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AI-based Drug Discovery Market (2nd Edition), 2022-2035

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from $4,799 335 Pages Roots Analysis September, 2023 ROAL17136739
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Price from $4,799
Length 335 Pages
Publisher Roots Analysis
Published Date September, 2023
SKU ROAL17136739


The discovery and development process of a novel therapeutic candidate is often tedious and fraught with several challenges. The key concern associated with the overall process is the high attrition rate, which is often attributed to the trial-and-error approach followed for the drug discovery process. In fact, only a small proportion of pharmacological leads are translated into viable product candidates for clinical studies. In addition, experts believe that close to 90% of the product candidates considered in such studies fail to advance further in the development process. This, in turn, often results in a massive financial burden. In this context, it is estimated that a prescription drug takes around 10 to 15 years and an average investment of USD 1 to 2 billion, in order to traverse from the bench to the market. Moreover, around one-third of the aforementioned expenditure is incurred during the drug discovery phase alone. Therefore, to address the existing concerns, such as rising capital requirements and failure of late-stage programs, pharmaceutical players are currently exploring the implementation of Artificial Intelligence (AI) based tools to better inform their discovery and development operations, using available chemical and biological data. Specifically, AI is believed to be capable of processing and analyzing large volumes of clinical / medical data, as well as leverage it to better inform modern drug discovery efforts. In this context, deep learning algorithms have been demonstrated to be able to cross-reference published scientific literature (structured data) with electronic health records (EHRs) and clinical trial information (unstructured data), in order to generate actionable insights for target identification, hit generation and lead optimization.

At present, machine learning, deep learning, supervised learning, unsupervised learning and natural language processing are some of the key AI-based tools being deployed across different processes, including drug discovery, within the healthcare sector. The use of AI-enabled technologies in drug discovery operations is expected to not only improve the overall R&D productivity, but also reduce clinical failure of product candidates, by enabling accurate prediction of its safety and efficacy during early stages of development. Close to 210 companies currently claim to offer AI-based services, platforms and tools for drug discovery. Further, over USD 10 billion has been invested in this market by both private and public sector investors, in the last five years. Interestingly, close to 50% of the aforementioned amount was invested in the last two years, reflecting the increasing interest of stakeholders in AI-based tools for drug discovery. Additionally, close to 440 recently instances of collaborations have been reported between industry / academic stakeholders in order to advance the development of various AI-based solutions for drug discovery. Considering the active initiatives being undertaken by players based in this domain, we are led to believe that the opportunity for stakeholders in this niche, albeit upcoming, industry is likely to grow at a commendable pace in the foreseen future.

SCOPE OF THE REPORT

The ‘AI-based Drug Discovery Market (2nd Edition): Distribution by Drug Discovery Steps (Target Identification / Validation, Hit Generation / Lead Identification and Lead Optimization), Therapeutic Area (Oncological Disorders, CNS Disorders, Infectious Diseases, Respiratory Disorders, Cardiovascular Disorders, Endocrine Disorders, Gastrointestinal Disorders, Musculoskeletal Disorders, Immunological Disorders, Dermatological Disorders and Others) and Key Geographies (North America, Europe, Asia-Pacific, Latin America, MENA and Rest of the World): Industry Trends and Global Forecasts, 2022-2035’ report features an extensive study of the current landscape and future potential of the players engaged in offering AI-based services, platforms and tools for the discovery of novel drug candidates. The study features an in-depth analysis, highlighting the capabilities of AI-based drug discovery service / technology providers. Amongst other elements, the report features:

  • A detailed overview of the overall landscape of companies offering AI-based services, platforms and tools for drug discovery, along with information on several relevant parameters, such as their year of establishment, company size (in terms of employee count), location of headquarters (North America, Europe, Asia-Pacific and Rest of the World) and type of company (service providers, technology providers and in-house players). The chapter also covers details related to the type of AI technology (artificial intelligence (undefined), deep learning, machine learning (undefined), natural language processing, data science, reinforcement learning, supervised learning and unsupervised learning), drug discovery steps (target discovery / identification / validation, lead identification / optimization / generation and ADME / toxicity testing), type of drug molecule (small molecules, biologics and both) and target therapeutic area (oncological disorders, neurological disorders, infectious diseases, immunological disorders, cardiovascular disorders, rare diseases, metabolic disorders, respiratory disorders, gastrointestinal disorders, musculoskeletal disorders, dermatological disorders, hematological disorders, ophthalmic disorders and other disorders).
  • Elaborate profiles of prominent players (shortlisted based on a proprietary criterion) engaged in AI-based drug discovery domain, across North America, Europe and Asia-Pacific. Each profile provides an overview of the company, featuring information on the year of establishment, number of employees, location of their headquarters, key executives, details related to its AI-based drug discovery technology portfolio, recent developments and an informed future outlook.
  • An analysis of partnerships inked between stakeholders engaged in this domain, during the period 2009-2022, covering research and development agreements, technology access / utilization agreements, acquisitions, technology licensing agreements, joint ventures / mergers, technology integration agreements, service agreements and other related agreements. Further, the partnership activity in this domain has been analyzed based on various parameters, such as year of partnership, type of partnership, target therapeutic area, focus area, type of partner company and most active players (in terms of number of partnerships). It also highlights the regional distribution of the partnership activity witnessed in this market.
  • A detailed analysis of various investments, such as grants, awards, seed financing, venture capital financing, debt financing, capital raised from IPOs and subsequent offerings, that were undertaken by players engaged in this domain, during the period 2006-2022.
  • An in-depth analysis of the various patents that have been filed / granted related to AI-based drug discovery technologies, from 2019 to February 2022, taking into consideration parameters, such as application year, geographical region, CPC symbols, emerging focus areas, type of applicant and leading players (in terms of size of intellectual property portfolio). It also includes a patent benchmarking analysis and a detailed valuation analysis.
  • A qualitative analysis, highlighting the five competitive forces prevalent in this domain, including threats for new entrants, bargaining power of drug developers, bargaining power of AI-based drug discovery companies, threats of substitute technologies and rivalry among existing competitors.
  • An elaborate valuation analysis of companies that are involved in the AI-based drug discovery market, based on our proprietary, multi-variable dependent valuation model to estimate the current valuation / net worth of industry players.
  • An insightful analysis highlighting the likely cost saving potential associated with the use of AI in the drug discovery sector, based on information gathered from close to 15 countries, taking into consideration various parameters, such as pharmaceutical R&D expenditure, drug discovery expenditure / budget and adoption of AI across various drug discovery steps.
One of the key objectives of the report was to evaluate the current opportunity and future potential associated with the AI-based drug discovery, over the coming 13 years. We have provided informed estimates of the likely evolution of the market in the short to mid-term and long term, for the period 2022-2035. Our year-wise projections of the current and future opportunity have further been segmented based on relevant parameters, such as [A] drug discovery steps (target identification / validation, hit generation / lead identification and lead optimization), [B] target therapeutic area (oncological disorders, CNS disorders, infectious diseases, respiratory disorders, cardiovascular disorders, endocrine disorders, gastrointestinal disorders, musculoskeletal disorders, immunological disorders, dermatological disorders and others) and [C] key geographical regions (North America, Europe, Asia-Pacific, MENA, Latin America and Rest of the world). To account for future uncertainties in the market and to add robustness to our model, we have provided three forecast scenarios, portraying the conservative, base and optimistic tracks of the market’s evolution.

The opinions and insights presented in the report were also influenced by discussions held with senior stakeholders in the industry. The report features detailed transcripts of interviews held with the following individuals:
  • Steve Yemm (Chief Commercial Officer, Aigenpulse) and Satnam Surae (Chief Product Officer, Aigenpulse)
  • Ed Addison (Co-founder, Chairman and Chief Executive Officer, Cloud Pharmaceuticals)
  • Bo Ram Beck (Head Researcher, DEARGEN)
  • Simon Haworth (Chief Executive Officer, Intelligent Omics)
  • Immanuel Lerner (Chief Executive Officer and Co-Founder, Pepticom)
  • David Chiang (Chairman, Sage-N Research)
MARKET SEGMENTATIONS

AI-based Drug Discoveryss: Market Segmentations

Market SegmentsDetails

Forecast Period
  • 2022 - 2035

    • Drug Discovery Steps
    • Target Identification / Validation
    • Hit Generation / Lead Identification
    • Lead Optimization
    Therapeutic Areas
    • Oncological Disorders
    • CNS Disorders
    • Infectious Diseases
    • Respiratory Disorders
    • Cardiovascular Disorders
    • Endocrine Disorders
    • Gastrointestinal Disorders
    • Musculoskeletal Disorders
    • Immunological Disorders
    • Other Disorders
    Key Geographical Regions
    • North America
    • Europe
    • Asia-Pacific
    • MENA
    • Latin America
    • Rest of the World
    Source: Roots Analysis

    RESEARCH METHODOLOGY

    The data presented in this report has been gathered via secondary and primary research. For all our projects, we conduct interviews / surveys with experts in this domain (academia, industry, medical practice and other associations) to solicit their opinions on emerging trends in the market. This is primarily useful for us to draw out our own opinion on how the market will evolve across different regions and technology segments. Wherever possible, the available data has been checked for accuracy from multiple sources of information.

    The secondary sources of information include
    • Annual reports
    • Investor presentations
    • SEC filings
    • Industry databases
    • News releases from company websites
    • Government policy documents
    • Industry analysts’ views
    All actual figures have been sourced and analyzed from publicly available information forums and primary research discussions. Financial figures mentioned in this report are in USD, unless otherwise specified.

    KEY QUESTIONS ANSWERED
    • Who are the leading players engaged in the AI-based drug discovery market?
    • Which of the key AI technologies are presently being most commonly adopted by drug discovery focused companies?
    • What is the likely valuation / net worth of companies engaged in this domain?
    • What is the likely cost saving potential associated with the use of AI in the drug discovery process?
    • How is the intellectual property landscape for AI-based drug discovery technologies likely to evolve in the foreseen future?
    • Which partnership models are most commonly adopted by stakeholders engaged in this industry?
    • What is the overall trend of funding and investments within this domain?
    • How is the current and future opportunity likely to be distributed across key market segments?
    CHAPTER OUTLINES

    Chapter 2 is an executive summary of the key insights captured during our research. It offers a high-level view on the likely evolution of the AI-based drug discovery market in the short to mid-term, and long term.

    Chapter 3 provides a general overview on the digital revolution in the healthcare industry. It further features details on the applications of artificial intelligence and its subsets, including machine learning (supervised learning, unsupervised learning, reinforcement learning, deep learning, natural language processing) and data science. The chapter specifically emphasizes on the applications of AI in the healthcare sector, along with detailed information on drug discovery, drug manufacturing, drug marketing, diagnosis and treatment, and clinical trials. Additionally, it features detailed information on the different steps involved in the overall drug discovery process. The chapter concludes with a discussion on the advantages and challenges related to the use of AI in drug discovery.

    Chapter 4 features a detailed review of the current market landscape of around 210 companies offering AI-based services, platforms and tools for drug discovery. Additionally, it features an in-depth analysis of AI-based drug discovery companies, based on a number of relevant parameters, such as their year of establishment, company size (in terms of employee count), location of headquarters (North America, Europe, Asia-Pacific and rest of the world) and type of company (service providers, technology providers and in-house players). The chapter also covers details related to the type of AI technology (artificial intelligence (undefined), deep learning, machine learning (undefined), natural language processing, data science, reinforcement learning, supervised learning and unsupervised learning), drug discovery steps (target discovery / identification / validation, lead identification / optimization / generation and ADME / toxicity testing), type of drug molecule (small molecules, biologics and both) and target therapeutic area (oncological disorders, neurological disorders, infectious diseases, immunological disorders, cardiovascular disorders, rare diseases, metabolic disorders, respiratory disorders, gastrointestinal disorders, musculoskeletal disorders, dermatological disorders, hematological disorders, ophthalmic disorders and other disorders).

    Chapter 5 consists of detailed profiles of the prominent players (shortlisted based on a proprietary criterion) that are engaged in AI-based drug discovery domain in North America. Each profile provides an overview of the company, its AI-based drug discovery technology portfolio and details on recent developments, as well as an informed future outlook.

    Chapter 6 consists of detailed profiles of the prominent players (shortlisted based on a proprietary criterion) that are engaged in AI-based drug discovery domain in Europe. Each profile provides an overview of the company, its AI-based drug discovery technology portfolio and details on recent developments, as well as an informed future outlook.

    Chapter 7 consists of detailed profiles of the prominent players (shortlisted based on a proprietary criterion) that are engaged in AI-based drug discovery domain in Asia-Pacific. Each profile provides an overview of the company, its AI-based drug discovery technology portfolio and details on recent developments, as well as an informed future outlook.

    Chapter 8 features an insightful analysis of the various partnerships and collaborations that have been inked by stakeholders engaged in this domain, since 2009. It includes a brief description of the partnership models (including research and development agreements, technology access / utilization agreements, acquisitions, technology licensing agreements, joint ventures / mergers, technology integration agreements, service agreements and other related agreements) adopted by stakeholders in the domain of AI-based drug discovery. Further, it comprises of analysis based on several relevant parameters such as year of agreement, type of agreement, target therapeutic area, focus area, type of partner company and most active players (in terms of number of partnerships). Further, the chapter includes a world map representation of all the deals inked in this field in the period 2006-2022, highlighting both intercontinental and intracontinental partnership activities.

    Chapter 9 provides details on the various investments and grants that have been awarded to players focused on AI-based drug discovery. It includes a detailed analysis of the funding instances that have taken place during the period 2006 to 2022 (till February), highlighting the growing interest of venture capital (VC) community and other strategic investors in this domain.

    Chapter 10 provides an in-depth analysis of the various patents that have been filed / granted related to AI-based drug discovery technologies. For this analysis, we considered those patents that have been filed / granted related to AI-based drug discovery and development, from 2019 to February 2022, taking into consideration various parameters, such as application year, geographical region, CPC symbols, emerging focus areas, type of applicant and leading industry players (in terms of size of intellectual property portfolio). It also includes a patent benchmarking analysis and a detailed valuation analysis.

    Chapter 11 provides insights on a qualitative analysis highlighting five competitive forces in this domain, including threats for new entrants, bargaining power of drug developers, bargaining power of AI-based drug discovery companies, threats of substitute technologies and rivalry among existing competitors.

    Chapter 12 provides an elaborate valuation analysis of companies that are involved in the AI-based drug discovery market, based on our proprietary, multi-variable dependent valuation model to estimate the current valuation / net worth of industry players.

    Chapter 13 features brief details related to initiatives undertaken by technology giants in AI-based healthcare sector. The chapter includes information about companies, such as Amazon Web Services, Alibaba Cloud, Google, IBM, Intel, Microsoft and Siemens.

    Chapter 14 includes an insightful analysis highlighting the likely cost saving potential associated with the use of AI in the drug discovery sector, based on information gathered from close to 15 countries, taking into consideration various parameters, such as pharmaceutical R&D expenditure, drug discovery expenditure / budget and adoption of AI across various drug discovery steps.

    Chapter 15 presents an insightful market forecast analysis, highlighting the likely growth of the AI-based drug discovery market, for the period 2022-2035. Additionally, the report features the likely distribution of the current and forecasted opportunity across various relevant parameters such as [A] drug discovery steps (target identification / validation, hit generation / lead identification and lead optimization), [B] target therapeutic area (oncological disorders, CNS disorders, infectious diseases, respiratory disorders, cardiovascular disorders, endocrine disorders, gastrointestinal disorders, musculoskeletal disorders, immunological disorders, dermatological disorders and others) and [C] key geographical regions (North America, Europe, Asia-Pacific, MENA, Latin America and Rest of the World). To account for future uncertainties in the market and to add robustness to our model, we have provided three forecast scenarios, portraying the conservative, base and optimistic tracks of the market’s evolution.

    Chapter 16 summarizes the overall report. In this chapter, we have provided a list of key takeaways from the report, and expressed our independent opinion related to the research and analysis described in the previous chapters.

    Chapter 17 provides the transcripts of the interviews conducted with representatives from renowned organizations that are engaged in AI-based drug discovery. The chapter contains the details of our conversation with Steve Yemm (Chief Commercial Officer, Aigenpulse) and Satnam Surae (Chief Product Officer, Aigenpulse), Ed Addison (Co-founder, Chairman and Chief Executive Officer, Cloud Pharmaceuticals), Bo Ram Beck (Head Researcher, DEARGEN), Simon Haworth (Chief Executive Officer, Intelligent Omics), Immanuel Lerner (Chief Executive Officer, Co-Founder, Pepticom) and David Chiang (Chairman, Sage-N Research).

    Chapter 18 is an appendix, that provides tabulated data and numbers for all the figures included in the report.

    Chapter 19 is an appendix that provides the list of companies and organizations that have been mentioned in the report.


    1. PREFACE
    1.1. Scope of the Report
    1.2. Research Methodology
    1.3. Key Questions Answered
    1.4. Chapter Outlines
    2. EXECUTIVE SUMMARY
    3. INTRODUCTION
    3.1. Chapter Overview
    3.2. Artificial Intelligence
    3.3. Subsets of AI
    3.3.1. Machine Learning
    3.3.1.1. Supervised Learning
    3.3.1.2. Unsupervised Learning
    3.3.1.3. Reinforced / Reinforcement Learning
    3.3.1.4. Deep Learning
    3.3.1.5. Natural Language Processing (NLP)
    3.4. Data Science
    3.5. Applications of AI in Healthcare
    3.5.1. Drug Discovery
    3.5.2. Disease Prediction, Diagnosis and Treatment
    3.5.3. Manufacturing and Supply Chain Operations
    3.5.4. Marketing
    3.5.5. Clinical Trials
    3.6. AI in Drug Discovery
    3.6.1. Identification of Pathway and Target
    3.6.2. Identification of Hit or Lead
    3.6.3. Lead Optimization
    3.6.4. Synthesis of Drug-Like Compounds
    3.7. Advantages of Using AI in the Drug Discovery Process
    3.8. Challenges Associated with the Adoption of AI
    3.9. Concluding Remarks
    4. COMPETITIVE LANDSCAPE
    4.1. Chapter Overview
    4.2. AI-based Drug Discovery: Overall Market Landscape
    4.2.1. Analysis by Year of Establishment
    4.2.2. Analysis by Company Size
    4.2.3. Analysis by Location of Headquarters
    4.2.4. Analysis by Type of Company
    4.2.5. Analysis by Type of AI Technology
    4.2.6. Analysis by Drug Discovery Steps
    4.2.7. Analysis by Type of Drug Molecule
    4.2.8. Analysis by Drug Development Initiatives
    4.2.9. Analysis by Technology Licensing Option
    4.2.10. Analysis by Target Therapeutic Area
    4.2.11. Key Players: Analysis by Number of Platforms / Tools Available
    5. COMPANY PROFILES: AI-BASED DRUG DISCOVERY PROVIDERS IN NORTH AMERICA
    5.1. Chapter Overview
    5.2. Atomwise
    5.2.1. Company Overview
    5.2.2. AI-based Drug Discovery Technology Portfolio
    5.2.3. Recent Developments and Future Outlook
    5.3. BioSyntagma
    5.3.1. Company Overview
    5.3.2. AI-based Drug Discovery Technology Portfolio
    5.3.3. Recent Developments and Future Outlook
    5.4. Collaborations Pharmaceuticals
    5.4.1. Company Overview
    5.4.2. AI-based Drug Discovery Technology Portfolio
    5.4.3. Recent Developments and Future Outlook
    5.5. Cyclica
    5.5.1. Company Overview
    5.5.2. AI-based Drug Discovery Technology Portfolio
    5.5.3. Recent Developments and Future Outlook
    5.6. InveniAI
    5.6.1. Company Overview
    5.6.2. AI-based Drug Discovery Technology Portfolio
    5.6.3. Recent Developments and Future Outlook
    5.7. Recursion Pharmaceuticals
    5.7.1. Company Overview
    5.7.2. AI-based Drug Discovery Technology Portfolio
    5.7.3. Recent Developments and Future Outlook
    5.8. Valo Health
    5.8.1. Company Overview
    5.8.2. AI-based Drug Discovery Technology Portfolio
    5.8.3. Recent Developments and Future Outlook
    6. COMPANY PROFILES: AI-BASED DRUG DISOCVERY SERVICE PROVIDERS IN EUROPE
    6.1. Chapter Overview
    6.2. Aiforia Technologies
    6.2.1. Company Overview
    6.2.2. AI-based Drug Discovery Technology Portfolio
    6.2.3. Recent Developments and Future Outlook
    6.3. Chemalive
    6.3.1. Company Overview
    6.3.2. AI-based Drug Discovery Technology Portfolio
    6.3.3. Recent Developments and Future Outlook
    6.4. DeepMatter
    6.4.1. Company Overview
    6.4.2. AI-based Drug Discovery Technology Portfolio
    6.4.3. Recent Developments and Future Outlook
    6.5. Exscientia
    6.5.1. Company Overview
    6.5.2. AI-based Drug Discovery Technology Portfolio
    6.5.3. Recent Developments and Future Outlook
    6.6. MAbSilico
    6.6.1. Company Overview
    6.6.2. AI-based Drug Discovery Technology Portfolio
    6.6.3. Recent Developments and Future Outlook
    6.7. Optibrium
    6.7.1. Company Overview
    6.7.2. AI-based Drug Discovery Technology Portfolio
    6.7.3. Recent Developments and Future Outlook
    6.8. Sensyne Health
    6.8.1. Company Overview
    6.8.2. AI-based Drug Discovery Technology Portfolio
    6.8.3. Recent Developments and Future Outlook
    7. COMPANY PROFILES: AI-BASED DRUG DISOCVERY SERVICE PROVIDERS IN ASIA PACIFIC
    7.1. Chapter Overview
    7.2. 3BIGS
    7.2.1. Company Overview
    7.2.2. AI-based Drug Discovery Technology Portfolio
    7.2.3. Recent Developments and Future Outlook
    7.3. Gero
    7.3.1. Company Overview
    7.3.2. AI-based Drug Discovery Technology Portfolio
    7.3.3. Recent Developments and Future Outlook
    7.4. Insilico Medicine
    7.4.1. Company Overview
    7.4.2. AI-based Drug Discovery Technology Portfolio
    7.4.3. Recent Developments and Future Outlook
    7.5. KeenEye
    7.5.1. Company Overview
    7.5.2. AI-based Drug Discovery Technology Portfolio
    7.5.3. Recent Developments and Future Outlook
    8. PARTNERSHIPS AND COLLABORATIONS
    8.1. Chapter Overview
    8.2. Partnership Models
    8.3. AI-based Drug Discovery: Partnerships and Collaborations
    8.3.1. Analysis by Year of Partnership
    8.3.2. Analysis by Type of Partnership
    8.3.3. Analysis by Year and Type of Partnership
    8.3.4. Analysis by Target Therapeutic Area
    8.3.5. Analysis by Focus Area
    8.3.6. Analysis by Year of Partnership and Focus Area
    8.3.7. Analysis by Type of Partner Company
    8.3.8. Analysis by Type of Partnership and Type of Partner Company
    8.3.9. Most Active Players: Analysis by Number of Partnerships
    8.3.10. Analysis by Region
    8.3.11.1. Intercontinental and Intracontinental Deals
    8.3.11.2. International and Local Deals
    9. FUNDING AND INVESTMENT ANALYSIS
    9.1. Chapter Overview
    9.2. Types of Funding
    9.3. AI-based Drug Discovery: Funding and Investments
    9.3.1. Analysis of Number of Funding Instances by Year
    9.3.2. Analysis of Amount Invested by Year
    9.3.3. Analysis by Type of Funding
    9.3.4. Analysis of Amount Invested and Type of Funding
    9.3.5. Analysis of Amount Invested by Company Size
    9.3.6. Analysis by Type of Investor
    9.3.7. Analysis of Amount Invested by Type of Investor
    9.3.8. Most Active Players: Analysis by Number of Funding Instances
    9.3.9. Most Active Players: Analysis by Amount Invested
    9.3.10. Most Active Investors: Analysis by Number of Funding Instances
    9.3.11. Analysis of Amount Invested by Geography
    9.3.11.1. Analysis by Region
    9.3.11.2. Analysis by Country
    10. PATENT ANALYSIS
    10.1. Chapter Overview
    10.2. Scope and Methodology
    10.3. AI-based Drug Discovery: Patent Analysis
    10.3.1 Analysis by Application Year
    10.3.2. Analysis by Geography
    10.3.3. Analysis by CPC Symbols
    10.3.4. Analysis by Emerging Focus Areas
    10.3.5. Analysis by Type of Applicant
    10.3.6. Leading Players: Analysis by Number of Patents
    10.4. AI-based Drug Discovery: Patent Benchmarking
    10.4.1. Analysis by Patent Characteristics
    10.5. AI-based Drug Discovery: Patent Valuation
    11. PORTER’S FIVE FORCES ANALYSIS
    11.1. Chapter Overview
    11.2. Methodology and Assumptions
    11.3. Key Parameters
    11.3.1. Threats of New Entrants
    11.3.2. Bargaining Power of Drug Developers
    11.3.3. Bargaining Power of Companies Using AI for Drug Discovery
    11.3.4. Threats of Substitute Technologies
    11.3.5. Rivalry Among Existing Competitors
    11.4. Concluding Remarks
    12. COMPANY VALUATION ANALYSIS
    12.1. Chapter Overview
    12.2. Company Valuation Analysis: Key Parameters
    12.3. Methodology
    12.3.1. Employee Score
    12.3.2. Experience Score
    12.3.3. Portfolio Strength / Uniqueness Score
    12.3.4. Funding Score
    12.3.5. Partnerships Score
    12.3.6. Weighted Average Score
    12.4. Company Valuation Analysis: Roots Analysis Proprietary Scores
    13. AI-BASED HEALTHCARE INITIATIVES OF TECHNOLOGY GIANTS
    13.1 Chapter Overview
    13.1.1. Amazon Web Services
    13.1.2. Microsoft
    13.1.3. Intel
    13.1.4. Alibaba Cloud
    13.1.5. Siemens
    13.1.6. Google
    13.1.7. IBM
    14. COST SAVING ANALYSIS
    14.1. Chapter Overview
    14.2. Key Assumptions and Methodology
    14.3. Overall Cost Saving Potential Associated with Use of AI-based Solutions in Drug Discovery, 2022-2035
    14.3.1. Likely Cost Savings: Analysis by Drug Discovery Steps, 2022-2035
    14.3.1.1. Likely Cost Savings During Target Identification / Validation, 2022-2035
    14.3.1.2. Likely Cost Savings During Hit Generation / Lead Identification, 2022-2035
    14.3.1.3. Likely Cost Savings During Lead Optimization, 2022-2035
    14.3.2. Likely Cost Savings: Analysis by Target Therapeutic Area, 2022-2035
    14.3.2.1. Likely Cost Savings for Drugs Targeting Oncological Disorders, 2022-2035
    14.3.2.2. Likely Cost Savings for Drugs Targeting Neurological Disorders, 2022-2035
    14.3.2.3. Likely Cost Savings for Drugs Targeting Infectious Diseases, 2022-2035
    14.3.2.4. Likely Cost Savings for Drugs Targeting Respiratory Disorders, 2022-2035
    14.3.2.5. Likely Cost Savings for Drugs Targeting Cardiovascular Disorders, 2022-2035
    14.3.2.6. Likely Cost Savings for Drugs Targeting Endocrine Disorders, 2022-2035
    14.3.2.7. Likely Cost Savings for Drugs Targeting Gastrointestinal Disorders, 2022-2035
    14.3.2.8. Likely Cost Savings for Drugs Targeting Musculoskeletal Disorders, 2022-2035
    14.3.2.9. Likely Cost Savings for Drugs Targeting Immunological Disorders, 2022-2035
    14.3.2.10. Likely Cost Savings for Drugs Targeting Dermatological Disorders, 2022-2035
    14.3.2.11. Likely Cost Savings for Drugs Targeting Other Disorders, 2022-2035
    14.3.3. Likely Cost Savings: Analysis by Geography, 2022-2035
    14.3.3.1. Likely Cost Savings in North America, 2022-2035
    14.3.3.2. Likely Cost Savings in Europe, 2022-2035
    14.3.3.3. Likely Cost Savings in Asia Pacific, 2022-2035
    14.3.3.4. Likely Cost Savings in MENA, 2022-2035
    14.3.3.5. Likely Cost Savings in Latin America, 2022-2035
    14.3.3.6. Likely Cost Savings in Rest of the World, 2022-2035
    15. MARKET FORECAST
    15.1. Chapter Overview
    15.2. Key Assumptions and Methodology
    15.3. Global AI-based Drug Discovery Market, 2022-2035
    15.3.1. AI-based Drug Discovery Market: Distribution by Drug Discovery Steps,
    2022-2035
    15.3.1.1. AI-based Drug Discovery Market for Target Identification / Validation,
    2022-2035
    15.3.1.2. AI-based Drug Discovery Market for Hit Generation / Lead Identification,
    2022-2035
    15.3.1.3. AI-based Drug Discovery Market for Lead Optimization, 2022-2035
    15.3.2. AI-based Drug Discovery Market: Distribution by Target Therapeutic Area,
    2022-2035
    15.3.2.1. AI-based Drug Discovery Market for Oncological Disorders, 2022-2035
    15.3.2.2. AI-based Drug Discovery Market for Neurological Disorders, 2022-2035
    15.3.2.3. AI-based Drug Discovery Market for Infectious Diseases, 2022-2035
    15.3.2.4. AI-based Drug Discovery Market for Respiratory Disorders, 2022-2035
    15.3.2.5. AI-based Drug Discovery Market for Cardiovascular Disorders, 2022-2035
    15.3.2.6. AI-based Drug Discovery Market for Endocrine Disorders, 2022-2035
    15.3.2.7. AI-based Drug Discovery Market for Gastrointestinal Disorders, 2022-2035
    15.3.2.8. AI-based Drug Discovery Market for Musculoskeletal Disorders, 2022-2035
    15.3.2.9. AI-based Drug Discovery Market for Immunological Disorders, 2022-2035
    15.3.2.10. AI-based Drug Discovery Market for Dermatological Disorders, 2022-2035
    15.3.2.11. AI-based Drug Discovery Market for Other Disorders, 2022-2035
    15.3.3. AI-based Drug Discovery Market: Distribution by Geography, 2022-2035
    15.3.3.1. AI-based Drug Discovery Market in North America, 2022-2035
    15.3.3.1.1. AI-based Drug Discovery Market in the US, 2022-2035
    15.3.3.1.2. AI-based Drug Discovery Market in Canada, 2022-2035
    15.3.3.2. AI-based Drug Discovery Market in Europe, 2022-2035
    15.3.3.2.1. AI-based Drug Discovery Market in the UK, 2022-2035
    15.3.3.2.2. AI-based Drug Discovery Market in France, 2022-2035
    15.3.3.2.3. AI-based Drug Discovery Market in Germany, 2022-2035
    15.3.3.2.4. AI-based Drug Discovery Market in Spain, 2022-2035
    15.3.3.2.5. AI-based Drug Discovery Market in Italy, 2022-2035
    15.3.3.2.6. AI-based Drug Discovery Market in Rest of Europe, 2022-2035
    15.3.3.3. AI-based Drug Discovery Market in Asia Pacific, 2020-2035
    15.3.3.3.1. AI-based Drug Discovery Market in China, 2022-2035
    15.3.3.3.2. AI-based Drug Discovery Market in India, 2022-2035
    15.3.3.3.3. AI-based Drug Discovery Market in Japan, 2022-2035
    15.3.3.3.4. AI-based Drug Discovery Market in Australia, 2022-2035
    15.3.3.3.5. AI-based Drug Discovery Market in South Korea, 2022-2035
    15.3.3.4. AI-based Drug Discovery Market in MENA, 2022-2035
    15.3.3.4.1. AI-based Drug Discovery Market in Saudi Arabia, 2022-2035
    15.3.3.4.2. AI-based Drug Discovery Market in UAE, 2022-2035
    15.3.3.4.3. AI-based Drug Discovery Market in Iran, 2022-2035
    15.3.3.5. AI-based Drug Discovery Market in Latin America, 2022-2035
    15.3.3.5.1. AI-based Drug Discovery Market in Argentina, 2022-2035
    15.3.3.6. AI-based Drug Discovery Market in Rest of the World, 2022-2035
    16. CONCLUSION
    17. EXECUTIVE INSIGHTS
    17.1. Chapter Overview
    17.2. Aigenpulse
    17.2.1. Company Snapshot
    17.2.2. Interview Transcript: Steve Yemm (Chief Commercial Officer) and Satnam
    Surae (Chief Product Officer)
    17.3. Cloud Pharmaceuticals
    17.3.1. Company Snapshot
    17.3.2. Interview Transcript: Ed Addison (Co-founder, Chairman and Chief Executive
    Officer)
    17.4. DEARGEN
    17.4.1. Company Snapshot
    17.4.2. Interview Transcript: Bo Ram Beck (Head Researcher)
    17.5. Intelligent Omics
    17.5.1. Company Snapshot
    17.5.2. Interview Transcript: Simon Haworth (Chief Executive Officer)
    17.6. Pepticom
    17.6.1. Company Snapshot
    17.6.2. Interview Transcript: Immanuel Lerner (Chief Executive Officer, Co-Founder)
    17.7. Sage-N Research
    17.7.1. Company Snapshot
    17.7.2. Interview Transcript: David Chiang (Chairman)
    18. APPENDIX I: TABULATED DATA
    19. APPENDIX II:
    LIST OF COMPANIES AND ORGANIZATIONS
    LIST OF FIGURES
    Figure 2.1 Executive Summary: Overall Market Landscape
    Figure 2.2 Executive Summary: Partnerships and Collaborations
    Figure 2.3 Executive Summary: Funding and Investment Analysis
    Figure 2.4 Executive Summary: Patent Analysis
    Figure 2.5 Executive Summary: Cost Saving Analysis
    Figure 2.6 Executive Summary: Market Forecast
    Figure 3.1 Evolution of AI
    Figure 3.2. Key Segments of AI
    Figure 3.3. Interconnection between Data Science, Artificial Intelligence and Big Data
    Figure 3.4. Applications of AI
    Figure 4.1. AI-based Drug Discovery: Distribution by Year of Establishment
    Figure 4.2. AI-based Drug Discovery: Distribution by Company Size
    Figure 4.3. AI-based Drug Discovery: Distribution by Location of Headquarters (Region-Wise)
    Figure 4.4. AI-based Drug Discovery: Distribution by Location of Headquarters (Country-Wise)
    Figure 4.5. AI-based Drug Discovery: Distribution by Company Size and Location of Headquarters
    Figure 4.6. AI-based Drug Discovery: Distribution by Type of Company
    Figure 4.7. AI-based Drug Discovery: Distribution by Type of AI Technology
    Figure 4.8. AI-based Drug Discovery: Distribution by Drug Discovery Steps
    Figure 4.9. AI-based Drug Discovery: Distribution by Type of Drug Molecule
    Figure 4.10. AI-based Drug Discovery: Distribution by Drug Development Initiatives
    Figure 4.11. AI-based Drug Discovery: Distribution by Technology Licensing Option
    Figure 4.12. AI-based Drug Discovery: Distribution by Target Therapeutic Area
    Figure 4.13. Key Players: Distribution by Number of Platforms / Tools Available
    Figure 8.1 Partnerships and Collaborations: Cumulative Year-wise Trend
    Figure 8.2 Partnerships and Collaborations: Distribution by Type of Partnership
    Figure 8.3 Partnerships and Collaborations: Distribution by Year and Type of Partnership
    Figure 8.4 Partnerships and Collaborations: Distribution by Target Therapeutic Area
    Figure 8.5 Partnerships and Collaborations: Distribution by Focus Area
    Figure 8.6 Partnerships and Collaborations: Distribution by Year of Partnership and Focus Area
    Figure 8.7 Partnerships and Collaborations: Distribution by Type of Partner Company
    Figure 8.8 Partnerships and Collaborations: Distribution by Type of Partner Company and Type of Partnerships
    Figure 8.9 Most Active Players: Distribution by Number of Partnerships
    Figure 8.10 Partnerships and Collaborations: Distribution of Intercontinental and Intracontinental Deals
    Figure 8.11 Partnerships and Collaborations: Distribution of International and Local Deals
    Figure 9.1 Funding and Investment Analysis: Cumulative Year-wise Distribution of Funding Instances, 2006-2022
    Figure 9.2 Funding and Investment Analysis: Cumulative Year-wise Distribution of Amount Invested (USD Million), 2006-2022
    Figure 9.3 Funding and Investment Analysis: Distribution of Instances by Type of Funding
    Figure 9.4 Funding and Investment Analysis: Distribution of Amount Invested by Type of Funding (USD Million)
    Figure 9.6 Funding and Investment Analysis: Distribution of Amount Invested by Company Size (USD Million)
    Figure 9.7 Funding and Investment Analysis: Distribution of Number of Funding Instances by Type of Investor
    Figure 9.8 Funding and Investment Analysis: Distribution of Amount Invested by Type of Investor (USD Million)
    Figure 9.9 Most Active Players: Distribution by Number of Funding Instances
    Figure 9.10 Most Active Players: Distribution by Amount Invested
    Figure 9.11 Most Active Investors: Distribution by Number of Funding Instances
    Figure 9.12 Funding and Investment: Distribution of Amount Invested by Region (USD Million)
    Figure 9.13 Funding and Investment: Distribution of Amount Invested by Country (USD Million)
    Figure 10.1 Patent Analysis: Distribution by Type of Patent
    Figure 10.2 Patent Analysis: Distribution by Application Year
    Figure 10.3 Patent Analysis: Distribution by Location of Patent Jurisdiction (Region-wise)
    Figure 10.4 Patent Analysis: Distribution by Location of Patent Jurisdiction (Country-wise)
    Figure 10.5 Patent Analysis: Distribution by CPC Symbols
    Figure 10.6 Patent Analysis: Emerging Focus Area
    Figure 10.7 Patent Analysis: Cumulative Year-wise Distribution by Type of Applicant
    Figure 10.8 Leading Industry Players: Distribution by Number of Patents
    Figure 10.9 Leading Non-Industry Players: Distribution by Number of Patents
    Figure 10.10 Leading Patent Assignees: Distribution by Number of Patents
    Figure 10.11 Patent Benchmarking: Distribution of Leading Industry Players by Patent Characteristics (CPC Symbols)
    Figure 10.12 Patent Analysis: Distribution by Age
    Figure 10.13 Patent Analysis: Patent Valuation
    Figure 11.1 Porters Five Forces: Key Parameters
    Figure 11.2 Porters Five Forces: Harvey Ball Analysis
    Figure 12.1 Company Valuation Analysis: Categorization by Employee Score
    Figure 12.2 Company Valuation Analysis: Categorization by Experience Score
    Figure 12.3 Company Valuation Analysis: Categorization by Portfolio Strength / Uniqueness Score
    Figure 12.4 Company Valuation Analysis: Categorization by Funding Score
    Figure 12.5 Company Valuation Analysis: Categorization by Partnership Score
    Figure 12.6 Company Valuation Analysis: Categorization by Weighted Average Score
    Figure 14.1 Overall Cost Saving Potential Associated with Use of AI-based Solutions in Drug Discovery, 2022-2035 (USD Million)
    Figure 14.2 Likely Cost Savings: Distribution by Drug Discovery Steps, 2022-2035 (USD Million)
    Figure 14.3 Likely Cost Savings During Target Identification / Validation, 2022-2035 (USD Million)
    Figure 14.4 Likely Cost Savings During Hit Generation / Lead Identification, 2022-2035 (USD Million)
    Figure 14.5 Likely Cost Savings During Lead Optimization, 2022-2035 (USD Million)
    Figure 14.6 Likely Cost Savings: Distribution by Target Therapeutic Area, 2022-2035 (USD Million)
    Figure 14.7 Likely Cost Savings for Drugs Targeting Oncological Disorders, 2022-2035 (USD Million)
    Figure 14.8 Likely Cost Savings for Drugs Targeting Neurological Disorders, 2022-2035 (USD Million)
    Figure 14.9 Likely Cost Savings for Drugs Targeting Infectious Diseases, 2022-2035 (USD Million)
    Figure 14.10 Likely Cost Savings for Drugs Targeting Respiratory Disorders, 2022-2035 (USD Million)
    Figure 14.11 Likely Cost Savings for Drugs Targeting Cardiovascular Disorders, 2022-2035 (USD Million)
    Figure 14.12 Likely Cost Savings for Drugs Targeting Endocrine Disorders, 2022-2035 (USD Million)
    Figure 14.13 Likely Cost Savings for Drugs Targeting Gastrointestinal Disorders, 2022-2035 (USD Million)
    Figure 14.14 Likely Cost Savings for Drugs Targeting Musculoskeletal Disorders, 2022-2035 (USD Million)
    Figure 14.15 Likely Cost Savings for Drugs Targeting Immunological Disorders, 2022-2035 (USD Million)
    Figure 14.16 Likely Cost Savings for Drugs Targeting Dermatological Disorders, 2022-2035 (USD Million)
    Figure 14.17 Likely Cost Savings for Drugs Targeting Other Disorders, 2022-2035 (USD Million)
    Figure 14.18 Likely Cost Savings: Distribution by Geography, 2022-2035 (USD Million)
    Figure 14.19 Likely Cost Savings in North America, 2022-2035 (USD Million)
    Figure 14.20 Likely Cost Savings in Europe, 2022-2035 (USD Million)
    Figure 14.21 Likely Cost Savings in Asia Pacific, 2022-2035 (USD Million)
    Figure 14.22 Likely Cost Savings in MENA, 2022-2035 (USD Million)
    Figure 14.23 Likely Cost Savings in Latin America, 2022-2035 (USD Million)
    Figure 14.24 Likely Cost Savings in Rest of the World, 2022-2035 (USD Million)
    Figure 15.1 Global AI-based Drug Discovery Market, 2022-2035 (USD Million)
    Figure 15.2 AI-based Drug Discovery Market: Distribution by Drug Discovery Steps, 2022-2035 (USD Million)
    Figure 15.3 AI-based Drug Discovery Market for Target Identification / Validation, 2022-2035 (USD Million)
    Figure 15.4 AI-based Drug Discovery Market for Hit Generation / Lead Identification, 2022-2035 (USD Million)
    Figure 15.5 AI-based Drug Discovery Market for Lead Optimization, 2022-2035 (USD Million)
    Figure 15.6 AI-based Drug Discovery Market: Distribution by Target Therapeutic Area, 2022-2035 (USD Million)
    Figure 15.7 AI-based Drug Discovery Market for Oncological Disorders, 2022-2035 (USD Million)
    Figure 15.8 AI-based Drug Discovery Market for Neurological Disorders 2022-2035 (USD Million)
    Figure 15.9 AI-based Drug Discovery Market for Infectious Diseases, 2022-2035 (USD Million)
    Figure 15.10 AI-based Drug Discovery Market for Respiratory Disorders, 2022-2035 (USD Million)
    Figure 15.11 AI-based Drug Discovery Market for Cardiovascular Disorders, 2022-2035 (USD Million)
    Figure 15.12 AI-based Drug Discovery Market for Endocrine Disorders, 2022-2035 (USD Million)
    Figure 15.13 AI-based Drug Discovery Market for Gastrointestinal Disorders, 2022-2035 (USD Million)
    Figure 15.14 AI-based Drug Discovery Market for Musculoskeletal Disorders, 2022-2035 (USD Million)
    Figure 15.15 AI-based Drug Discovery Market for Immunological Disorders, 2022-2035 (USD Million)
    Figure 15.16 AI-based Drug Discovery Market for Dermatological Disorders, 2022-2035 (USD Million)
    Figure 15.17 AI-based Drug Discovery Market for Other Disorders, 2022-2035 (USD Million)
    Figure 15.18 AI-based Drug Discovery Market: Distribution by Geography, 2022-2035 (USD Million)
    Figure 15.19 AI-based Drug Discovery Market in North America, 2022-2035 (USD Million)
    Figure 15.20 AI-based Drug Discovery Market in the US, 2022-2035 (USD Million)
    Figure 15.21 AI-based Drug Discovery Market in Canada, 2022-2035 (USD Million)
    Figure 15.22 AI-based Drug Discovery Market in Europe, 2022-2035 (USD Million)
    Figure 15.23 AI-based Drug Discovery Market in UK, 2022-2035 (USD Million)
    Figure 15.24 AI-based Drug Discovery Market in France, 2022-2035 (USD Million)
    Figure 15.25 AI-based Drug Discovery Market in Germany, 2022-2035 (USD Million)
    Figure 15.26 AI-based Drug Discovery Market in Spain, 2022-2035 (USD Million)
    Figure 15.27 AI-based Drug Discovery Market in Italy, 2022-2035 (USD Million)
    Figure 15.28 AI-based Drug Discovery Market in Rest of Europe, 2022 - 2035 (USD Million)
    Figure 15.29 AI-based Drug Discovery Market in Asia Pacific, 2022-2035 (USD Million)
    Figure 15.30 AI-based Drug Discovery Market in China, 2022-2035 (USD Million)
    Figure 15.31 AI-based Drug Discovery Market in India, 2022-2035 (USD Million)
    Figure 15.32 AI-based Drug Discovery Market in Japan, 2022-2035 (USD Million)
    Figure 15.33 AI-based Drug Discovery Market in Australia, 2022-2035 (USD Million)
    Figure 15.34 AI-based Drug Discovery Market in South Korea, 2022-2035 (USD Million)
    Figure 15.35 AI-based Drug Discovery Market in MENA, 2022-2035 (USD Million)
    Figure 15.36 AI-based Drug Discovery Market in Saudi Arabia, 2022-2035 (USD Million)
    Figure 15.37 AI-based Drug Discovery Market in UAE, 2022-2035 (USD Million)
    Figure 15.38 AI-based Drug Discovery Market in Iran, 2022-2035 (USD Million)
    Figure 15.39 AI-based Drug Discovery Market in Latin America, 2022-2035 (USD Million)
    Figure 15.40 AI-based Drug Discovery Market in Argentina, 2022-2035 (USD Million)
    Figure 15.41 AI-based Drug Discovery Market in Rest of the World, 2022-2035 (USD Million)
    Figure 16.1 Concluding Remarks: Current Market Landscape
    Figure 16.2 Concluding Remarks: Partnerships and Collaborations
    Figure 16.3 Concluding Remarks: Funding and Investments
    Figure 16.4 Concluding Remarks: Patent Analysis
    Figure 16.5 Concluding Remarks: Company Valuation Analysis
    Figure 16.6 Concluding Remarks: Cost Saving Analysis
    Figure 16.7 Concluding Remarks: Market Forecast
    LIST OF TABLES
    Table 4.1 AI-based Drug Discovery Service / Technology Providers: Information on Year of Establishment, Company Size, Location of Headquarters, Number and Name of Platforms / Tools Available
    Table 4.2 AI-based Drug Discovery Service / Technology Providers: Information on Type of AI Technology and Drug Discovery Steps
    Table 4.3 AI-based Drug Discovery Service / Technology Providers: Information on Type of Drug Molecule and Target Therapeutic Area
    Table 5.1 Leading AI-based Drug Discovery Service / Technology Providers in North America
    Table 5.2 Atomwise: Company Snapshot
    Table 5.3 Atomwise: AI-based Drug Discovery Technologies
    Table 5.4 Atomwise: Recent Developments and Future Outlook
    Table 5.5 BioSyntagma: Company Snapshot
    Table 5.6 BioSyntagma: AI-based Drug Discovery Technologies
    Table 5.7 BioSyntagma: Recent Developments and Future Outlook
    Table 5.8 Collaborations Pharmaceuticals: Company Snapshot
    Table 5.9 Collaborations Pharmaceuticals: AI-based Drug Discovery Technologies
    Table 5.10 Collaborations Pharmaceuticals: Recent Developments and Future Outlook
    Table 5.11 Cyclica: Company Snapshot
    Table 5.12 Cyclica: AI-based Drug Discovery Technologies
    Table 5.13 Cyclica: Recent Developments and Future Outlook
    Table 5.14 InveniAI: Company Snapshot
    Table 5.15 InveniAI: AI-based Drug Discovery Technologies
    Table 5.16 InveniAI: Recent Developments and Future Outlook
    Table 5.17 Recursion Pharmaceuticals: Company Snapshot
    Table 5.18 Recursion Pharmaceuticals: AI-based Drug Discovery Technologies
    Table 5.19 Recursion Pharmaceuticals: Recent Developments and Future Outlook
    Table 5.20 Valo Health: Company Snapshot
    Table 5.21 Valo Health: AI-based Drug Discovery Technologies
    Table 5.22 Valo Health: Recent Developments and Future Outlook
    Table 6.1 Leading AI-based Drug Discovery Service / Technology Providers in Europe
    Table 6.2 Aiforia Technologies: Company Snapshot
    Table 6.3 Aiforia Technologies: AI-based Drug Discovery Technologies
    Table 6.4 Aiforia Technologies: Recent Developments and Future Outlook
    Table 6.5 Chemalive: Company Snapshot
    Table 6.6 Chemalive: AI-based Drug Discovery Technologies
    Table 6.7 Chemalive: Recent Developments and Future Outlook
    Table 6.8 DeepMatter: Company Snapshot
    Table 6.9 DeepMatter: AI-based Drug Discovery Technologies
    Table 6.10 DeepMatter: Recent Developments and Future Outlook
    Table 6.11 Exscientia: Company Snapshot
    Table 6.12 Exscientia: AI-based Drug Discovery Technologies
    Table 6.13 Exscientia: Recent Developments and Future Outlook
    Table 6.14 MAbSilico: Company Snapshot
    Table 6.15 MAbSilico: AI-based Drug Discovery Technologies
    Table 6.16 MAbSilico: Recent Developments and Future Outlook
    Table 6.17 Optibrium: Company Snapshot
    Table 6.18 Optibrium: AI-based Drug Discovery Technologies
    Table 6.19 Optibrium: Recent Developments and Future Outlook
    Table 6.20 Sensyne Health: Company Snapshot
    Table 6.21 Sensyne Health: AI-based Drug Discovery Technologies
    Table 6.22 Sensyne Health: Recent Developments and Future Outlook
    Table 7.1 Leading AI-based Drug Discovery Service / Technology Providers in Asia Pacific
    Table 7.2 3BIGS: Company Snapshot
    Table 7.3 3BIGS: AI-based Drug Discovery Technologies
    Table 7.4 3BIGS: Recent Developments and Future Outlook
    Table 7.5 Gero: Company Snapshot
    Table 7.6 Gero: AI-based Drug Discovery Technologies
    Table 7.7 Gero: Recent Developments and Future Outlook
    Table 7.8 Insilico Medicine: Company Snapshot
    Table 7.9 Insilico Medicine: AI-based Drug Discovery Technologies
    Table 7.10 Insilico Medicine: Recent Developments and Future Outlook
    Table 7.11 KeenEye: Company Snapshot
    Table 7.12 KeenEye: AI-based Drug Discovery Technologies
    Table 7.13 KeenEye: Recent Developments and Future Outlook
    Table 8.1 AI-based Drug Discovery Service / Technology Providers: List of Partnerships and Collaborations, 2009-2022
    Table 9.1 AI-based Drug Discovery Service / Technology Providers: List of Funding and Investments, 2006-2022
    Table 10.1 Patent Analysis: Prominent CPC Symbols
    Table 10.2 Patent Analysis: Most Popular CPC Symbols
    Table 10.3 Patent Analysis: List of Top CPC Symbols
    Table 10.4 Patent Analysis: Summary of Benchmarking Analysis
    Table 10.5 Patent Analysis: Categorization based on Weighted Valuation Scores
    Table 10.6 Patent Portfolio: List of Leading Patents (in terms of Highest Relative Valuation)
    Table 10.7 Patent Portfolio: List of Leading Patents (in terms of Number of Citations)
    Table 11.1. Company Valuation Analysis: Weighted Average Score
    Table 11.2. Company Valuation Analysis: Estimated Valuation
    Table 17.1 Aigenpulse: Company Snapshot
    Table 17.2 Cloud Pharmaceuticals: Company Snapshot
    Table 17.3 DEARGEN: Company Snapshot
    Table 17.4 Intelligent Omics: Company Snapshot
    Table 17.5 Pepticom: Company Snapshot
    Table 17.6 Sage-N Research: Company Snapshot
    Table 18.1 AI-based Drug Discovery: Distribution by Year of Establishment
    Table 18.2 AI-based Drug Discovery: Distribution by Company Size
    Table 18.3 AI-based Drug Discovery: Distribution by Location of Headquarters
    (Region-Wise)
    Table 18.4 AI-based Drug Discovery: Distribution by Location of Headquarters
    (Country-Wise)
    Table 18.5 AI-based Drug Discovery: Distribution by Company Size and Location of Headquarters
    Table 18.6 AI-based Drug Discovery: Distribution by Type of Company
    Table 18.7 AI-based Drug Discovery: Distribution by Type of AI Technology
    Table 18.8 AI-based Drug Discovery: Distribution by Drug Discovery Steps
    Table 18.9 AI-based Drug Discovery: Distribution by Type of Drug Molecule
    Table 18.10. AI-based Drug Discovery: Distribution by Drug Development Initiatives
    Table 18.11 AI-based Drug Discovery: Distribution by Technology Licensing Option
    Table 18.12 AI-based Drug Discovery: Distribution by Target Therapeutic Area
    Table 18.13 AI-based Drug Discovery: Distribution by Number of Platforms / Tools
    Available
    Table 18.14 Partnerships and Collaborations: Cumulative Year-wise Trend
    Table 18.15 Partnerships and Collaborations: Distribution by Type of Partnership
    Table 18.16 Partnerships and Collaborations: Distribution by Year and Type of Partnership
    Table 18.17 Partnerships and Collaborations: Distribution by Target Therapeutic Area
    Table 18.18 Partnerships and Collaborations: Distribution by Focus Area
    Table 18.19 Partnerships and Collaborations: Distribution by Year of Partnership and Focus Area
    Table 18.20 Partnerships and Collaborations: Distribution by Type of Partner Company
    Table 18.21 Partnerships and Collaborations: Distribution by Type of Partner Company and Type of Partnerships
    Table 18.22 Most Active Players: Distribution by Number of Partnerships
    Table 18.23 Partnerships and Collaborations: Distribution of Intercontinental and Intracontinental Deals
    Table 18.24 Partnerships and Collaborations: Distribution of International and Local Deals
    Table 18.25 Funding and Investment Analysis: Cumulative Year-wise Distribution of Funding Instances, 2006-2022
    Table 18.26 Funding and Investment Analysis: Cumulative Year-wise Distribution of Amount Invested (USD Million), 2006-2022
    Table 18.27 Funding and Investment Analysis: Distribution of Instances by Type of Funding
    Table 18.28 Funding and Investment Analysis: Distribution of Amount Invested by Type of Funding (USD Million)
    Table 18.29 Funding and Investment Analysis: Distribution of Amount Invested by Year and Type of Funding (USD Million)
    Table 18.30 Funding and Investment Analysis: Distribution of Amount Invested by Company Size (USD Million)
    Table 18.31 Funding and Investment Analysis: Distribution of Funding Instances by Type of Investor
    Table 18.32 Funding and Investment Analysis: Distribution of Amount Invested by Investor (USD Million)
    Table 18.33 Most Active Players: Distribution by Number of Funding Instances
    Table 18.34 Most Active Players: Distribution by Amount Invested
    Table 18.35 Most Active Investors: Distribution by Number of Funding Instances
    Table 18.36 Funding and Investment: Distribution of Amount Invested by Region (USD Million)
    Table 18.37 Funding and Investment: Distribution of Amount Invested by Geography (Country-wise) (USD Million)
    Table 18.38 Patent Analysis: Distribution by Type of Patent
    Table 18.39 Patent Analysis: Distribution by Year of Publication
    Table 18.40 Patent Analysis: Distribution by Location of Patent Jurisdiction (Region-wise)
    Table 18.41 Patent Analysis: Distribution by Location of Patent Jurisdiction (Country-wise)
    Table 18.42 Patent Analysis: Distribution by CPC Symbols
    Table 18.43 Patent Analysis: Emerging Focus Area
    Table 18.44 Patent Analysis: Cumulative Year-wise Distribution by Type of Applicant
    Table 18.45 Leading Industry Players: Distribution by Number of Patents
    Table 18.46 Leading Non-Industry Players: Distribution by Number of Patents
    Table 18.47 Leading Patent Assignees: Distribution by Number of Patents
    Table 18.48 Patent Benchmarking: Distribution of Leading Industry Player by Patent Characteristics (CPC Symbols)
    Table 18.49 Patent Analysis: Distribution by Age
    Table 18.50 Patent Analysis: Patent Valuation
    Table 18.51 Company Valuation Analysis: Categorization by Employee Score
    Table 18.52 Company Valuation Analysis: Categorization by Experience Score
    Table 18.53 Company Valuation Analysis: Categorization by Portfolio Score
    Table 18.54 Company Valuation Analysis: Categorization by Partnerships Score
    Table 18.55 Company Valuation Analysis: Categorization by Funding Score
    Table 18.56 Company Valuation Analysis: Categorization by Weighted Average Score
    Table 18.57 Overall Cost Saving Potential Associated with Use of AI-based Solutions in Drug Discovery, 2022-2035 (USD Million)
    Table 18.58 Likely Cost Savings: Distribution by Drug Discovery Steps, 2022-2035
    (USD Million)
    Table 18.59 Likely Cost Savings During Target Identification / Validation, 2022-2035
    (USD Million)
    Table 18.60 Likely Cost Savings During Hit Generation / Lead Identification, 2022-2035 (USD Million)
    Table 18.61 Likely Cost Savings During Lead Optimization, 2022-2035 (USD Million)
    Table 18.62 Likely Cost Savings: Distribution by Target Therapeutic Area, 2022-2035 (USD
    Million)
    Table 18.63 Likely Cost Savings for Drugs Targeting Oncological Disorders, 2022-2035 (USD Million)
    Table 18.64 Likely Cost Savings for Drugs Targeting Neurological Disorders, 2022-2035 (USD Million)
    Table 18.65 Likely Cost Savings for Drugs Targeting Infectious Diseases, 2022-2035 (USD Million)
    Table 18.66 Likely Cost Savings for Drugs Targeting Respiratory Disorders, 2022-2035 (USD Million)
    Table 18.67 Likely Cost Savings for Drugs Targeting Cardiovascular Disorders, 2022-2035 (USD Million)
    Table 18.68 Likely Cost Savings for Drugs Targeting Endocrine Disorders, 2022-2035 (USD Million)
    Table 18.69 Likely Cost Savings for Drugs Targeting Gastrointestinal Disorders, 2022-2035 (USD Million)
    Table 18.70 Likely Cost Savings for Drugs Targeting Musculoskeletal Disorders, 2022-2035 (USD Million)
    Table 18.71 Likely Cost Savings for Drugs Targeting Immunological Disorders, 2022-2035 (USD Million)
    Table 18.72 Likely Cost Savings for Drugs Targeting Dermatological Disorders, 2022-2035 (USD Million)
    Table 18.73 Likely Cost Savings for Drugs Targeting Other Disorders, 2022-2035
    (USD Million)
    Table 18.74 Likely Cost Savings: Distribution by Geography, 2022-2035 (USD Million)
    Table 18.75 Likely Cost Savings in North America, 2022-2035 (USD Million)
    Table 18.76 Likely Cost Savings in Europe, 2022-2035 (USD Million)
    Table 18.77 Likely Cost Savings in Asia Pacific, 2022-2035 (USD Million)
    Table 18.78 Likely Cost Savings in MENA, 2022-2035 (USD Million)
    Table 18.80 Likely Cost Savings in Latin America, 2022-2035 (USD Million)
    Table 18.81 Likely Cost Savings in Rest of the World, 2022-2035 (USD Million)
    Table 18.82 Global AI-based Drug Discovery Market, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.83 AI-based Drug Discovery Market: Distribution by Drug Discovery Steps,
    2022-2035 (USD Million)
    Table 18.84 AI-based Drug Discovery Market for Target Identification / Validation, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.85 AI-based Drug Discovery Market for Hit Generation / Lead Identification, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.86 AI-based Drug Discovery Market for Lead Optimization, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.87 AI-based Drug Discovery Market: Distribution by Target Therapeutic Area,
    2022-2035 (USD Million)
    Table 18.88 AI-based Drug Discovery Market for Oncological Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.89 AI-based Drug Discovery Market for Neurological Disorders Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.90 AI-based Drug Discovery Market for Infectious Diseases, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.90 AI-based Drug Discovery Market for Respiratory Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.91 AI-based Drug Discovery Market for Cardiovascular Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.92 AI-based Drug Discovery Market for Endocrine Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.93 AI-based Drug Discovery Market for Gastrointestinal Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.94 AI-based Drug Discovery Market for Musculoskeletal Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.95 AI-based Drug Discovery Market for Immunological Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.96 AI-based Drug Discovery Market for Dermatological Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.97 AI-based Drug Discovery Market for Other Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.98 AI-based Drug Discovery Market: Distribution by Geography, 2022-2035 (USD Million)
    Table 18.99 AI-based Drug Discovery Market in North America, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.100 AI-based Drug Discovery Market in the US, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.101 AI-based Drug Discovery Market in Canada, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.102 AI-based Drug Discovery Market in Europe, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.103 AI-based Drug Discovery Market in UK, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.104 AI-based Drug Discovery Market in France, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.105 AI-based Drug Discovery Market in Germany, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.106 AI-based Drug Discovery Market in Spain, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.107 AI-based Drug Discovery Market in Italy, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.108 AI-based Drug Discovery Market in Rest of Europe, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.109 AI-based Drug Discovery Market in Asia Pacific, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.110 AI-based Drug Discovery Market in China, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.111 AI-based Drug Discovery Market in India, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.112 AI-based Drug Discovery Market in Japan, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.113 AI-based Drug Discovery Market in Australia, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.114 AI-based Drug Discovery Market in South Korea, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.115 AI-based Drug Discovery Market in MENA, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.116 AI-based Drug Discovery Market in Saudi Arabia, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.117 AI-based Drug Discovery Market in UAE, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.118 AI-based Drug Discovery Market in Iran, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.119 AI-based Drug Discovery Market in Latin America, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.120 AI-based Drug Discovery Market in Argentina, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    Table 18.121 AI-based Drug Discovery Market in Rest of the World, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
    LIST OF COMPANIES AND ORGANIZATIONS
    The following companies and organizations have been mentioned in the report.
    1. 3BIGS
    2. 3W Healthcare Fund
    3. 3W Partners
    4. 4B Technologies
    5. 5Y Capital
    6. 6 Dimensions Capital
    7. 8VC
    8. 99andBeyond
    9. A2A Pharmaceuticals
    10. A2i Therapeutics
    11. AARP Foundation
    12. AbbVie
    13. AbCellera
    14. Absci
    15. Abstract Ventures
    16. Accelerate Long Island
    17. Accenture
    18. Accutar Biotech
    19. Acellera
    20. Acequia Capital
    21. Acerand Therapeutics
    22. ACF Investors
    23. AcuraStem
    24. Adagene
    25. Adare Pharma Solutions
    26. ADC Therapeutics
    27. ADEL
    28. adMare BioInnovations
    29. Advantage Capital
    30. AdventHealth
    31. Adynxx
    32. Aganitha
    33. Agent Capital
    34. AI Therapeutics
    35. Ai Vedam Technologies
    36. AI VIVO
    37. Ai-biopharma
    38. Aiforia
    39. Aigenpulse
    40. Air Street Capital
    41. Ajou University
    42. Akashi Therapeutics
    43. Aktia Nordic Micro Cap
    44. Albany Molecular Research
    45. A-Level Capital
    46. Alexandria Real Estate Equities
    47. Alexandria Venture Investments
    48. Allcyte
    49. Allergan
    50. AllianThera Biopharma
    51. Allosteric Bioscience
    52. Almirall
    53. Alphanosos
    54. ALS Association
    55. ALS Investment Fund
    56. Altos Ventures
    57. Amadeus Capital Partners
    58. AME Cloud Ventures
    59. Amgen Ventures
    60. Amidi
    61. Ampel Biosolutions
    62. Amplify Partners
    63. Amplitude
    64. Amyloid Solution
    65. Anagenesis Biotechnologies
    66. Andreessen Horowitz
    67. Angios
    68. Anima Biotech
    69. Ansa Biotechnologies
    70. Antengene
    71. Antiverse
    72. Apeiron Therapeutics
    73. ApexQubit
    74. APRINOIA Therapeutics
    75. Aqemia
    76. Arbutus Biopharma
    77. ARCH Venture Partners
    78. Arctoris
    79. Ardigen
    80. Aria Pharmaceuticals
    81. Arpeggio
    82. ArrowMark Partners
    83. ARTIS Ventures
    84. Arzeda
    85. Asset Management Ventures
    86. Astellas Pharma
    87. Astia
    88. AstraZeneca
    89. Astrogen
    90. atai Life Sciences
    91. Ataxia
    92. Atinum Invest­ment
    93. Atlantic Labs
    94. Atlas Venture
    95. Atomico
    96. Atomwise
    97. Atrius Health
    98. AUM Biosciences
    99. Auransa
    100. Aurinvest
    101. Autism Impact Fund
    102. AV8 Ventures
    103. AVIC Trust
    104. Avidity Partners
    105. B Capital Group
    106. BABEL Ventures
    107. Baidu Ventures
    108. Balderton Capital
    109. Bangarang
    110. BASSETTI
    111. Baupost
    112. Bavarian Nordic
    113. Bayer
    114. Beiersdorf
    115. BenevolentAI
    116. BERG
    117. Better Ventures
    118. Bezos Expeditions
    119. BigHat Biosciences
    120. Bill & Melinda Gates Foundation
    121. BioAge Labs
    122. Bioeconomy Capital
    123. BioFocus DPI
    124. Biogen
    125. BioLizard
    126. Biolojic Design
    127. BioMarin Pharmaceutical
    128. Biomea Fusion
    129. BioMotiv
    130. BioPharmics
    131. Biorelate
    132. Biortus
    133. Bios Partners
    134. BioSymetrics
    135. bioSyntagma
    136. biotx.ai
    137. BioVentures MedTech Funds
    138. Bioverge
    139. Biovista
    140. BioXcel Therapeutics
    141. BlackRock
    142. Bloomberg Beta
    143. Blue Bear Ventures
    144. Blue Oak Pharmaceuticals
    145. bluebird bio
    146. Boehringer Ingelheim
    147. Bold Capital Partners
    148. Bpifrance
    149. Brace Pharma Capital
    150. Brain Canada
    151. Breakout Labs
    152. BridgeBio Pharma
    153. Brigham and Women's Hospital
    154. Brightspark Ventures
    155. Bristol Myers Squibb
    156. btov
    157. Buck Institute for Research on Aging
    158. Builders VC
    159. Bulba Ventures
    160. Busolantix Investment
    161. BVF Partners
    162. C4X Discovery
    163. CaaS Capital Management
    164. Caffeinated Capital
    165. Califia Pharma
    166. California Institute for Biomedical Research
    167. California Institute for Regenerative Medicine
    168. CALYM Carnot Institute
    169. Cambia Health Solutions
    170. Cambridge Angels
    171. Cambridge Cancer Genomics
    172. Cambridge Crystallographic Data Center
    173. Cambridge Enterprise
    174. Cambridge Research for International Research
    175. Cambridge University Hospitals NHS Foundation Trust
    176. Cantos Ventures
    177. CARB-X
    178. CaroCure
    179. Casdin Capital
    180. Catalio Capital Management
    181. Catapult Ventures
    182. Cathay Innovation
    183. Causaly
    184. CB Clean Lux
    185. CDH Investments
    186. Celgene
    187. Cellarity
    188. Celsius Therapeutics
    189. Center for the Development of Industrial Technology (CDTI)
    190. CENTOGENE
    191. Cerebras
    192. Cerevel Therapeutics
    193. Charcot–Marie–Tooth
    194. Charles River Laboratories
    195. Chartered Group
    196. ChemAlive
    197. Chemaxon
    198. ChemDiv
    199. ChemPass
    200. Chiesi Farmaceutici
    201. Children's Tumor Foundation (CTF)
    202. China Canada Angels Alliance
    203. China Life Healthcare Fund
    204. China Oncology Focus
    205. Chinese Academy of Medical Sciences
    206. Cigna Ventures
    207. City Hill Ventures
    208. Civilization Ventures
    209. CJ HEALTHCARE
    210. Claremont Creek Ventures
    211. Clarus Ventures
    212. Cleveland Clinic
    213. CLI Ventures
    214. Cloud Pharmaceuticals
    215. CM-CIC Innovation
    216. CMT Research Foundation (CMTRF)
    217. Coatue
    218. Code Ocean
    219. Collaborations Pharmaceuticals
    220. Collaborative Drug Discovery
    221. Collective Scientific
    222. Colorcon Ventures
    223. Colt Ventures
    224. Computational biology
    225. Congressionally Directed Medical Research Programs (CDMRP)
    226. Conifer Point Pharmaceuticals
    227. Cormorant Asset Management
    228. Cortex Discovery
    229. Cosine
    230. Cota Capital
    231. Courier Therapeutics
    232. COVID-19 Vaccine Corporation (CVC)
    233. CPE
    234. CPP Investments
    235. CQDM
    236. Creative Destruction Lab
    237. Cresset
    238. CRV
    239. CrystalGenomics
    240. CTI Life Sciences Fund
    241. Cultivian Sandbox Ventures
    242. Cyclica
    243. CytoReason
    244. Daewoong Pharmaceuticals
    245. Daily Partners
    246. Danhua Venture Capital (DHVC)
    247. Dante Labs
    248. Data2Discovery
    249. Data4cure
    250. Datavant
    251. DCVC
    252. Deargen
    253. Debiopharm
    254. Deep Genomics
    255. Deep Knowledge Ventures
    256. Deep Track Capital
    257. DeepCure
    258. DeepMatter
    259. DeepTrait
    260. Deerfield Management
    261. DEFTA Partners
    262. Delin Ventures
    263. Denali Therapeutics
    264. Denovicon Therapeutics
    265. Denovium
    266. Department of Health and Social Care (DHSC)
    267. Development Bank of Wales
    268. DEXSTR
    269. Diamond Light Source
    270. Dolby Family Ventures
    271. Dow AgroSciences
    272. Driehaus Capital Management
    273. Drive Capital
    274. Droia Ventures
    275. Drugs for Neglected Diseases initiative (DNDi)
    276. dRx Capital
    277. DSC Investment
    278. Dualogics
    279. Dynamk Capital
    280. Dyno Therapeutics
    281. Echo Health Ventures
    282. EcoR1 Capital
    283. EDBI
    284. Edge Capital
    285. eFlasks
    286. EIC Accelerator
    287. Eight Roads Ventures
    288. EIT Climate-KIC
    289. Elevian
    290. Eli Lilly
    291. Elsevier
    292. Elucidata
    293. Empire State Development (ESD)
    294. Empirico
    295. Enamine
    296. Endogena Therapeutics
    297. Endure Capital
    298. Engine Biosciences
    299. Enterprise Ireland
    300. Envisagenics
    301. Epic Capital Management
    302. EPIC Ventures
    303. Epicombi Therapeutics
    304. Epredia
    305. EQRx
    306. Erasca
    307. e-therapeutics
    308. Euretos
    309. European Bank for Reconstruction and Development (EBRD)
    310. European Investment Bank
    311. European Union
    312. Eurostars
    313. Evaxion Biotech
    314. Everest Medicines
    315. Evotec
    316. Ewha Womans University
    317. Excelra
    318. Executive Agency for Small and Medium-sized Enterprises (EASME)
    319. Exelixis
    320. Exscientia
    321. Facio Therapies
    322. Farallon Capital
    323. Federal Economic Development Agency for Southern Ontario
    324. Felicis Ventures
    325. Ferring Pharmaceuticals
    326. Fidelity Asia Fund
    327. Fidelity Biosciences
    328. Fidelity Management & Research Company
    329. Financière Boscary
    330. FinLab EOS VC
    331. First Round Capital
    332. First Star Ventures
    333. Flagship Pioneering
    334. Foresite Capital
    335. Forma Therapeutics
    336. Formic Ventures
    337. Foundation for Angelman Syndrome Therapeutics (FAST)
    338. Founders Factory
    339. Founders Fund
    340. Fountain Therapeutics
    341. Fox Chase Cancer Center
    342. F-Prime Capital (Formerly known as Fidelity Biosciences)
    343. Frazier Life Sciences
    344. Frees Fund
    345. Friedreich’s Ataxia Research Alliance (FARA)
    346. Frontier Medicines
    347. FundersClub
    348. Future Ventures
    349. FutuRx
    350. G3 Therapeutics
    351. Galapagos
    352. Gaorong Capital
    353. Garage Capital
    354. Gatehouse Bio
    355. GC Pharma
    356. Genentech
    357. General Atlantic
    358. General Catalyst
    359. Genesen
    360. Genesis Therapeutics
    361. GenFleet Therapeutics
    362. Genialis
    363. GenoKey ApS
    364. Genomatica
    365. Genome Biologics
    366. Genomics England
    367. Genuity Science
    368. Gero
    369. Gi Global Health Fund LP
    370. Gilead Sciences
    371. GlaxoSmithKline
    372. Global Brain
    373. Global Founders Capital
    374. Global Genomics Series
    375. GM&C Life Sciences Fund
    376. GNS Healthcare
    377. Golden Ventures
    378. Goodman Capital
    379. Google
    380. Gopher Asset Management
    381. Gordian
    382. Government of Canada
    383. Government of Switzerland
    384. GP Healthcare Capital
    385. GPG Ventures
    386. Grand Challenges Canada
    387. GrayMatter
    388. Great Ormond Street Hospital
    389. Greater Paris University Hospitals - AP-HP
    390. Green Park & Golf Ventures
    391. GreenSky Capital
    392. Gritstone Oncology
    393. Grove Ventures
    394. GT Healthcare Capital Partners
    395. Guangdong Institute of Microbiology
    396. Gustave Roussy
    397. GV (formerly known as Google Ventures)
    398. Hafnium Labs
    399. Hampshire Hospitals NHS Foundation Trust
    400. Hana Ventures
    401. Hanmi Pharmaceutical
    402. Hansoh Pharma
    403. Harbour Antibodies
    404. Harbour BioMed
    405. Harris & Harris
    406. Harvard Innovation Labs
    407. Haystack Science
    408. HBM Health­care Invest­ments
    409. HCS Pharma
    410. Health Wildcatters
    411. HealthInc
    412. Healx
    413. Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)
    414. Heritage Provider Network
    415. HEWLETT PACKARD
    416. Hibiskus BioPharma
    417. Hike Ventures
    418. Hinge Therapeutics
    419. Hiventures
    420. HLTH
    421. HOF Capital
    422. HotSpot Therapeutics
    423. Hoxton Ventures
    424. Huadong Medicine
    425. Human Capital
    426. Humonic
    427. Huons
    428. Hyperplane Venture Capital
    429. IA Ventures
    430. IBM Research
    431. IBM TechU
    432. ICHOR
    433. IDG Capital
    434. Iktos
    435. IMM Investment
    436. ImmuMap Services
    437. ImmuneMed
    438. Immunocure
    439. Impact Therapeutics
    440. Index Ventures
    441. IndieBio
    442. Indivumed
    443. Infinity Medical
    444. InfoChem (acquired by DeepMatter)
    445. InnoPharmaScreen
    446. Innophore
    447. Innoplexus
    448. Innospark Ventures
    449. Innova31
    450. Innovate NY Fund
    451. Innovate UK
    452. Innovation Endeavors
    453. Innovation Fund Denmark
    454. Innovative Medicines Initiative (IMI)
    455. Inovia Capital
    456. inSili.com
    457. Insiliance
    458. Insilico Medicine
    459. insitro
    460. Institut Pasteur
    461. Intel Capital
    462. Intellegens
    463. IntelliCyt
    464. Intelligent OMICS
    465. Intermountain Ventures
    466. Interprotein
    467. Intuition Systems
    468. InveniAI
    469. Invetx
    470. Invus
    471. Ionis Pharmaceuticals
    472. IP Group
    473. IPF Partners
    474. IQVIA
    475. Ireland Strategic Investment Fund (ISIF)
    476. IRICoR
    477. Iris Pharma
    478. Irving Investors
    479. I-STEM
    480. IT-translation
    481. IvyCap Ventures
    482. IWAKI SEIYAKU
    483. JADBio
    484. Janssen
    485. Jefferson Health
    486. Jiangsu Chia Tai Fenghai Pharmaceutical (CTFH)
    487. Jiangsu Hengrui Pharmaceuticals
    488. JLABS
    489. Johnson & Johnson
    490. Johnson & Johnson Innovation
    491. Jove Equity Partners
    492. Juvena Therapeutics
    493. Juvenescence
    494. JW Pharmaceutical
    495. K Cube Ventures
    496. Kadmon
    497. Kaiser Permanente
    498. KB Securities
    499. KDB Capital
    500. Kebotix
    501. Keen Eye
    502. Keio University
    503. KemPharm
    504. Kennedy Krieger Institute
    505. Kester Capital
    506. Khosla Ventures
    507. Kindred
    508. Kinetic Discovery
    509. King Star Capital
    510. King's College London
    511. Kinogen
    512. Koch Dis­rup­tive Tech­nolo­gies
    513. Kodiak Sciences
    514. Korea Atomic Energy Research Institute
    515. Korea Development Bank
    516. Korea Fixed-Income Investment Advisory
    517. Korea Investment Partners
    518. Korea Research Institute of Chemical Technology
    519. ksilink
    520. KTB Network
    521. Kuano
    522. Kyowa Kirin
    523. La Financiere Gaspard
    524. Labcyte
    525. LabGenius
    526. LabKey
    527. Lake Bleu Capital
    528. Lansdowne Partners
    529. Lantern Pharma
    530. LanzaTech
    531. Laurent and Benon
    532. Laurion Capital Management
    533. Lawrence Livermore National Laboratory
    534. Laxai Life Sciences
    535. LB Investment
    536. Leaps by Bayer
    537. Legend Capital
    538. Leland Stanford Junior University
    539. LEO Pharma
    540. Lequio Pharma
    541. Lhasa
    542. Life Sciences Institute
    543. LifeArc
    544. Lifeforce Capital
    545. LifeSci NYC
    546. LifeSci Venture Partners
    547. Lightspeed Venture Partners
    548. Lilly Asia Ventures
    549. Linguamatics
    550. Lodo Therapeutics
    551. Long Island Emerging Technologies Fund (LIETF)
    552. Longevity Fund
    553. Loup Ventures
    554. LSP
    555. Luminous Ventures
    556. Lundbeck
    557. Lux Capital
    558. Luxembourg Centre for Systems Biomedicine (LCSB)
    559. M12 - Microsoft's Venture Fund
    560. MAbSilico
    561. Macroceutics
    562. Madrona Venture
    563. Magnetic Ventures
    564. Maison Capital
    565. Manchester Tech Trust Angels
    566. Mannin Research
    567. Maple Capital Management
    568. Marathon Venture Capital
    569. MaRS Catalyst Fund
    570. Marshall Wace
    571. Maruho
    572. Massachusetts Institute of Technology
    573. Massachusetts Life Sciences Center
    574. MassBiologics
    575. MassChallenge
    576. Maxygen
    577. MBC BioLabs
    578. Medchemica
    579. Medirita
    580. Memorial Sloan Kettering Cancer Center
    581. Menlo Ventures
    582. Menten AI
    583. Merck
    584. Merck Accelerator
    585. Merck KGaA
    586. Mercury Fund
    587. Meridian Street Capital
    588. Micar Innovation
    589. Michigan State University
    590. Microsoft
    591. Mila
    592. Mind the Byte
    593. Mirae Assest Venture Investment
    594. Mirae Asset Cap­i­tal Markets
    595. Mission: Cure
    596. MIT delta v
    597. Mitsui
    598. Model Medicines
    599. Moderna
    600. Molecular Health
    601. Molecule
    602. Molecule.one
    603. Moleculomics
    604. Molomics
    605. Monashee Investment Management
    606. Monsanto Growth Ventures
    607. Morningside
    608. Morpheus Ventures
    609. MPM Capital
    610. MRL Ventures Fund
    611. Mubadala
    612. Multiple Myeloma Research Foundation (MMRF)
    613. Muscular Dystrophy Association
    614. Mutuelle d'Assurances du Corps de Sante Francais (MACSF)
    615. myTomorrows
    616. Nan Fung Life Sciences
    617. Nanna Therapeutics
    618. Nantes University Hospital
    619. National Cancer Institute (NCI)
    620. National Center for Advancing Translational Sciences ( NCATS )
    621. National Centre for Research and Development
    622. National Fertilizers
    623. National Institute of Environmental Health Sciences (NIEHS)
    624. National Institute of General Medical Sciences (NIGMS)
    625. National Institute of Health (NIH)
    626. National Institute of Neurological Disorders and Stroke (NINDS)
    627. National Institute of Standards and Technology (NIST)
    628. National Institute on Aging (NIA)
    629. National Institutes of Health
    630. National Research Council Canada
    631. National Science Foundation (NSF)
    632. National Science Foundation Small Business Innovation Research (NSF SBIR)
    Program
    633. Nektar Therapeutics
    634. Nest.Bio Ventures
    635. Nestlé
    636. Netabolics
    637. Neuropore Therapies
    638. NeuroTheryX
    639. Neuroventi
    640. New Protein Capital
    641. New Wave Ventures
    642. New World TMT
    643. New York Medical College
    644. New York Ventures
    645. NewDo Venture
    646. Nex Cubed
    647. NineteenGale Therapeutics
    648. NJF Capital
    649. Nonacus
    650. Northern Powerhouse Investment Fund
    651. Notable Labs
    652. Novartis
    653. Novo Holdings
    654. Novo Nordisk
    655. Nucleai
    656. Numedii
    657. Numerate
    658. Nuritas
    659. NVIDIA
    660. O2H Ventures
    661. Oak Ridge National Laboratory
    662. Obvious Ventures
    663. OCA Ventures
    664. OccamzRazor
    665. Octopus Ventures
    666. Olaris
    667. OMNY Health
    668. OncoArendi Therapeutics
    669. Oncologie
    670. Oncology Venture (Acquired by Allarity Therapeutics)
    671. OneThree Biotech
    672. Ono Pharmaceutical
    673. Optibrium
    674. Optum Ventures
    675. OrbiMed
    676. OS Fund
    677. OSE Immunotherapeutics
    678. OSEO
    679. OurCrowd
    680. Overkill Ventures
    681. OVP Venture Partners
    682. Owkin
    683. Oxford Drug Design
    684. Oxford University
    685. Pacific Western Bank
    686. Panache Ventures
    687. PAQ Therapeutics
    688. Parinvest
    689. Parker Institute for Cancer Immunotherapy
    690. Parkinson’s UK
    691. ParticleX
    692. Partner Fund Management
    693. Pavilion Capital
    694. PEACCEL
    695. Pear VC
    696. Pending.AI
    697. Pentech Ventures
    698. Pepticom
    699. Peptilogics
    700. Peptone
    701. Peptris Technologies
    702. PercayAI
    703. Perceptive Advisors
    704. Pfizer
    705. Pfizer Venture Investments
    706. Pharmacelera
    707. Pharmavite
    708. PharmCADD
    709. PharmEnable
    710. Pharnext
    711. Pharos iBio
    712. Phenomic AI
    713. Phi-X Capital
    714. Phoenix Venture Partners
    715. PhoreMost
    716. Pi Campus
    717. PICC Capital
    718. Pictet
    719. Pikas
    720. Pivotal bioVenture Partners
    721. Plex
    722. Plug and Play Tech Center
    723. Polaris Partners
    724. Polaris Quantum Biotech
    725. Polyclone
    726. Polycystic Kidney Disease Charity
    727. Porton
    728. PostEra
    729. PrecisionLife
    730. Predictive Oncology
    731. Prefix Capital
    732. President International Development
    733. Presight Capital
    734. Primary Venture Partners
    735. Primavera Capital
    736. Prime Movers Lab
    737. Primordial Genetics
    738. PROSILICO
    739. ProteinQure
    740. ProteiQ Biosciences
    741. Pub­lic Sec­tor Pen­sion Invest­ment Board (PSP Invest­ments)
    742. QIAGEN
    743. Qiming Venture Partners
    744. Quantitative Medicine
    745. Quiet Capital
    746. QuLab
    747. RA Capital Management
    748. Radical ventures
    749. Ramen Ventures
    750. RaQualia Pharma
    751. Real Ventures
    752. RealHealthData
    753. Receptor.AI
    754. Recursion Pharmaceuticals
    755. Red Cell Partners
    756. Redalpine
    757. Redbiotec
    758. Redmile Group
    759. Redpoint Ventures
    760. Refactor Capital
    761. Regional Cancer Centre (RCC)
    762. Regional Council of Auvergne-Rhône-Alpes
    763. Rejuveron Life Sciences
    764. Relation Therapeutics
    765. Relay Therapeutics
    766. RELIEF THERAPEUTICS
    767. Remedium AI
    768. Reneo Capital
    769. Renren
    770. Repare Therapeutics
    771. REPROCELL
    772. Repurpose.AI
    773. Research Triangle Park
    774. Resonant Therapeutics
    775. Revelation Partners
    776. Reverie Labs
    777. ReviveMed
    778. Ridgeback Capital Investments
    779. Rigetti Computing
    780. Rising Tide Fund
    781. Rivas Capital
    782. Roche
    783. Rock Springs Capital
    784. Romulus Capital
    785. Rough Draft Ventures
    786. RRJ Capital
    787. RT Partners
    788. RwHealth
    789. Ryerson University
    790. Saehan Venture Capital
    791. Sage Partners
    792. Sage-N Research
    793. Saint Louis University
    794. Samsara BioCapital
    795. Samyang
    796. Sanabil Investments
    797. Sanford Burnham Prebys
    798. Sanofi
    799. Santen Pharmaceutical
    800. Sapir Venture Partners
    801. Sarepta Therapeutics
    802. SARomics Biostructures
    803. Saverna Therapeutics
    804. SciFi Technology Systems
    805. Scottish Mortgage Investment Trust
    806. Scripps Research
    807. SD Biosensor
    808. Sea Lane Ventures
    809. Searchbolt
    810. Sedec Therapeutics
    811. Selvita
    812. Sema4
    813. SEngine Precision Medicine
    814. Sensyne Health
    815. Sentauri
    816. Sequoia Capital
    817. Seraph Group
    818. Serra Ventures
    819. Servier
    820. Seventure Partners
    821. Sheba
    822. Sheffield Institute
    823. Shionogi
    824. Shivom
    825. Shuimu BioSciences
    826. Sidney Kimmel Comprehensive Cancer Center
    827. SIG
    828. SignalChem Lifesciences
    829. Signet Therapeutics
    830. Silexon
    831. Sinequa
    832. Singleron Biotechnologies
    833. Sino Biopharmaceutical
    834. Sinopia Biosciences
    835. Sinovation Ventures
    836. Sirenas
    837. SK Biopharmaceuticals
    838. SK Chemicals
    839. Small Business Innovation Research (SBIR)
    840. Smilegate Investment
    841. Socium
    842. Sofinnova Partners
    843. SoftBank Ventures
    844. SoftBank Vision Fund
    845. Solasta Ventures
    846. Solve ME/CFS Initiative
    847. SOM Biotech
    848. Sookmyung Women’s University
    849. Sosei Heptares
    850. SOSV
    851. SparkBeyond
    852. Sparta
    853. Spektron Systems
    854. Sphera Funds Management
    855. Spring Discovery
    856. SR One
    857. SR One Capital Management
    858. Sravathi AI
    859. SRI International
    860. Stage Venture Partners
    861. Standigm
    862. Stanford University School of Medicine
    863. StarFinder Capital Fund
    864. Startupbootcamp
    865. StartX
    866. STEM&More
    867. StemoniX
    868. Stonehaven
    869. Structura Biotechnology
    870. Sunfish Partners
    871. Sunwest Bank
    872. Susa Ventures
    873. Sustainable Conversion Ventures
    874. SV Angel
    875. SyndicateRoom
    876. SYNSIGHT
    877. Syntekabio
    878. Synthelis
    879. SYSTEMS ONCOLOGY
    880. T. Rowe Price
    881. Tachyon
    882. Taisho Pharmaceutical
    883. Takeda
    884. Tanabe Research Laboratories
    885. Tanarra Credit Partners
    886. TARA Biosystems
    887. Tasly Biopharmaceuticals
    888. Tavistock Group
    889. TB Alliance
    890. Team Builder Ventures
    891. Tech Incubation Program for Startup (TIPS)
    892. Techammer
    893. Tekla Capital Management
    894. Temasek
    895. Tencent
    896. TenOneTen ventures
    897. Tensor Ventures
    898. Terra Magnum Capital Partners
    899. Terray Therapeutics
    900. TeselaGen
    901. Teva Pharmaceutical
    902. TF Bioinformatics
    903. The BioCollective
    904. The Column Group
    905. The Cure Parkinson’s Trust (CPT)
    906. The Edge Software Consultancy
    907. The Heritage Group
    908. The Institute of Cancer Research
    909. The Johns Hopkins University School of Medicine
    910. The Michael J. Fox Foundation for Parkinson's Research (MJFF)
    911. The Pritzker Organization
    912. The Royal Wolverhampton NHS Trust
    913. The Syndicate
    914. Third Kind Venture Capital (3KVC)
    915. Third Rock Ventures
    916. Three Lakes Partners
    917. Tillotts Pharma
    918. Timewise Investment
    919. TLV Partners
    920. Top Technology Ventures
    921. Topspin Fund
    922. Toyohashi University of Technology
    923. Transcriptic
    924. Transilico
    925. Trinitas Capital Management
    926. True Ventures
    927. Truffle Capital
    928. TS Investment
    929. TSVC
    930. Turbine
    931. Twin Ventures
    932. Two Sigma Ventures
    933. U.S. Securities and Exchange Commission
    934. UCB Pharma
    935. Uncork Capital
    936. United States Department of Health and Human Services
    937. Universal Materials Incubator
    938. University College London
    939. University Hospital Institute Méditerranée Infection
    940. University of Barcelona
    941. University of California San Diego
    942. University of Cambridge
    943. University of Chicago
    944. University of Connecticut
    945. University of Dundee
    946. University of Florida
    947. University of Groningen
    948. University of Kentucky
    949. University of Leeds
    950. University of Manitoba
    951. University of Miami
    952. University of Michigan College of Pharmacy
    953. University of Milan
    954. University of Minnesota
    955. University of Nottingham
    956. University of Oxford
    957. University of Pittsburgh
    958. University of Toronto
    959. University of Wisconsin-Milwaukee Research Foundation
    960. University of North Carolina
    961. Unnatural Products
    962. UPPthera
    963. Upsher Smith
    964. Usynova Pharmaceuticals
    965. Valence Discovery
    966. Valo
    967. VantAI
    968. Vector Institute
    969. Vectr Ventures
    970. Verge Genomics
    971. VeriSIM
    972. Versant Ventures
    973. Vertex Ventures
    974. VIB
    975. Viking Global Investors
    976. Village Global
    977. Vingyani
    978. VisVires New Protein
    979. Vium
    980. Viva BioInnovator
    981. Vyant Bio (Formerly known as Cancer Genetics)
    982. Warburg Pincus
    983. Watson Foundation
    984. Wave
    985. West Lake Pharmaceutical Services
    986. Wheatsheaf
    987. WI Harper
    988. Wild Basin Investments
    989. Wisecube
    990. Woodford Investment Management
    991. Woodline Partners
    992. WorldQuant Ventures
    993. Wren Capital
    994. WRF Capital
    995. WuXi AppTec
    996. WuXi Biologics
    997. WuXi Healthcare Ventures
    998. Wuyuan Capital
    999. X-37
    1000. Xbiome
    1001. X-Chem
    1002. XenoTherapeutics
    1003. XtalPi
    1004. Y Combinator
    1005. Yael Capital
    1006. Yahui Investment
    1007. Yale School of Medicine
    1008. Yayi Capital
    1009. YiMei Capital
    1010. YITU Technology
    1011. Yonsei University College of Medicine
    1012. YOZMA GROUP KOREA
    1013. Yuhan Corporation
    1014. Yunfeng Capital
    1015. Zastra
    1016. ZebiAI
    1017. ZhenFund
    1018. Zymergen

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