590.8737.25-450.76%0.23-34,834Berkeley Lights356-0.61%6.63%-6.76%-9.52%1.59N/A272.60-88.44%-3.39-77,715LargeMediumLowAI in Pharma Corporations FinancialsDeep Pharma Intelligence58Market capitaliz
164、ation of some AI in Pharma corporations(such as Schrdinger)exceeds$6B whereas other companies are priced in the range of dozens of millions of dollars-the difference in the valuation is immense.There is no strong correlation between operating margin or net income and market capitalization-the valuat
165、ion of the corporations still being unprofitable can exceed billion of dollars.Selling shares to investors allows them to maintain their cash burn ratios on an acceptable levels.CompanyCapitalization,$MMean Daily ReturnVolatility of Daily ReturnsEstimated Monthly Return Actual Monthly ReturnIBB Beta
166、S&P 500 BetaTotal Funding Amount,$MOperating MarginEV/EBITDANet Income,$MBiodesix110-0.22%6.89%-10.85%82.91%N?A1.43289.70-162.47%-2.46-51,784C4X discovery78-0.01%3.18%12.75%28.91%0.140.188.71-120.92%-7.71-4,721DeepMatter Group4.63-0.72%7.47%-5.89%-11.54%1.220.37N/A-323.44%-1.54-3,026eTherapeutics108
167、-0.01%4.32%14.72%26.25%0.350.9798.50-2,006.29%-8.73-8,070GenFit231.140.16%4.93%14.68%31.92%1.320.8393.6937.71%0.3367,25Biomea Fusion347.090.13%6.54%-14.26%-2.13%N?A0.3256.000.00%-2.86-60,940LargeMediumLowAI in Pharma Corporations FinancialsDeep Pharma Intelligence59Market capitalization growth of AI
168、-driven Pharma corporations exceeds that of the entire market and general BioTech Industry indices represented as S&P 500 index and IBB,respectively.The difference is that compared to the general market,the AI-driven pharma market segment is more volatile.The distribution of the returns in the segme
169、nt of AI-driven pharma companies is right-skewed,which differentiates it from the vast majority of stock indices and segments.CompanyCapitalization,$MMean Daily ReturnVolatility of Daily ReturnsEstimated Monthly Return Actual Monthly ReturnIBB BetaS&P 500 BetaTotal Funding Amount,$MOperating MarginE
170、V/EBITDANet Income,$MBioXcel Therapeutics459.24-0.04%5.49%-5.89%8.13%1.181.03N/A0.00%-1.95-112,027Evolutionary Genomics4.63-0.06%4.51%6.44%0.00%-0.06-0.071.50.00%-4.81-3,090IDEAYA Biosciences608.192-0.10%3.92%2.48%8.68%1.361.47226.10-172.69%-6.49-56,839ITeos Therapeutics968.4840.12%4.04%8.42%29.24%1
171、.500.73249.7477.35%0.29297,637Recursion Pharmaceuticals1737-0.29%5.87%5.56%8.47%N?A1.22465.38-1,608.40%-5.74-211,741Sangamo Therapeutics814.076-0.19%4.08%7.98%2.14%1.401.1493.20-157.09%-2.62-176,330Renalytix AI98.31-0.79%5.63%1.66%5.580%1.691.0576.40-1,922.86%-0.37-46,2Evaxion Biotech73.408 0.00%8.0
172、5%12.58%33.18%N?A0.9617.000.00-1.89-26,230LargeMediumLowTop Publicly Traded Companies Related to AI-PharmaDEEP PHARMA INTELLIGENCECompanies Related to AI-PharmaDeep Pharma Intelligence61AI in pharma sector is an integral part of the contemporary pharmaceutical industry.AI-Pharma sector,defined broad
173、ly,is not limited to AI companies,but includes also pharma,tech,chemistry corporations,and CROs that are engaged in collaborations with AI startups,including but not limited to:Mergers&Acquisitions,scientific researches,partnerships,and so on.Hence the companies chosen are better to be described as
174、AI-related or AI-aiming than AI-based solely.The number of new partnerships between pharma companies and AI companies is ever increasing across the whole industry.On the one hand,AI-focused companies may spend a few years developing all software and tools which pharma companies do not have.On the ot
175、her hand,large companies,mainly public ones,have solid understanding of their science,extensive experience in the industry and regulatory field,and they are ready to share the risk.In this chapter we introduce the list of top corporations related to AI-Pharma that were selected based on the analysis
176、 of their R&D,financials,and collaborations with the most promising and advanced AI-Pharma startups.Big Pharma CompaniesAI CompaniesBiotechnology CompaniesData Integration CompaniesGenetics CompaniesAI in PharmaPublicly Traded Companies Related to AI-PharmaDriven to some extent by the COVID-19 pande
177、mic,publicly traded companies related to AI-Pharma demonstrated significant growth,reaching$14.13T industry capitalization as of the end of Q3 2022.Investors interest is being shifted towards industries of this nature.We see significant potential for Artificial Intelligence in the Pharmaceutical Ind
178、ustry.The Expected Compound Annual Growth Rate for this is market is projected to be around 40%over the next 3 years.The Biotechnology Industry is poised to witness a quantum leap soon,mainly because of the impact of Artificial Intelligence on biomedicine R&D.Many transactions are being announced,in
179、cluding Parexels acquisition for$8.5B,that indicates growing awareness of the disruptive potential in this sector for ones having the right means for participation.COVID-19 will continue to push valuations and M&A activity in the sector.Deep Pharma Intelligence62Cumulative Capitalization of Publicly
180、 Traded Companies Related to AI-Pharma,Q2-Q3 2022,$Billions$18T$16T$14T$12T1-June-20221-July-20221-August-20221-September-2022Top 10 Publicly Traded AI-Pharma Related Companies by Market Capitalization in 2022The chart represents the top-10 public companies that ended up in our portfolios according
181、to their market capitalization.Johnson and Johnson,NVIDIA and Eli Lilly top our list,accounting 50.5%of the capitalization of all companies included.During the last year and a half period of pandemic,AstraZeneca has being raised the capitalization by more than 10 times,reaching$172B.63Deep Pharma In
182、telligence$500B$400B$300B$200B$100B$429B$311B$307B$307B$266B$237B$227B$218B$205B$172BRoche Holding(RHHBY)Roche Holding AG offers pharmaceutical products for treating anemia,cancer,cardiovascular,central nervous system,dermatology,hepatitis B and C,HIV/AIDS,inflammatory,autoimmune and other diseases.
183、The company widely implements data-driven solutions,for example Roche has acquired Viewics,Inc.Viewics focuses on business analytics for laboratories,taking data from a variety of sources and extracting it to make faster data-driven decisions in operating processes in the labs.Novo Nordisk(NVO)Novo
184、Nordisk is a healthcare company,engages in the research,development,manufacture,and marketing of pharmaceutical products worldwide.It operates in two segments,Diabetes and Obesity care,and Biopharm.Novo Nordisk actively implements different AI in Pharma solutions,its foundation awards DKK 138 millio
185、n under its new data science and artificial intelligence initiative.Astrazeneca(AZN)Astrazeneca discovers,develops,manufactures,and commercializes prescription medicines in the areas of oncology,cardiovascular,renal and metabolism,respiratory,infection,neuroscience,and gastroenterology worldwide.Ast
186、razeneca uses advancing genomics research with AI and big data,AI is already being embedded across companies R&D both for research and experiment optimization.AbbVie(ABBV)AbbVie is one of the so-called Big Pharma companies.The company uses AI not only for direct development but also for its own enha
187、ncement:Abbelfish Machine Translation and AbbVie Search are built for accelerating and scaling the work of the company researchers,reducing the time it takes to discover and deliver transformative medicines and therapies for patients.Top Publicly Traded Companies Related to AI-Pharma64Deep Pharma In
188、telligenceBerkeley Lights(BLI)Berkeley Lights is a leading Digital Cell Biology company focused on enabling and accelerating the rapid development and commercialization of biotherapeutics and other cell-based products for the customers.Besides 2 unique optofluidics system,Berkeley Lights is known fo
189、r antibody discovery and cell lines development that definitely requires the usage of AI-powered algorithms and technical solutions.DeepMatter Group(DMTR)DeepMatter Group Plc operates as a big data and analysis company.It offers DigitalGlassware platform to deliver applications resulting in optimize
190、d chemicals,materials,and formulations in various areas,such as pharmaceutical research,fine chemicals,scientific publications,and teaching.The company develops and commercialises cheminformatics software to handle,store,and retrieve chemical structures and reactions for application in pharma;and to
191、ols for the production of synthesis planning and reaction prediction solutions,as well as engages in the automatic extraction of scientific information from text and images.Pharmaceutical Product Development(PPD)Pharmaceutical Product Development is another big CRO company.PPD ended up in our portfo
192、lio for a great reason,collaborating with Happy Life Tech for AI support,the company aims to create Data Science-driven Clinical Research Solutions in China to enhance global drug development.Charles River Laboratories(CRL)Charles River Laboratories is a well-known Contract Research Organization(CRO
193、)specializing in research and drug development.CRL uses the AtomNet platform,which is a deep convolutional neural network created for structure-based drug discovery.The company also works with the Valence Discovery Platform for Hit-to-Lead acceleration and optimization and provides all research serv
194、ices considering these platforms.Top Publicly Traded Companies Related to AI-Pharma65Deep Pharma IntelligenceAgilent(A)Agilent is one of the biggest Biotech companies providing technical solutions for the Pharmaceutical industry.Lots of company technical solutions already have built-in or support di
195、fferent type of AI algorithms.Also,Agilent and Visiopharm co-promote advanced digital Precision Pathology Solutions.Thermo Fisher Scientific(TMO)Thermo Fisher is another,even bigger,Biotech company that is specializing in technical solutions,providing also a wide range of other services.“The connect
196、ed Lab”is a good example of AI-enhanced services providing by the company,creating solutions for enhanced in-Lab performance via AI-based info-gathering and analysis.AI-based processing tools are now also available in Thermo Scientific Amira-Avizo Software and PerGeos Software.Johnson and Johnson(JN
197、J)Johnson and Johnson is considered o be among the TOP-3 biggest Pharmaceutical companies in the world,therefore not only implementation but also investing in AI in Pharma is provided by the company.In 2020,J&J announced an investment in Datavant Holdings,which is working to help healthcare organiza
198、tions unite data across institutions to enhance medical research and patient care.Another JJI partner,Aetion Inc.,analyzes electronic medical records,insurance claims,patient registries and lab results to generate healthcare-related decisions.Almirall(ALM)Almirall is a leading skin-health focused gl
199、obal pharmaceutical company,that has some recent collaborations with Iktos for the creation of generative modelling AI technology for quick identification of molecules with multiple bioactivity and drug-like criteria.Top Publicly Traded Companies Related to AI-Pharma66Deep Pharma IntelligenceAI for
200、Advanced R&D:Applications and Use CasesDEEP PHARMA INTELLIGENCENotable AI Breakthroughs68Deep Pharma IntelligenceIBM Watson released a cognitive computing platform for Clinical trial matching that has shown significant improvement in patient enrollment rate at Mayo Clinic.The platform demonstrated a
201、n 80%increase in enrollment in clinical trials for breast cancer and a decrease in time to match a clinical trial to one patient.Healx has prepared a rare disease Fragile X syndrome drug for a Phase 2a clinical trial in 15 months.Healx has demonstrated the power of combining domain expertise,deep le
202、arning,and proprietary data.DeepMind built the AlphaFold platform to predict 3D protein structures that outperformed all other algorithms.AlphaFold won the CASP13 competition,where it could most accurately predict the shape for 25 of the 43 proteins without using previously solved proteins as templa
203、tes.Recursion Pharmaceuticals has evaluated Takedas preclinical and clinical molecules in over 60 indications in less than 18 months by Recursions AI-enabled drug discovery platform.Insilico Medicine has published a research paper about the first in vivo active drug candidate developed from scratch(
204、de-novo)in 46 days(with target selection)using the GENTRL AI-based system.Oct 2018Mar 2018Dec 2018Sep 2019Jan 2019Notable AI Breakthroughs69Deep Pharma IntelligenceDeep Genomics created a DG12P1 drug in 18 months using an AI-augmented drug design.It is an antisense oligonucleotide therapy to treat r
205、are Wilson disease.Deed Genomics platform screened over 2,400 diseases and over 100,000 mutations to predict and confirm the precise disease-causing mechanism of the mutation Met645Arg.Mendel Recruit proprietary platform increases patient enrollment for clinical trials by 24-50%.It applies AI algori
206、thms that combine the recognition of scanned documents with natural language processing of clinical records and automated clinical reasoning.A new drug candidate,DSP-1181,created using the Exscientia Centaur Chemist Artificial Intelligence platform,began clinical study.The drug was developed togethe
207、r with Sumitomo Dainippon Pharma for the treatment of an obsessive-compulsive disorder.It was advanced to Phase 1 clinical trials.Scientists from MIT discovered halicin a new super powerful antibiotic capable of killing 35 of the worlds most problematic disease-causing bacteria,including multiresist
208、ant strains.The model applied was able to screen more than a hundred million chemical compounds and pick out potential antibiotics that kill bacteria using different mechanisms than existing drugs.Aladdin has built a platform for the early diagnostics of Alzheimers disease and COVID-19.Disease Diagn
209、osis platform uses AI and multimodal data,including biomarkers,imaging,blood samples,medical records,etc.Jan 2020Sep 2020Sep 2019Feb 2020Jan 2020Notable AI Breakthroughs70Deep Pharma IntelligenceMELLODDY the Machine Learning Ledger Orchestration for Drug Discovery group was created by ten pharma com
210、panies to develop ML models without sharing data.MELLODDY leverages the worlds most extensive collection of small molecules with known biochemical or cellular activity to provide more accurate predictive models and improve drug discovery efficiency.Insilico Medicine achieved industry-first nominatin
211、g Preclinical Candidate.The company performed all the required human patient cell,tissue,and animal validation experiments to claim a first-in-class preclinical candidate for a novel pan-fibrotic target.The company is preparing for clinical development.Cyclica launched an AI-based drug discovery pla
212、tform that achieved over 60%of actionable hits for its pharma clients.Cyclica has partnered with over 100 global pharma and biotech companies and academia across many therapy areas.These partnerships have resulted in 64%of programmes resulting in actionable hits.BioXcel Therapeutics,Inc.,a clinical-
213、stage biopharmaceutical company utilizing AI approaches,announced that the FDA has accepted for filing the New Drug Application for BXCL501,for the acute treatment of agitation associated with schizophrenia and bipolar disorders I and II.Using its AI technology,Exscientia designed an Alzheimers dise
214、ase drug candidate who has entered Phase I clinical testing.The AI-designed drug candidate will be assessed for improved antipsychotic effects associated with Alzheimers psychosis,in addition to improvements in behavioural and psychological symptoms of dementiaSep 2020Mar 2021May 2021Feb 2021May 202
215、1Notable AI Breakthroughs71Deep Pharma IntelligenceThe University of Washington has developed a deep learning model,“RoseTTAFold”,that calculates protein structure on a single gaming computer within ten minutes.Insilico Medicine announces the preclinical candidate for kidney fibrosis discovered usin
216、g end-to-end Artificial Intelligence engine.The preclinical candidate has the desirable pharmacological properties,pharmacokinetic profile and demonstrated auspicious results in in-vitro and in-vivo preclinical studies.Exscientia,in cooperation with the Medical University of Vienna,published a paper
217、 that illustrates the potential real-world impact of using Exscientias AI-supported precision medicine platform.The platform proposes the most effective therapy for late-stage haematological cancer patients based on testing drug responses ex vivo in their own tissue samples.AstraZeneca,Merck,Pfizer
218、and Teva formed AION Labs,the innovative lab that will create and adopt AI technology to transform the process of drug discovery.AION Labs will create and invest in companies that implement AI for drug development.Additionally,they will offer special resources and mentorships to such companies.The A
219、I-empowered company Healx has secured FDA approval for a phase 2a clinical trial of an AI-discovered compound that could help manage the symptoms of the genetic disorder Fragile X syndrome.Jul 2021Oct 2021Oct 2021Oct 2021Aug 2021Standigm had established a Synthetic Research Center in the headquarter
220、s of SK Chemicals Co.,Ltd(SK Chemicals,KRX 285130),a life science and green chemicals company.Notable AI Breakthroughs72Deep Pharma IntelligenceInsilico Medicine,an end-to-end artificial intelligence(AI)-driven drug discovery company,announced that the first healthy volunteer has been dosed in a fir
221、st-in-human microdose trial of ISM001-055.BenevolentAI,a leading clinical-stage AI drug discovery company,announced that AstraZeneca had added a novel target for idiopathic pulmonary fibrosis(IPF),discovered using BenevolentAIs platform,to its drug development portfolio.This is the second novel targ
222、et from the collaboration that has been identified,validated,and selected for AstraZenecas portfolio.Lantern Pharma presented positive data on the effectiveness of LP-284 in hematologic cancers at the 63rd American Society of Hematology(ASH)Annual Meeting.Erasca announced the FDA has cleared an inve
223、stigational new drug application for ERAS-801,an orally available small molecule epidermal growth factor receptor inhibitor specifically designed to have high central nervous system penetration for the treatment of recurrent glioblastoma multiforme.Dec 2021Dec 2021Dec 2021Nov 2021Nov 2021Notable AI
224、Breakthroughs73Deep Pharma IntelligenceAbCellera and its collaborators released new preclinical data showing the pseudovirus neutralization status of its two monoclonal antibodies,bamlanivimab and bebtelovimab(also known as LY-CoV1404),against the Omicron variant.Bristol Myers Squibb announced the C
225、MPH of the EMA has recommended approval of Breyanzi,a CD19-directed chimeric antigen receptor T cell therapy for the treatment of adult patients with relapsed or refractory(R/R)diffuse large B-cell lymphoma(DLBCL),primary mediastinal large B-cell lymphoma(PMBCL),and follicular lymphoma grade 3B(FL3B
226、)after two or more lines of systemic therapy.AI Therapeutics announced the initiation of a Phase II study for a promising new approach to treat amyotrophic lateral sclerosis(ALS).Aizon announced the launch of its new asset monitoring application for pharmaceutical manufacturers and biotech companies
227、.Built on Aizons GxP compliant AI SaaS Platform,Aizon Asset Health provides intelligent historical maintenance analysis,proactively monitors the condition of critical assets in real time,and provides actionable maintenance recommendations that keep equipment up and running optimally.Cyclica launched
228、 Perturba Therapeutics-a spin out from the Stagljar Lab at the University of Toronto,Donnelly Centre for Cellular and Biomolecular Research.Perturba is advancing a rich pipeline of assets from undrugged protein-protein interactions.Feb 2022Feb 2022Feb 2022Jan 2022Jan 2022Notable AI Breakthroughs74De
229、ep Pharma IntelligenceThe US FDA has officially approved Niramai Health Analytixs first product,which is used to provide an innovative radiation-free,non-touch,accurate breast cancer screening solution.A breast thermography tool aids medical professionals in reviewing,measuring,and analyzing thermal
230、ly relevant indications in the breast regionThe purchase of TARA Biosystems,a biotech business focused on cardiovascular illness,by Valo Health has created the first vertically integrated platform for the development of cardiovascular drugs.The combination of TARAs unique human 3D tissue engineering
231、 technology and Valos Opal Computational PlatformTM allows Valo to revolutionize the research and development of drugs for cardiovascular diseases.The FDA has given Breakthrough Device Designation to Anumana,Inc.,an AI-driven health technology firm from nference,Inc.,for its AI-enhanced,ECG-based Pu
232、lmonary Hypertension(PH)Early Detection Algorithm.The algorithm is a precise,screening tool for earlier diagnosis of patients with pulmonary hypertension.Aizon wins the 2022 Artificial Intelligence Breakthrough Awards Programs Best AI-based Solution for Manufacturing Award.The FDAs gave Biogen and E
233、isais follow-up to the Alzheimers disease medication Aduhelm priority review status.The businesses are aiming for a quick assessment of their anti-amyloid medication lecanemab,which can replace the contentious Aduhelm.May 2022Jul 2022Jun 2022Apr 2022Mar 2022DEEP PHARMA INTELLIGENCEComputational Meth
234、ods Used by the Most Advanced AI CompaniesComputational Methods Used by the Most Advanced AI Companies76Deep Pharma IntelligenceNatural Language ProcessingComputational MethodsMachine LearningDeep LearningChemoinformaticsBioinformaticsSymbolic AIReinforcement LearningGANsQuantum ComputingCNNEvolutio
235、nary AlgorithmsFederated LearningCompanyComputational methods usedTechnology AbstractBioinformatics,Deep Learning,NLPArdigen is active in the field of laboratory information management systems,biological and clinical data analysis,Big Data integration,as well as custom application development.Machin
236、e Learning,Deep Learning(Convolutional neural networks),chemoinformaticsAtomNet is the first drug discovery algorithm to use a deep convolutional neural network.It has already explored questions in cancer,neurological diseases,antivirals,antiparasitics,and antibiotics.NLP,Deep Learning,Machine Learn
237、ingDecodes open-and closed-access data on reagents such as antibodies and present published figures with actionable insights.Machine Learning,Deep Learning,symbolic AI,chemoinformaticsEvolved from text mining and semantic linking into knowledge graphs to tackle complex multifactorial diseases,identi
238、fy novel targets,small molecule drug discovery and patient stratification.Machine Learning,Deep Learning,bioinformaticsAnalyze data from patient samples in both healthy and diseased states to generate novel biomarkers and therapeutic targets.Machine Learning,bioinformaticsAutomate selection,manipula
239、tion,and analysis of cells.Allows researchers to:Expedite development of cell lines and automate manufacturing of cellular therapeutics.Computational Methods Used by the Most Advanced AI Companies77Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractNLP,Deep Learning,Machine
240、LearningProcess raw phenotypic,imaging,drug,and genomic data sets.Allows researchers to integrate rapid analytics and machine learning capabilities into existing business processes.NLP,Deep Learning,Machine LearningBioz has developed a search engine for Life Sciences community using natural language
241、 processing and machine learning technology to scan hundreds of millions of pages of complex and unstructured scientific papers on the web.Machine Learning,Deep Learning,chemoinformaticsBioxcel Corporation is a biopharmaceutical company pioneering the application of artificial intelligence and big d
242、ata analytics integrated with drug development expertise.Machine Learning,Deep Learning,chemoinformatics,bioinformaticsC4X innovative DNA-based target identification platform(Taxonomy3(R)utilises human genetic datasets to identify novel patient-specific targets.Deep Learning,BioinformaticsIt is a de
243、ep learning company that uses innovative,computer-based methods to degrade undruggable targets and validate lead drug candidates in automated labMachine Learning,Deep Learning,symbolic AI,chemoinformatics,bioinformaticsCytoReasons access to unmatched proprietary and public data,combined with cutting
244、-edge machine learning technologies,creates their unique biological models of disease,tissue and drug.Computational Methods Used by the Most Advanced AI Companies78Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractMachine Learning,Deep Learning,NLPThe Data4Cure platforms mo
245、dular architecture allows independent system components to handle integration and advanced analysis of heterogeneous data types spanning molecular,phenotypic and clinical data,both structured and unstructured.Machine Learning,Deep Learning,bioinformaticsDeep Genomics is using artificial intelligence
246、 to build a new universe of life-saving genetic therapies.Bioinformatics,Machine LearningDesktop Genetics is team of genome editing experts,bioinformaticians and data scientists,driven by the real-world impact of CRISPR technology.Their core technology,DESKGEN AI,was trained on the largest database
247、of genome editing data in the world.Machine Learning,Deep Learning,high-performance computingEnvisagenics SpliceCore platform integrates proprietary machine learning algorithms,high performance computing,and RNA-splicing analytics to identify disease-specific alternatively spliced RNA that will func
248、tion as therapeutic targets.Machine Learning,Deep Learning,bioinformaticsEuretos provides direct access to the cloud based discovery platform via user friendly application and also allows integration of company proprietary data and public data in a secure environment.Machine Learning,Deep Learning,b
249、ioinformatics,chemoinformaticsThe company uses ML for predicting ADME,novelty,synthetic accessibility,pharmacology of molecules.Computational Methods Used by the Most Advanced AI Companies79Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractMachine Learning,Deep LearningBlen
250、ding computational biology and AI-based methods,Genialis merges and models data at the intersection of clinical and translational medicine.Machine Learning,Deep LearningGNS Healthcare AI technology integrates and transforms a wide variety of patient data types into in silico patients which reveal th
251、e complex system of interactions underlying disease progression and drug response.Machine Learning,NLP,symbolic AI,chemoinformatics,bioinformaticsHealx AI platform uses natural language processing to extract disease knowledge from published sources and to complement biomedical databases and propriet
252、ary,curated data.Machine Learning,Deep Learning,cheminformaticsIktos has invented and is developing a technology based on DL for ligand-based de novo drug design,focusing on multi parametric optimization(MPO)Deep Learning,GANs,GANs+Reinforcement Learning,symbolic AI,Machine Learning,chemoinformatics
253、,bioinformaticsComprehensive DL pipeline.Biology:Signaling pathways,DNNs for target ID and HTS analysis.Chemistry:GANs-RL for novel molecule generation.NLP,Deep Learning,Machine LearningKyndi provides leading artificial intelligence software that can analyze long-form text and find actionable insigh
254、ts in a smarter,faster,and more explainable way.Computational Methods Used by the Most Advanced AI Companies80Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractMachine Learning,chemoinformaticsWith a huge experience in Lead Generation,Lead Optimisation and method developmen
255、t the goal of the company is to accelerate the progress of our clients programmes.NLP,Deep LearningnferX uses state-of-the-art Neural Networks for real-time,automated extraction of knowledge from the commercial,scientific,and regulatory body of literature.Big data analytics;Deep Learning,Machine Lea
256、rningDiscover connections between drugs and diseases at a systems level by analyzing of millions of raw human,biological,pharmacological,and clinical data points.Deep Learning,BioinformaticsPredict the therapeutic potential of food-derived bioactive peptides.Allows researchers to:cost-effectively de
257、velop highly targeted treatments for specific diseases from natural food sources.Machine Learning,Federated LearningOwkin combines the expertise in biology and machine learning to fuel precision medicine.Owkin facilitates access to real-world data by therapeutic area through its data connect service
258、.Deep Learning(TensorFlow+Keras base)Worlds first protein database specifically for Deep Learning and AI applications with full Keras and Tensorflow integration.Computational Methods Used by the Most Advanced AI Companies81Deep Pharma IntelligenceCompanyComputational methods usedTechnology AbstractD
259、eep Learning,Reinforcement LearningPhenomic predicts which cells will survive chemotherapy and identifies compounds that selectively target these resistant cells.It will then develop the compounds and bring them to market.Quantum Computing,Reinforcement Learning,ChemoinformaticsProteinQure is combin
260、ing quantum computing,reinforcement learning,and atomistic simulations to design protein drugs.They can design peptide-based therapeutics and explore protein structures without crystal structures.Evolutionary algorithms,Machine LearningML-based structure based predictive models for potency and ADMET
261、/PK properties of small molecules.Machine Learning,Deep LearningReviveMeds platform enables the rapid,high-throughput,and cost-effective application of metabolic data to discover new disease mechanisms for drug discovery and,simultaneously metabolomic biomarkers to identify which patients stand to b
262、enefit by targeting the disease mechanism.Machine Learning(stochastic gradient descent and branch-and-bound maximum likelihood optimization)The cryoSPARC System enables high-throughput structure discovery of proteins and molecular complexes from cryo-EM data with help of machine learning.Quantum phy
263、sics;Machine LearningXtalPis ID4 platform provides accurate predictions on the physiochemical and pharmaceutical properties of small-molecule candidates for drug design,solid-form selection,and other critical aspects of drug development.Computational Methods Used by the Most Advanced AI Companies82D
264、eep Pharma IntelligenceDEEP PHARMA INTELLIGENCE15 Notable R&D Use Cases of AI Application in BiopharmaPharmaChemical synthesisSmall drugs molecules 84Deep Pharma Intelligence2 Key AdvantagesAIMachine LearningDeep LearningCognitive Reasoning TechnologiesNatural Language ProcessingBiopharma utilizes l
265、iving organisms(such as yeasts,bacterias,and mammalian cells)which are capable to produce complexly structured products such as proteins,hormones,RNA and DNA products,and virus capsids.Whereas Pharma relies on a classical chemical synthesis producing small drug molecules.However,both industries may
266、benefit from AI-driven applications.To develop new small drug molecules or biologically-derived products,AI-driven data processing serves as a tool that allows minimising time consuming biological testings while helping to select the most promising products to test.BiopharmaLiving organisms(yeasts,b
267、acterias,mammalian cells)Complex structures(proteins,DNA,RNA,hormones,viruses capsids)Introduction to Most Innovative R&D Approaches of AI in Biopharma Most Innovative R&D Approaches of AI in Biopharma.Strados Labs85Deep Pharma IntelligenceStrados Labs enters the Pharma and Life Science market with
268、a Respiratory Management Solution that includes the only FDA-cleared,RESP biosensor which acquires lung sound acoustics wireless and hands-free,making it a perfect fit for clinical research to measure patient response to new drugs by objectively collecting coughs and other lung sounds discreetly,com
269、fortably,and securely in a streamlined way,while having access to data for post-processing and analysis.Cough Trial SolutionTrial Partner SolutionDecentralized Trial CompatibilityDigital Acoustics Biomarkers*Listed companies are industry leaders and prospects for StradoslabsHow Strados Labs Uses AI
270、in R&D?220 hours of continuous data collection without patient intervention of objective lung sounds and respiratory dynamics while having access to data for post-processing and analysis.Noise cancellation is applied to enhance the signal to noise ratio and eliminate speech discernibility while bein
271、g HIPAA compliant with an end to end encryption.Data collected via RESP is uploaded automatically to the Strados Cloud to allow assessment of recordings timely with identification of adventitious breath sounds including respiratory dynamics with ML algorithms.Wireless,non invasive biosensor that mon
272、itors,records and stores every lung sound.That translates into longer wear times and an astounding 99.59%patient compliance.Identification of wheeze,cough,and CABS detection gives the objective measurement of these changes over time on a patient and population basis with an ability to differentiate
273、cough types in addition to frequency.Strados Labs a respiratory management solution,which brings innovation at the intersection of lung biomarkers,patient centricity,and machine learning.The industry of life sciences can largely benefit from the enhancement of pulmonary care monitoring capabilities
274、provided by Strados Labs to gain insight into patient drug response by analysis of longitudinal lung acoustics.Data Collection Capacity Patient Privacy&SecurityReal-Time Data AnalysisPatient CentricityLongitudinal Lung Data86Deep Pharma IntelligenceHow Strados Labs Uses AI in R&D?Today Strados Labs
275、has a unique opportunity to stand as a leader in Respiratory Health:their clinically validated bioacoustic library of sounds and AI engine is the worlds largest entirely hands-free,clinical-grade dataset enabling Strados Labs to be the standard bearer of acoustic digital biomarkers for clinical rese
276、arch and respiratory care globally.The Strados Respiratory Management Solution is the worlds first FDA-cleared lung sound platform with a proprietary wireless biosensor,RESP,that is passive,patient-friendly,and clinically validated to acquire lung sounds in the real world.For instance,Strados Labs R
277、ESP fits perfectly into decentralized trials allowing remote patient access by unlocking lung sound data and putting it into the hands of the entire research team via the cloud.Making decentralized respiratory trials scalable and able to develop entirely new insights about respiratory status without
278、 episodic patient interaction.Strados Cloud:companys passive and longitudinal bioacoustics insights allow them to build a more complete picture of the subjects respiratory status leading to better trial outcomes.87Deep Pharma IntelligencePatientStrados CloudStrados Clinical PortalStrados AISolutions
279、How Standigm Accelerates Drug Discovery using AINovel TargetsIdentification First-in-ClassLead GenerationCataloged AssetsTailored Partnership ModelsStandigms AI solution Standigm ASKTM provides novel targets perfectly fit to a customers research context within two weeks.Standigms optimized workflow
280、AI system can generate multiple First-in-Class compounds within seven months.Standigm has an exceptional reservoir of ready-made in-house therapeutic assets,which are as attractive as to meet customers pipeline needs.Therapeutic areas of assets:Standigm has tailored partnership models perfectly fit
281、to a customers needs,from licensing of AI platform and assets to AI solution providing.Standigms partnership models:Licensing of Standigms AI Platform(Standigm ASKTM,Standigm BESTTM)Licensing of First-in-class assets driven by Standigms AI platformProviding Standigms AI solution88Deep Pharma Intelli
282、genceCancerNASHParkinsons DiseaseMitochondrial DiseaseIntegrated workflow AINovel target Identification(Stangdim ASKTM)Novel compound design(Stangdim BESTTM)Novel target IdentificationQuery:DiseaseStangdim ASKTMOutput:Novel Target123Standigm is a workflow AI-driven drug discovery company headquarter
283、ed in Seoul,South Korea and subsidiarized in Cambridge,UK.Standigm has proprietary AI platforms encompassing novel target identification to compound design,to generate commercially valuable drug pipelines.The company has established an early-stage drug discovery workflow AI to generate First-in-Clas
284、s lead compounds within seven months.o date,Standigm is running 42 in-house or collaborative pipelines for drug discovery using the workflow AI technology.One of the companys pipelines is expected to enter a pre-clinical stage in 4Q 2021.How Standigm Accelerates Drug Discovery using AIStandigm ASKTM
285、 is a customizable,AI-aided drug target identification platform,prioritizing disease-target relationships and providing evidence-based results through an interactive user interface.Standigm BESTTM is a novel compound generation platform,which can investigate lead compounds whenever target or ligand
286、information is lacking or enough.89Deep Pharma IntelligenceDatabaseHit IDHit to LeadLead OptimizationGraph DBPrioritization AlgorithmMulti FiltersNovel Target SelectionHow Standigm Accelerates Drug Discovery using AITarget Proposal(Patent Analysis)Purchasing Compound,Assays,Hit ScreenStandigm Releas
287、es First-in-Class Compounds within 7 MonthsFeatured Partners90Deep Pharma IntelligenceStandardized workflowNovel target Identification(Stangdim ASK)Novel compoundDesign(Stangdim BEST)7 months(rather than years/Standigm)30 months(Traditional Approach)Synthesis(Only top 30 compounds)In vitro assay(pot
288、ency,microsomal stability,hERG)First-in-classcompounds1MCollaborator(Pharma Company):3M(Hit Compounds)Stangdim BESTTMData Collection,Analysis*and Model Validation*3M(Hit Compounds)3M(Hit Compounds)Preclinical CandidateStandigm made the hit-to-lead stage with a cancer Target A within 7 Months*Data An
289、alysis Binding site analysis using protein structure*Model Validation Validation of activity prediction models:ChemMap-based,2D structure QSAR-based,Simulation-based and Ensemble-based methods1M1st Round Commercial Compounds(Library of x million)1M2nd Round Commercial Compounds&Hit-Based Design1MSAR
290、-Based Novel DesignChemical Synthesis,Assays,Hit SelectionChemical Synthesis,Assays,(In Vitro&In Vivo),Lead SelectionLead OptimizationIn Vivo&Tox3M(Hit Compounds)Most Innovative R&D Approaches of AI in Biopharma.AntiverseAntiverse is a new type of antibody discovery company accelerating drug develop
291、ment.The Antiverse platform exists at the intersection of structural biology,machine learning and medicine to enable breakthroughs to happen more quickly and cost-effectively.Library DiversityExploring the Full Functional Spacing Antiverse prevents diversity loss during amplification to uncover more
292、 diverse and rare antibodies.Antiverse provides more candidates by analysing NGS data,clustering on multi-dimensional space,and selecting based on sequential and structural grouping.The generative module offers new sequences and gives you options that havent even been considered.Traditional in vitro
293、 screening:Antiverse discovery:1010 antibodies3 amplification rounds10 antibodiesAntigen-antibody database96 antibodiesAI-augmented screeningAntiverse AI-Augmented Discovery:Recovery ModuleGenerative Module96 antibodies91Deep Pharma IntelligenceAntigen-antibody databaseAntiverse is recognized as one
294、 of the top biotech startups in the UK with our antibody discovery service already in use by big pharma.The main feature of the company is 10 x Diversity with AI-Augmented Drug Discovery.How Antiverse Engineers the Future of Drug DiscoveryExisting antibody discovery methods are well-developed and of
295、ten effective at discovering binders.But when there is a need to find the best possible candidate,or when finding a suitable candidate is hard with current methods,the options are limited and often costly.Antiverse uses next-generation sequencing(NGS)to extract more data from existing workloads.The
296、AI-Augmented Drug Discovery platform and trained models analyse the statistics gained from thousands of experiments.These outputs are compared against known data in order to select best candidates.Binder CustomisationAntiverse can generate new binder variants that will be sufficient for clients purp
297、oses.Target SelectionAntiverse provides targeted options in order to focus on testing safely once there are too many antibody-antigen binding options.Binder Recovery Antiverse can help to find sufficient potential binders that can be missed by conventional methods.92Deep Pharma IntelligenceThe Antiv
298、erse AI-ADD system found each and every cluster identified by other methods,plus more.These additional clusters contained rare and unique sequences.How Antiverse Uses AI in R&DBindersNGSDataAntiverse sends the sequences to Customer.Customer tests scFvs recovered/generated using either two-step linke
299、r PCR*or synthesise93Deep Pharma IntelligenceNGS FacilityThe Drug Discovery Ecosystem is Evolving Rapidly-And Data is at the Core.Arctoris is one of them:a biotech platform company with operations in Oxford,Boston,and Singapore,leveraging its fully automated platform for drug discovery.LIFE SCIENCE
300、AUTOMATIONLIFE SCIENCE DATA MININGContract research organizationsSynthetic biologyData-powered drug discoveryLarge pharma/traditional biotechCloud laboratoriesAI drug discoveryDrug discovery is undergoing massive and rapid change-the rise of Artificial Intelligence and Machine Learning for Drug Disc
301、overy and the evolution of robotics-centric companies in the biomedical research space has enabled a new generation of companies to emerge:data-powered drug discovery companies that combine automation and data science.94Deep Pharma IntelligenceThe company was founded by an oncologist and a medicinal
302、/synthetic chemist,with the goal to accelerate the discovery and development of new therapies by harnessing the power of technology and combining it with deep industry expertise.The core thesis of the company is that better data leads to better decisions,and that in order for drug discovery programs
303、 to develop and meet the next milestone faster and with higher chance of success,the underlying data must be rich,reliable,and reproducible.According to Arctoris,the status quo is no longer enough:in order to develop the best drugs,industry leaders have to rethink how they can improve their decision
304、-making,powered by better data.Having developed a suite of proprietary technologies across robotics and data science/AI/ML,Arctoris is a leader in this new and rapidly evolving field.How Do Robotics and AI/ML Synergize in Drug Discovery?Both quality and speed are achieved by combining precision robo
305、tics with a unique data science platform and world-class drug discovery expertise from biotech and pharma veterans.Arctoris tracks all experimental outputs in full depth,including the capture and analysis of extensive metadata temperature,humidity,CO2,reagent provenance and batch ID among many other
306、s.At the same time,the platform enables automated QA/QC processing,applying statistical tools to ensure full reliability and validity of all results.Thereby,Arctoris ensures superior data to be generated in accelerated timeframes,leading to better decisions taken earlier-in human-powered but especia
307、lly in AI/ML-driven programs,thanks to training of AI models with the best possible data.Taken together,Arctoris has developed a unique technology platform based on robotics and data science that powers drug discovery programs both in the companys internal pipeline and in partnerships with biotech a
308、nd pharma companies worldwide.95Deep Pharma IntelligenceINDUSTRY-STANDARD DATA GENERATION&PROCESSINGARCTORIS-ENABLED DATA GENERATION&PROCESSING Widespread lack of reproducibility Unclear reagent and cell line provenance Inconsistent use of methods&protocols Human error&variability Only collection of
309、 high-level results data Highly fragmented file&storage systems Strict adherence to automated protocols Fully verified reagents and cell lines with complete audit trails Reproducible results data in standardized structure Additional collection of rich research meta-data Secure&convenient data storag
310、e&access Advanced assay performance monitoringThe greatest challenge in AI-driven and ML-powered drug discovery is access to well structured,fully annotated,reproducible and robust data.Arctoris leverages the power of robotics to generate vast amounts of ML-ready data that enable better decisions-th
311、ereby significantly accelerating timelines from target to hit,lead,and candidate.The Arctoris Platform:Leveraging Robotics&Data Science from Target to Candidate.Target ValidationHit finding&Hit-to-LeadLead OptimizationAnalysis of target expression and target half-live by quantifying protein turnover
312、 and route to degradationInvestigation of target function(changes in phenotype,pathways,gene expression,etc.)via cell-based and molecular biology readoutsAdvanced insights into effects of target modulation by employing complex model systems such as organoids,primary cells,etc.Machine-learning guided
313、 screening set selection and hit evolutionIn silico and in vitro screening and profilingBiophysical screening/profiling and FBDDRapid synthetic hit expansion and diversification incl.use of CADDKinetic and mechanistic biochemistry/enzymology and biophysical quantitation of target engagement energeti
314、cs&kinetics Protein science and(co)crystallography for SBDDRapid biochemical profiling,kinetics,selectivity,mechanism of actionIsolated and in-cell target engagementCellular mode of action,elucidation of pathway modulation,confirmation of on-target/off-target effectMedicinal and synthetic chemistry(
315、optimizing SAR,SPR,STR)Integration of synthetic and computational chemistry as well as in vivo ADMET for late-stage lead optimization Pharmacokinetics and pharmaco-dynamics(PK/PD)&safety pharma-cologyIn-depth pharmacokinetics,including ADME,drug-drug interactions,metabolite profiling,concentration t
316、ime profilesComprehensive acute toxicology assess-ment,incl.single dose and repeated dose to determine MTD and NOAELAdditional toxicology studies(e.g.repro-ductive and developmental toxicity,etc.)Preclinical96Deep Pharma IntelligenceGenomenon is an AI-driven genomics company that organizes the world
317、s genomic knowledge to accelerate the diagnosis and development of treatments for genetic disease.Genomenons Prodigy Genomic Landscapes deliver a profound understanding of the genetic drivers and clinical attributes of any genetic disease and support the entire drug development process,from discover
318、y to commercialization.Genomenons main focus therapeutic areas are rare diseases,genetic diseases,and hereditary and somatic cancers.Most Innovative R&D Approaches of AI in Biopharma.Genomenon97Deep Pharma Intelligence2 Key AdvantagesProdigyIdentify molecular drivers of diseaseEvaluate market potent
319、ialAccelerate natural history studiesIdentify genomic biomarkers for trial Identify CDx inclusion criteriaIdentify patients for clinical trials/approved therapiesDrug DiscoveryTrial OptimizationPatient IdentificationGenomenons Prodigy Genomic Landscapes use a unique combination of proprietary Genomi
320、c Language Processing(GLP)and expert,scientific review to provide an evidence-based foundation for all stages of the drug development process.These landscapes can be completed at the disease,gene,variant,or patient level,and are maximally comprehensive as a result of GLP.Genomic Landscapes are also
321、rapidly produced using an AI-assisted curation engine that expedites manual review of the data indexed by GLP.Genomic Language Processing(GLP)is a novel technology that systematically extracts and standardizes genomic and clinical information from the medical and scientific literature.Designed speci
322、fically to recognize this complex genomic information,GLP provides superior sensitivity compared to traditional methods,finding more variants and subsequently,more patients.Genomenons database,built using GLP,currently contains over 14.8 million variants,8.8 million full-text articles,and 3 million
323、supplemental datasets.How Genomenon Uses AI in R&D98Deep Pharma Intelligence99Deep Pharma IntelligenceHow Genomenon Uses AI in R&DATP7B gene associated with Wilson diseaseGenomenons AI-driven ApproachClinVar Crowd-sourced DatabaseSearch for mutations 634 Pathogenic Variants235 Pathogenic VariantsGen
324、omenons AI-driven approach identified 3.7x more evidence-supported,pathogenic/likely pathogenic variants for ATP7B than ClinVar.We predict that this will improve the diagnosis of people living with Wilson disease by improving the ability to interpret genetic testing results.In collaboration with Ale
325、xion,AstraZenecas Rare Disease group,Genomenon applied its AI technology to help accelerate the genetic diagnosis for rare disease patients.Genomenons novel combination of AI-powered Genomic Language Processing and expert review identified significantly more pathogenic variants associated with Wilso
326、n disease.Genomenons AI-driven approach identified 3.7x more evidence-supported,pathogenic/likely pathogenic variants for ATP7B a gene associated with Wilson disease compared to the crowd-sourced database,ClinVar.This significantly expands the resources available to healthcare providers to make more
327、 informed diagnostic decisions.With greater adoption of Mastermind,we predict that the substantial increase in the number of known,disease-causing variants will improve the diagnosis of people living with Wilson disease by improving the ability to interpret genetic testing results.ATP7B gene associa
328、ted with Wilson diseaseSearch for mutations Genomenons AI-driven ApproachClinVar Crowd-sourced Database869 Pathogenic Variants235 Pathogenic VariantsGATC Health has an unprecedented technology that will lower costs and accelerate the drug discovery and development process to create better and safer
329、drugs,faster.The company delivers highly efficient services for pharma companies reducing the risk in the drug discovery process.GATC Health develops an end-to-end drug development cutting-edge AI-based platform with capabilities that include:earlier disease detection,identification of the disease b
330、iology,creation of new drug and therapeutic solutions,simulation of in-silico clinical trials and providing a feedback loop for in-vitro and in-vivo testing.GATCs Platform combines massive volumes of disease-specific data and proprietary AI solutions to replicate human biologys billions of interacti
331、ons for rapidly and accurately discovering and validating novel drugs.This is a revolutionary approach to drug discovery that can address nearly any condition,disease or disorder;while drastically improving costs,efficiency and time for clinical development.De-Risking and Accelerating Drug Discovery
332、&Development for Improved Success in Biopharma.GATC Health100Deep Pharma IntelligenceTarget Diseases Oncology NeurologyPsychiatrySubstance Abuse Cardiology Immunology Rheumatology Other diseasesOutcome 1-3 Small Molecules or Biologics Optimized for Specific Diseases and Patients2 Key AdvantagesDrug
333、Discovery Drug Validation Drug Efficacy Prediction Human-like Molecular ModelsDisease State Specific Models 101Deep Pharma IntelligenceHow GATC Health Uses AI in R&D Develop new therapeutics using in-silico and in-vivo clinical studies with more comprehensive analysis.Ensure higher levels of success as the drug progresses through FDA trials.Eliminate majority of the risk and cost associated with t