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Market Research Report

AI-based Drug Discovery Market: Focus on Deep Learning and Machine Learning, 2020-2030

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AI-based Drug Discovery Market: Focus on Deep Learning and Machine Learning, 2020-2030
Published: January 31, 2021 Content info: 294 Pages
Description

Overview:

The drug discovery process, which includes the identification of a relevant biological target and a corresponding pharmacological lead, is crucial to the clinical success of a drug candidate. Considering the growing complexity of modern pharmacology, the discovery of viable therapeutic candidates is very demanding, both in terms of capital investment and time. In fact, according to a study conducted by Tufts Center for the Study of Drug Development, it was estimated that a prescription drug requires around 10 years and over USD 2.5 billion in capital investment, while traversing from the bench to the market. Around one-third of the aforementioned expenditure is incurred during the drug discovery phase alone. Moreover, it is well-known that only a small proportion of pharmacological leads identified during the discovery stages are actually translated into viable product candidates for clinical studies. Currently, experts believe that close to 90% of the product candidates fail to make it past the clinical stage of development. This high attrition rate has long been attributed to the legacy drug discovery process, which is more of a trial-and-error paradigm. In attempts to address the concerns associated with rising capital requirements in drug discovery, and prevent late stage failure of drug development programs, stakeholders in the pharmaceutical industry are currently exploring the implementation of Artificial Intelligence (AI) based tools in order to better inform drug development operations using available chemical and biological data.

Over time, AI-based tools have been gradually deployed across various processes, including drug discovery, within the healthcare sector. The predictive power of AI is primarily based on the processing and analysis of large volumes of clinical / medical data, which is now being leveraged to better inform modern drug discovery efforts. In this context, deep learning algorithms have demonstrated the ability to cross-reference published scientific literature (structured data) with electronic health records available in public medical data banks and clinical trial information (unstructured data), in order to generate actionable insights for target identification, hit generation and lead optimization. In other words, the use of AI-enabled technologies in drug discovery operations is likely to not only improve overall R&D productivity, but also reduce clinical failure with accurate predictions of a drug candidate's safety and efficacy during the early stages of product development. Although the adoption of such advanced tools is still limited in mainstream drug development programs, it is worth mentioning that, in the last five years alone, close to USD 5 billion was invested into companies that are developing AI-based solution for drug discovery applications. Interestingly, ~50% of the aforementioned amount was invested in the last two years. Therefore, we are led to believe that the opportunity for stakeholders in this niche, but upcoming industry is likely to grow at a commendable pace in the foreseen future.

Scope of the Report:

The "AI-based Drug Discovery Market: Focus on Machine Learning and Deep Learning, 2020-2030" report features an extensive study of the current market 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 presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this domain. Amongst other elements, the report features:

  • A detailed review of the current market landscape of companies that claim to offer AI-based services, platforms and tools for drug discovery. It includes information on year of establishment, company size (in terms of number of employees), location of headquarters, number of AI-based platforms / tools available, type of AI technology used, drug discovery steps for which the company has expertise involving the use of AI (target identification / validation, lead identification / optimization and ADMET studies), type of drug molecule handled (small molecules, biologics and both), drug development initiatives undertaken by the firm and target therapeutic area.
  • An in-depth analysis of the contemporary trends, presented using three schematic representations, including [A] a logo landscape highlighting the distribution of companies based on expertise across drug discovery steps and company size (in terms of number of employees), [B] a world map representation highlighting the distribution of companies based on availability of number of AI-based platforms / tools, and [C] an insightful grid analysis presenting the distribution of companies based on the type of drug molecules handled, expertise across drug discovery steps and geographical presence.
  • An analysis of the partnerships that have been inked by stakeholders engaged in the AI-based drug discovery domain, during the period 2009-2020, including research agreements, research and development agreements, technology access / utilization agreements, technology integration agreements, licensing agreements, acquisitions and other relevant types of deals.
  • An analysis of the investments made, including award / grant, seed financing, venture capital financing, debt financing and others, in companies that are involved in AI-based drug discovery.
  • 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.
  • Elaborate profiles of key players that are engaged in the AI-based drug discovery domain. Each company profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and key members of the executive team) , details related to their respective portfolio of platforms / tools, recent developments and an informed future outlook.

One of the key objectives of this report was to estimate the existing market size and the future growth potential within the AI-based drug discovery market. We have developed informed estimates on the financial evolution of the market, over the period 2020-2030. The report also provides details on the likely distribution of the current and forecasted opportunity across [A] geographical regions (North America (the US and Canada), Europe (the UK, France, Germany, Spain, Italy and other European countries), Asia Pacific (China, India, Japan, Australia and South Korea)), [B] drug discovery steps (target identification, target validation, hit identification, lead identification and lead optimization), [C] therapeutic areas (oncological disorders, neurological disorders / CNS disorders, infectious diseases, immunological disorders, cardiovascular disorders, metabolic disorders and others) and [D] end users (pharmaceutical / biotechnology companies, and academic institutes). In order to account for future uncertainties and to add robustness to our forecast model, we have provided three scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry's growth.

The opinions and insights presented in the report were also influenced by discussions held with multiple stakeholders in this domain. The report features detailed transcripts of interviews held with the following individuals (in alphabetical order) :

  • Bo Ram Beck (Head Researcher, DEARGEN)
  • Ed Addison (Co-founder, Chairman and Chief Executive Officer, Cloud Pharmaceuticals)
  • Steve Yemm (Chief Commercial Officer, Aigenpulse) and Satnam Surae (Chief Product Officer, Aigenpulse)

All actual figures have been sourced and analyzed from publicly available information forums. 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 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?
  • 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 in our research. It offers a high-level view on the current state of the AI-based drug discovery market and its likely evolution in the short-mid term and long term.

Chapter 3 is an introductory chapter that presents details on the digital revolution in the healthcare industry. It elaborates on the applications of artificial intelligence and its subsets, including machine learning (supervised learning, unsupervised learning, reinforcement learning, deep learning, natural language processing). The chapter also provides an overview of the importance of data science. It further emphasizes on the applications of AI in the healthcare sector, along with detailed information on areas, including 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. Further, it highlights the advantages and challenges related to the use of AI in drug discovery.

Chapter 4 provides an assessment of the current market landscape of companies that claim to offer AI-based services, platforms and tools for drug discovery. It includes information on year of establishment, company size (in terms of number of employees), location of headquarters, number of AI-based platforms / tools available, type of AI technology used, drug discovery steps for which the company has expertise involving the use of AI (target identification / validation, lead identification / optimization and ADMET studies), type of drug molecule handled (small molecules, biologics and both), drug development initiatives undertaken by the firm and target therapeutic area.It also provides information on the contemporary trends, which have been presented using three schematic representations, including [A] a logo landscape highlighting the distribution of companies based on expertise across drug discovery steps and company size (in terms of number of employees), [B] a world map representation highlighting the distribution of companies based on availability of number of AI-based platforms / tools, and [C] an insightful grid analysis presenting the distribution of companies based on the type of drug molecules handled, expertise across drug discovery steps and geographical presence.

Chapter 5 provides elaborate profiles of key players that are engaged in the AI-based drug discovery domain. Each company profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and key members of the executive team), details related to their respective portfolio of platforms / tools, recent developments and an informed future outlook.

Chapter 6 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 7 offers detailed analysis of the partnerships that have been inked by stakeholders engaged in the AI-based drug discovery domain, during the period 2009-2020, including research agreements, research and development agreements, technology access / utilization agreements, technology integration agreements, licensing agreements, acquisitions and other relevant types of deals.

Chapter 8 contains comprehensive analysis of the investments made, including award / grant, seed financing, venture capital financing, debt financing and others, in companies that are involved in AI-based drug discovery.

Chapter 9 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 10 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 11 presents a comprehensive market forecast analysis highlighting the future potential of the AI-based drug discovery market till 2030. It features the likely distribution of the market based on [A] geographical regions (North America (US and Canada), Europe (UK, France, Germany, Spain, Italy and other European countries), Asia Pacific (China, India, Japan, Australia and South Korea)), [B] drug discovery steps (target identification, target validation, hit identification, lead identification and lead optimization), [C] therapeutic areas (oncological disorders, neurological disorders / CNS disorders, infectious diseases, immunological disorders, cardiovascular disorders, metabolic disorders and others) and [D] end users (pharmaceutical / biotechnology companies and academic institutes). In order to account for future uncertainties and to add robustness to our forecast model, we have provided three scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry's growth.

Chapter 12 is a summary of the overall report. It includes key takeaways related to research and analysis from the report in an infographic format.

Chapter 13 is a collection of interview transcripts of discussions held with key stakeholders in this industry. In this chapter, we have presented the details of our conversations held with Bo Ram Beck (Head Researcher, DEARGEN), Ed Addison (Co-founder, Chairman and Chief Executive Officer, Cloud Pharmaceuticals) and Steve Yemm (Chief Commercial Officer, Aigenpulse) and Satnam Surae (Chief Product Officer, Aigenpulse).

Chapter 14 is an appendix, which provides tabulated data and numbers for all the figures provided in the report.

Chapter 15 is an appendix, which provides the list of companies and organizations mentioned in the report.

Table of Contents

TABLE OF CONTENTS

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. Humans, Machines and Intelligence
  • 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. Reinforcement Learning
      • 3.3.1.4. Deep Learning
      • 3.3.1.5. Natural Language Processing
  • 3.4. Data Science
  • 3.5. Applications of AI in the Healthcare Industry
    • 3.5.1. Drug Discovery
    • 3.5.2. Drug Manufacturing
    • 3.5.3. Drug Marketing
    • 3.5.4. Diagnosis and Treatment
    • 3.5.5. Clinical Trials
  • 3.6. Steps Involved in the Drug Discovery Process
    • 3.6.1. Pathway or Target Identification
    • 3.6.2. Hit or Lead Identification
    • 3.6.3. Lead Optimization
    • 3.6.4. Synthesis of Drug-like Compounds
  • 3.7. Advantages of Using AI in Drug Discovery
  • 3.8. Challenges Related to the Adoption of AI in Drug Discovery Operations
  • 3.9. Future Perspectives

4. MARKET LANDSCAPE

  • 4.1. Chapter Overview
  • 4.2. AI-based Drug Discovery: List of Companies
    • 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 Number of Platforms / Tools Available
    • 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 Target Therapeutic Area
  • 4.3. Logo Landscape: Analysis by Company Size and Drug Discovery Steps
  • 4.. World Map Representation: Regional Analysis by Number of Solutions
  • 4.5. Grid Representation: Analysis by Drug Discovery Steps, Type of Drug Molecule and Geography

5. COMPANY PROFILES

  • 5.1. Chapter Overview
  • 5.2. 3BIGS
    • 5.2.1. Company Overview
    • 5.2.2. Product / Technology Portfolio
    • 5.2.3. Recent Developments and Future Outlook
  • 5.3. Atomwise
    • 5.3.1. Company Overview
    • 5.3.2. Product / Technology Portfolio
    • 5.3.3. Recent Developments and Future Outlook
  • 5.4. ChemAlive
    • 5.4.1. Company Overview
    • 5.4.2. Product / Technology Portfolio
    • 5.4.3. Recent Developments and Future Outlook
  • 5.5. Collaboration Pharmaceuticals
    • 5.5.1. Company Overview
    • 5.5.2. Product / Technology Portfolio
    • 5.5.3. Recent Developments and Future Outlook
  • 5.6. Cyclica
    • 5.6.1. Company Overview
    • 5.6.2. Product / Technology Portfolio
    • 5.6.3. Recent Developments and Future Outlook
  • 5.7. DeepMatter
    • 5.7.1. Company Overview
    • 5.7.2. Product / Technology Portfolio
    • 5.7.3. Recent Developments and Future Outlook
  • 5.8. Exscientia
    • 5.8.1. Company Overview
    • 5.8.2. Product / Technology Portfolio
    • 5.8.3. Recent Developments and Future Outlook
  • 5.9. Insilico Medicine
    • 5.9.1. Company Overview
    • 5.9.2. Product / Technology Portfolio
    • 5.9.3. Recent Developments and Future Outlook
  • 5.10. InveniAI
    • 5.10.1. Company Overview
    • 5.10.2. Product / Technology Portfolio
    • 5.10.3. Recent Developments and Future Outlook
  • 5.11. MabSilico
    • 5.11.1. Company Overview
    • 5.11.2. Product / Technology Portfolio
    • 5.11.3. Recent Developments and Future Outlook
  • 5.12. Optibrium
    • 5.12.1. Company Overview
    • 5.12.2. Product / Technology Portfolio
    • 5.12.3. Recent Developments and Future Outlook
  • 5.13. Recursion Pharmaceuticals
    • 5.13.1. Company Overview
    • 5.13.2. Product / Technology Portfolio
    • 5.13.3. Recent Developments and Future Outlook

6. AI-BASED HEALTHCARE INITIATIVES OF TECHNOLOGY GIANTS

  • 6.1. Chapter Overview
  • 6.2. AI-based Healthcare Initiatives of Technology Giants
    • 6.2.1. Amazon Web Services
    • 6.2.2. Alibaba Cloud
    • 6.2.3. Google
    • 6.2.4. IBM
    • 6.2.5. Intel
    • 6.2.6. Microsoft
    • 6.2.7. Siemens

7. PARTNERSHIPS AND COLLABORATIONS

  • 7.1. Chapter Overview
  • 7.2. Types of Partnership Models
  • 7.3. AI-based Drug Discovery: Partnerships and Collaborations
    • 7.3.1. Analysis by Year of Partnership
    • 7.3.2. Analysis by Type of Partnership
    • 7.3.3. Analysis by Year and Type of Partnership
    • 7.3.4. Analysis by Type of Partner
    • 7.3.5. Analysis by Target Therapeutic Area
    • 7.3.6. Analysis by Type of Partner
    • 7.3.7. Most Active Players: Analysis by Number of Partnerships
    • 7.3.8. Regional Analysis
      • 7.3.8.1. Intercontinental and Intracontinental Agreements
      • 7.3.8.2. Local and International Agreements

8. FUNDING AND INVESTMENT ANALYSIS

  • 8.1. Chapter Overview
  • 8.2. Types of Funding
  • 8.3. AI-based Drug Discovery: Funding and Investment Analysis
    • 8.3.1. Analysis by Number of Funding Instances
    • 8.3.2. Analysis by Amount Invested
    • 8.3.3. Analysis by Type of Funding
    • 8.3.4. Most Active Companies: Analysis by Number of Funding Instances and Amount Raised
    • 8.3.5. Most Active Investors: Analysis by Number of Funding Instances
    • 8.3.6. Geographical Analysis by Amount Invested

9. COMPANY VALUATION ANALYSIS

  • 9.1. Chapter Overview
  • 9.2. Methodology
  • 9.3. Company Valuation Analysis: Key Parameters
    • 9.3.1. Twitter Followers Score
    • 9.3.2. Google Hits Score
    • 9.3.3. Partnerships Score
    • 9.3.4. Portfolio Strength / Uniqueness Score
    • 9.3.5. Weighted Average Score
  • 9.4. Company Valuation Analysis: Roots Analysis Proprietary Scores

10. COST SAVING ANALYSIS

  • 10.1. Chapter Overview
  • 10.2. Key Assumptions and Methodology
  • 10.3. Overall Cost Saving Potential of Using AI-based Solutions in Drug Discovery, 2020-2030
    • 10.3.1. Cost Saving Potential: Analysis by Drug Discovery Steps, 2020-2030
      • 10.3.1.1. Likely Cost Savings in Target Identification / Validation, 2020-2030
      • 10.3.1.2. Likely Cost Savings in Hit Identification, 2020-2030
      • 10.3.1.3. Likely Cost Savings in Lead Identification / Optimization, 2020-2030
    • 10.3.2. Likely Cost Savings: Analysis by Geography, 2020-2030
      • 10.3.2.1. Likely Cost Savings in North America, 2020-2030
      • 10.3.2.2. Likely Cost Savings in Europe, 2020-2030
      • 10.3.2.3. Likely Cost Savings in Asia Pacific, 2020-2030
      • 10.3.2.4. Likely Cost Savings in Rest of the World, 2020-2030

11. MARKET FORECAST

  • 11.1. Chapter Overview
  • 11.2. Key Assumptions and Methodology
  • 11.3. Global AI-based Drug Discovery Market, 2020-2030
    • 11.3.1. AI-based Drug Discovery Market: Analysis by Geography, 2020-2030
      • 11.3.1.1. AI-based Drug Discovery Market in North America, 2020-2030
        • 11.3.1.1.1. AI-based Drug Discovery Market in US, 2020-2030
        • 11.3.1.1.2. AI-based Drug Discovery Market in Canada, 2020-2030
      • 11.3.1.2. AI-based Drug Discovery Market in Europe, 2020-2030
        • 11.3.1.2.1. AI-based Drug Discovery Market in UK, 2020-2030
        • 11.3.1.2.2. AI-based Drug Discovery Market in France, 2020-2030
        • 11.3.1.2.3. AI-based Drug Discovery Market in Germany, 2020-2030
        • 11.3.1.2.4. AI-based Drug Discovery Market in Spain, 2020-2030
        • 11.3.1.2.5. AI-based Drug Discovery Market in Italy, 2020-2030
        • 11.3.1.2.6. AI-based Drug Discovery Market in Other European Countries, 2020-2030
      • 11.3.1.3. AI-based Drug Discovery Market in Asia Pacific, 2020-2030
        • 11.3.1.3.1. AI-based Drug Discovery Market in China, 2020-2030
        • 11.3.1.3.2. AI-based Drug Discovery Market in India, 2020-2030
        • 11.3.1.3.3. AI-based Drug Discovery Market in Japan, 2020-2030
        • 11.3.1.3.4. AI-based Drug Discovery Market in Australia, 2020-2030
        • 11.3.1.3.5. AI-based Drug Discovery Market in South Korea, 2020-2030
      • 11.3.1.4. AI-based Drug Discovery Market in Rest of the World, 2020-2030
        • 11.3.1.4.1. AI-based Drug Discovery Market in Saudi Arabia, 2020-2030
        • 11.3.1.4.2. AI-based Drug Discovery Market in UAE, 2020-2030
        • 11.3.1.4.3. AI-based Drug Discovery Market in Iran, 2020-2030
        • 11.3.1.4.4. AI-based Drug Discovery Market in Argentina, 2020-2030
      • 11.3.1.5. AI-based Drug Discovery Market in Other Asia Pacific and Rest of the World Regions, 2020-2030
    • 11.3.2. AI-based Drug Discovery Market: Analysis by Drug Discovery Step, 2020-2030
      • 11.3.2.1. AI-based Drug Discovery Market for Target Identification / Validation, 2020-2030
      • 11.3.2.2. AI-based Drug Discovery Market for Hit Identification, 2020-2030
      • 11.3.2.3. AI-based Drug Discovery Market for Lead Identification / Optimization, 2020-2030
    • 11.3.3. AI-based Drug Discovery Market: Analysis by Therapeutic Area, 2020-2030
      • 11.3.3.1. AI-based Drug Discovery Market for Oncological Disorders, 2020-2030
      • 11.3.3.2. AI-based Drug Discovery Market for Neurological Disorders / CNS Disorders, 2020-2030
      • 11.3.3.3. AI-based Drug Discovery Market for Infectious Diseases, 2020-2030
      • 11.3.3.4. AI-based Drug Discovery Market for Cardiovascular Disorders, 2020-2030
      • 11.3.3.5. AI-based Drug Discovery Market for Autoimmune Disorders, 2020-2030
      • 11.3.3.6. AI-based Drug Discovery Market for Metabolic Disorders, 2020-2030
      • 11.3.3.7. AI-based Drug Discovery Market for Lung Disorders, 2020-2030
      • 11.3.3.8. AI-based Drug Discovery Market for Aging Associated Disorders, 2020-2030
      • 11.3.3.9. AI-based Drug Discovery Market for Other Rare Disorders, 2020-2030
      • 11.3.3.10. AI-based Drug Discovery Market for Others, 2020-2030
    • 11.3.4. AI-based Drug Discovery Market: Analysis by End User, 2020-2030
      • 11.3.4.1. AI-based Drug Discovery Market for Pharmaceutical / Biotechnology Companies, 2020-2030
      • 11.3.4.2. AI-based Drug Discovery Market for CROs, 2020-2030
      • 11.3.4.2. AI-based Drug Discovery Market for Academic Institutes / Organizations, 2020-2030

12. CONCLUSION

13. EXECUTIVE INSIGHTS

  • 13.1 Chapter Overview
  • 13.2 Aigenpulse
    • 13.2.1 Company Snapshot
    • 13.2.2 Interview Transcript: Steve Yemm (Chief Commercial Officer) and Satnam Surae (Chief Product Officer)
  • 13.3 Cloud Pharmaceuticals
    • 13.3.1 Company Snapshot
    • 13.3.2 Interview Transcript: Ed Addison (Co-founder, Chairman and Chief Executive Officer)
  • 13.4 DEARGEN
    • 13.4.1 Company Snapshot
    • 13.4.2 Interview Transcript: Bo Ram Beck (Head Researcher)

14. APPENDIX I: TABULATED DATA

15. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

List Of Figures

  • Figure 3.1 Historical Evolution of AI
  • Figure 3.2 Types of AI Technology
  • Figure 3.3 Interconnection between Data Science, Artificial Intelligence and Big Data
  • 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
  • Figure 4.4 AI-based Drug Discovery: Distribution by Number of Platforms / Tools Available
  • Figure 4.5 AI-based Drug Discovery: Distribution by Type of AI Technology
  • Figure 4.6 AI-based Drug Discovery: Distribution by Drug Discovery Steps
  • Figure 4.7 AI-based Drug Discovery: Distribution by Type of Drug Molecule
  • Figure 4.8 AI-based Drug Discovery: Distribution by Drug Development Initiatives
  • Figure 4.9 AI-based Drug Discovery: Distribution by Target Therapeutic Area
  • Figure 4.10 Logo Landscape: Distribution by Company Size and Drug Discovery Steps
  • Figure 4.11 World Map Representation: Regional Distribution by Number of Solutions
  • Figure 4.12 Grid Representation: Analysis by Drug Discovery Steps, Type of Drug Molecule and Geography
  • Figure 7.1 Partnerships and Collaborations: Distribution by Year of Partnership
  • Figure 7.2 Partnerships and Collaborations: Distribution by Type of Partnership
  • Figure 7.3 Partnerships and Collaborations: Distribution by Year and Type of Partnership
  • Figure 7.4 Partnerships and Collaborations: Distribution by Type of Partner
  • Figure 7.5 Partnerships and Collaborations: Distribution by Target Therapeutic Area
  • Figure 7.6 Most Active Players: Distribution by Number of Partnerships
  • Figure 7.7 Partnerships and Collaborations: Distribution by Local and International Agreements
  • Figure 7.8 World Map Representation: Intercontinental and Intracontinental Agreements
  • Figure 8.1 Funding and Investments: Distribution by Year and Type of Funding, Pre-2015 - 2020
  • Figure 8.2 Funding and Investments: Year-wise Trend, Pre-2015 - 2020
  • Figure 8.3 Funding and Investments: Quarterly Trend by Amount Invested and Number of Funding Instances, Pre-2015 - 2020
  • Figure 8.4 Funding and Investments: Distribution by Number of Instances and Type of Funding
  • Figure 8.5 Funding and Investments: Distribution by Amount Invested and Type of Funding
  • Figure 8.6 Most Active Players: Distribution by Number of Funding Instances and Amount Invested
  • Figure 8.7 Most Active Investors: Distribution by Number of Funding Instances
  • Figure 8.8 Funding and Investments: Geographical Distribution by Amount Invested (USD Million)
  • Figure 9.1 Company Valuation Analysis: Categorization by Twitter Followers Score
  • Figure 9.2 Company Valuation Analysis: Categorization by Google Hits Score
  • Figure 9.3 Company Valuation Analysis: Categorization by Partnerships Score
  • Figure 9.4 Company Valuation Analysis: Categorization by Portfolio Strength / Uniqueness Score
  • Figure 9.5 Company Valuation Analysis: Categorization by Weighted Average Score
  • Figure 10.1 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery, 2020-2030 (USD Million)
  • Figure 10.2 Likely Cost Savings: Distribution by Drug Discovery Steps, 2020-2030 (USD Million)
  • Figure 10.3 Likely Cost Savings Associated with the Use of AI in Target Identification, 2020-2030 (USD Million)
  • Figure 10.4 Likely Cost Savings Associated with the Use of AI in Target Validation, 2020-2030 (USD Million)
  • Figure 10.5 Likely Cost Savings Associated with the Use of AI in Hit Identification, 2020-2030 (USD Million)
  • Figure 10.6 Likely Cost Savings Associated with the Use of AI in Lead Identification, 2020-2030 (USD Million)
  • Figure 10.7 Likely Cost Savings Associated with the Use of AI in Lead Optimization, 2020-2030 (USD Million)
  • Figure 10.8 Likely Cost Savings: Distribution by Geography, 2020-2030 (USD Million)
  • Figure 10.9 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in North America, 2020-2030 (USD Million)
  • Figure 10.10 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Europe, 2020-2030 (USD Million)
  • Figure 10.11 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Asia Pacific, 2020-2030 (USD Million)
  • Figure 10.12 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Rest of the World, 2020-2030 (USD Million)
  • Figure 11.1. Global AI-based Drug Discovery Market, 2020-2030 (USD Million)
  • Figure 11.2. AI-based Drug Discovery Market: Distribution by Geography, 2020-2030 (USD Million)
  • Figure 11.3. AI-based Drug Discovery Market in North America, 2020-2030 (USD Million)
  • Figure 11.4. AI-based Drug Discovery Market in the US, 2020-2030 (USD Million)
  • Figure 11.5. AI-based Drug Discovery Market in Canada, 2020-2030 (USD Million)
  • Figure 11.6. AI-based Drug Discovery Market in Europe, 2020-2030 (USD Million)
  • Figure 11.7. AI-based Drug Discovery Market in the UK, 2020-2030 (USD Million)
  • Figure 11.8. AI-based Drug Discovery Market in France, 2020-2030 (USD Million)
  • Figure 11.9. AI-based Drug Discovery Market in Germany, 2020-2030 (USD Million)
  • Figure 11.10. AI-based Drug Discovery Market in Spain, 2020-2030 (USD Million)
  • Figure 11.11. AI-based Drug Discovery Market in Italy, 2020-2030 (USD Million)
  • Figure 11.12. AI-based Drug Discovery Market in Other European Countries, 2020-2030 (USD Million)
  • Figure 11.13. AI-based Drug Discovery Market in Asia Pacific, 2020-2030 (USD Million)
  • Figure 11.14. AI-based Drug Discovery Market in China, 2020-2030 (USD Million)
  • Figure 11.15. AI-based Drug Discovery Market in India, 2020-2030 (USD Million)
  • Figure 11.16. AI-based Drug Discovery Market in Japan, 2020-2030 (USD Million)
  • Figure 11.17. AI-based Drug Discovery Market in Australia, 2020-2030 (USD Million)
  • Figure 11.18. AI-based Drug Discovery Market in South Korea, 2020-2030 (USD Million)
  • Figure 11.19. AI-based Drug Discovery Market in Rest of the World, 2020-2030 (USD Million)
  • Figure 11.20. AI-based Drug Discovery Market in Saudi Arabia, 2020-2030 (USD Million)
  • Figure 11.21. AI-based Drug Discovery Market in UAE, 2020-2030 (USD Million)
  • Figure 11.22. AI-based Drug Discovery Market in Iran, 2020-2030 (USD Million)
  • Figure 11.23. AI-based Drug Discovery Market in Argentina, 2020-2030 (USD Million)
  • Figure 11.24. AI-based Drug Discovery Market in Asia Pacific and other Rest of the World Regions, 2020-2030 (USD Million)
  • Figure 11.25. AI-based Drug Discovery Market: Distribution by Drug Discovery Step, 2020-2030 (USD Million)
  • Figure 11.26. AI-based Drug Discovery Market for Target Identification, 2020-2030 (USD Million)
  • Figure 11.27. AI-based Drug Discovery Market for Target Validation, 2020-2030 (USD Million)
  • Figure 11.28. AI-based Drug Discovery Market for Hit Identification, 2020-2030 (USD Million)
  • Figure 11.29. AI-based Drug Discovery Market for Lead Identification, 2020-2030 (USD Million)
  • Figure 11.30. AI-based Drug Discovery Market for Lead Optimization, 2020-2030 (USD Million)
  • Figure 11.31. AI-based Drug Discovery Market: Distribution by Therapeutic Area, 2020-2030 (USD Million)
  • Figure 11.32. AI-based Drug Discovery Market for Oncological Disorders, 2020-2030 (USD Million)
  • Figure 11.33. AI-based Drug Discovery Market for Neurological Disorders / CNS Disorders, 2020-2030 (USD Million)
  • Figure 11.34. AI-based Drug Discovery Market for Infectious Diseases, 2020-2030 (USD Million)
  • Figure 11.35. AI-based Drug Discovery Market for Cardiovascular Disorders, 2020-2030 (USD Million)
  • Figure 11.36. AI-based Drug Discovery Market for Autoimmune Disorders, 2020-2030 (USD Million)
  • Figure 11.37. AI-based Drug Discovery Market for Metabolic Disorders, 2020-2030 (USD Million)
  • Figure 11.38. AI-based Drug Discovery Market for Lung Disorders, 2020-2030 (USD Million)
  • Figure 11.39. AI-based Drug Discovery Market for Aging Associated Disorders, 2020-2030 (USD Million)
  • Figure 11.40. AI-based Drug Discovery Market for Other Rare Disorders, 2020-2030 (USD Million)
  • Figure 11.41. AI-based Drug Discovery Market for Others, 2020-2030 (USD Million)
  • Figure 11.42. AI-based Drug Discovery Market: Distribution by End User, 2020-2030 (USD Million)
  • Figure 11.43. AI-based Drug Discovery Market for Pharmaceutical / Biotechnology Companies, 2020-2030 (USD Million)
  • Figure 11.44. AI-based Drug Discovery Market for CROs, 2020-2030 (USD Million)
  • Figure 11.45. AI-based Drug Discovery Market for Academic Institutes / Organizations, 2020-2030 (USD Million)
  • Figure 12.1. Concluding Remarks: Current Market Landscape
  • Figure 12.2. Concluding Remarks: Partnerships and Collaborations
  • Figure 12.3. Concluding Remarks: Funding and Investments
  • Figure 12.4. Concluding Remarks: Company Valuation Analysis
  • Figure 12.5. Concluding Remarks: Cost Saving Analysis
  • Figure 12.6. Concluding Remarks: Market Forecast

List Of Tables

  • Table 4.1 AI-based Drug Discovery: List of Companies (Information on Year of Establishment, Company Size, Location of Headquarters, Number and Name of Platforms / Tools Available)
  • Table 4.2 AI-based Drug Discovery: List of Companies (Information on Type of AI Technology)
  • Table 4.3 AI-based Drug Discovery: List of Companies (Information on Drug Discovery Steps, Type of Drug Molecule, Drug Development Initiatives and Target Therapeutic Area)
  • Table 5.1 3BIGS: Company Overview
  • Table 5.2 3BIGS: AI-based Product / Technology Portfolio
  • Table 5.3 3BIGS: Recent Developments and Future Outlook
  • Table 5.4 Atomwise: Company Overview
  • Table 5.5 Atomwise: AI-based Product / Technology Portfolio
  • Table 5.6 Atomwise: Recent Developments and Future Outlook
  • Table 5.7 ChemAlive: Company Overview
  • Table 5.8 ChemAlive: AI-based Product / Technology Portfolio
  • Table 5.9 ChemAlive: Recent Developments and Future Outlook
  • Table 5.10 Collaboration Pharmaceuticals: Company Overview
  • Table 5.11 Collaboration Pharmaceuticals: AI-based Product / Technology Portfolio
  • Table 5.12 Collaboration Pharmaceuticals: Recent Developments and Future Outlook
  • Table 5.13 Cyclica: Company Overview
  • Table 5.14 Cyclica: AI-based Product / Technology Portfolio
  • Table 5.15 Cyclica: Recent Developments and Future Outlook
  • Table 5.16 DeepMatter: Company Overview
  • Table 5.17 DeepMatter: AI-based Product / Technology Portfolio
  • Table 5.18 DeepMatter: Recent Developments and Future Outlook
  • Table 5.19 Exscientia: Company Overview
  • Table 5.20 Exscientia: AI-based Product / Technology Portfolio
  • Table 5.21 Exscientia: Recent Developments and Future Outlook
  • Table 5.22 Insilico Medicine: Company Overview
  • Table 5.23 Insilico Medicine: AI-based Product / Technology Portfolio
  • Table 5.24 Insilico Medicine: Recent Developments and Future Outlook
  • Table 5.25 InveniAI: Company Overview
  • Table 5.26 InveniAI: AI-based Product / Technology Portfolio
  • Table 5.27 InveniAI: Recent Developments and Future Outlook
  • Table 5.28 MabSilico: Company Overview
  • Table 5.29 MabSilico: AI-based Product / Technology Portfolio
  • Table 5.30 MabSilico: Recent Developments and Future Outlook
  • Table 5.31 Optibrium: Company Overview
  • Table 5.32 Optibrium: AI-based Product / Technology Portfolio
  • Table 5.33 Optibrium: Recent Developments and Future Outlook
  • Table 5.34 Recursion Pharmaceuticals: Company Overview
  • Table 5.35 Recursion Pharmaceuticals: AI-based Product / Technology Portfolio
  • Table 5.36 Recursion Pharmaceuticals: Recent Developments and Future Outlook
  • Table 7.1 AI-based Drug Discovery: List of Partnerships and Collaborations
  • Table 8.1. AI-based Drug Discovery: List of Funding and Investments
  • Table 9.1 Company Valuation Analysis: Weighted Average Score
  • Table 9.2 Company Valuation Analysis: Estimated Valuation
  • Table 13.1 Aigenpulse: Company Snapshot
  • Table 13.2 Cloud Pharmaceuticals: Company Snapshot
  • Table 13.3 DEARGEN: Company Snapshot
  • Table 14.1 AI-based Drug Discovery: Distribution by Year of Establishment
  • Table 14.2 AI-based Drug Discovery: Distribution by Company Size
  • Table 14.3 AI-based Drug Discovery: Distribution by Location of Headquarters
  • Table 14.4 AI-based Drug Discovery: Distribution by Number of Platforms / Tools Available
  • Table 14.5 AI-based Drug Discovery: Distribution by Type of AI Technology
  • Table 14.6 AI-based Drug Discovery: Distribution by Drug Discovery Steps
  • Table 14.7 AI-based Drug Discovery: Distribution by Type of Drug Molecule
  • Table 14.8 AI-based Drug Discovery: Distribution by Drug Development Initiatives
  • Table 14.9 AI-based Drug Discovery: Distribution by Target Therapeutic Area
  • Table 14.10 Partnerships and Collaborations: Distribution by Year of Partnership
  • Table 14.11 Partnerships and Collaborations: Distribution by Type of Partnership
  • Table 14.12 Partnerships and Collaborations: Distribution by Year and Type of Partnership
  • Table 14.13 Partnerships and Collaborations: Distribution by Type of Partner
  • Table 14.14 Partnerships and Collaborations: Distribution by Target Therapeutic Area
  • Table 14.15 Most Active Players: Distribution by Number of Partnerships
  • Table 14.16 Partnerships and Collaborations: Distribution by Local and International Agreements
  • Table 14.17 Funding and Investments: Distribution by Year and Type of Funding, Pre-2015 - 2020
  • Table 14.18 Funding and Investments: Year-wise Trend, Pre-2015 - 2020
  • Table 14.19 Funding and Investments: Quarterly Trend by Amount Invested and Number of Funding Instances, Pre-2015 - 2020
  • Table 14.20 Funding and Investments: Distribution by Number of Instances and Type of Funding
  • Table 14.21 Funding and Investments: Distribution by Amount Invested and Type of Funding
  • Table 14.22 Most Active Players: Distribution by Number of Funding Instances and Amount Invested
  • Table 14.23 Most Active Investors: Distribution by Number of Funding Instances
  • Table 14.24 Company Valuation Analysis: Sample Dataset
  • Table 14.25 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery, 2020-2030 (USD Million)
  • Table 14.26 Likely Cost Savings: Distribution by Drug Discovery Steps, 2020-2030 (USD Million)
  • Table 14.27 Likely Cost Savings Associated with the Use of AI in Target Identification, 2020-2030 (USD Million)
  • Table 14.28 Likely Cost Savings Associated with the Use of AI in Target Validation, 2020-2030 (USD Million)
  • Table 14.29 Likely Cost Savings Associated with the Use of AI in Hit Identification, 2020-2030 (USD Million)
  • Table 14.30 Likely Cost Savings Associated with the Use of AI in Lead Identification, 2020-2030 (USD Million)
  • Table 14.31 Likely Cost Savings Associated with the Use of AI in Lead Optimization, 2020-2030 (USD Million)
  • Table 14.32 Likely Cost Savings: Distribution by Geography, 2020-2030 (USD Million)
  • Table 14.33 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in North America, 2020-2030 (USD Million)
  • Table 14.34 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Europe, 2020-2030 (USD Million)
  • Table 14.35 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Asia Pacific, 2020-2030 (USD Million)
  • Table 14.36 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Rest of the World, 2020-2030 (USD Million)
  • Table 14.37. Global AI-based Drug Discovery Market, 2020-2030 (USD Million)
  • Table 14.38. AI-based Drug Discovery Market: Distribution by Geography, 2020-2030 (USD Million)
  • Table 14.39. AI-based Drug Discovery Market in North America, 2020-2030 (USD Million)
  • Table 14.40. AI-based Drug Discovery Market in the US, 2020-2030 (USD Million)
  • Table 14.41. AI-based Drug Discovery Market in Canada, 2020-2030 (USD Million)
  • Table 14.42. AI-based Drug Discovery Market in Europe, 2020-2030 (USD Million)
  • Table 14.43. AI-based Drug Discovery Market in the UK, 2020-2030 (USD Million)
  • Table 14.44. AI-based Drug Discovery Market in France, 2020-2030 (USD Million)
  • Table 14.45. AI-based Drug Discovery Market in Germany, 2020-2030 (USD Million)
  • Table 14.46. AI-based Drug Discovery Market in Spain, 2020-2030 (USD Million)
  • Table 14.47. AI-based Drug Discovery Market in Italy, 2020-2030 (USD Million)
  • Table 14.48. AI-based Drug Discovery Market in Other European Countries, 2020-2030 (USD Million)
  • Table 14.49. AI-based Drug Discovery Market in Asia Pacific, 2020-2030 (USD Million)
  • Table 14.50. AI-based Drug Discovery Market in China, 2020-2030 (USD Million)
  • Table 14.51. AI-based Drug Discovery Market in India, 2020-2030 (USD Million)
  • Table 14.52. AI-based Drug Discovery Market in Japan, 2020-2030 (USD Million)
  • Table 14.53. AI-based Drug Discovery Market in Australia, 2020-2030 (USD Million)
  • Table 14.54. AI-based Drug Discovery Market in South Korea, 2020-2030 (USD Million)
  • Table 14.55. AI-based Drug Discovery Market in Rest of the World, 2020-2030 (USD Million)
  • Table 14.56. AI-based Drug Discovery Market in Saudi Arabia, 2020-2030 (USD Million)
  • Table 14.57. AI-based Drug Discovery Market in UAE, 2020-2030 (USD Million)
  • Table 14.58. AI-based Drug Discovery Market in Iran, 2020-2030 (USD Million)
  • Table 14.59. AI-based Drug Discovery Market in Argentina, 2020-2030 (USD Million)
  • Table 14.60. AI-based Drug Discovery Market in Asia Pacific and other Rest of the World Regions, 2020-2030 (USD Million)
  • Table 14.61. AI-based Drug Discovery Market: Distribution by Drug Discovery Step, 2020-2030 (USD Million)
  • Table 14.62. AI-based Drug Discovery Market for Target Identification, 2020-2030 (USD Million)
  • Table 14.63. AI-based Drug Discovery Market for Target Validation, 2020-2030 (USD Million)
  • Table 14.64. AI-based Drug Discovery Market for Hit Identification, 2020-2030 (USD Million)
  • Table 14.65. AI-based Drug Discovery Market for Lead Identification, 2020-2030 (USD Million)
  • Table 14.66. AI-based Drug Discovery Market for Lead Optimization, 2020-2030 (USD Million)
  • Table 14.67. AI-based Drug Discovery Market: Distribution by Therapeutic Area, 2020-2030 (USD Million)
  • Table 14.68. AI-based Drug Discovery Market for Oncological Disorders, 2020-2030 (USD Million)
  • Table 14.69. AI-based Drug Discovery Market for Neurological Disorders / CNS Disorders, 2020-2030 (USD Million)
  • Table 14.70. AI-based Drug Discovery Market for Infectious Diseases, 2020-2030 (USD Million)
  • Table 14.71. AI-based Drug Discovery Market for Cardiovascular Disorders, 2020-2030 (USD Million)
  • Table 14.72. AI-based Drug Discovery Market for Autoimmune Disorders, 2020-2030 (USD Million)
  • Table 14.73. AI-based Drug Discovery Market for Metabolic Disorders, 2020-2030 (USD Million)
  • Table 14.74. AI-based Drug Discovery Market for Lung Disorders, 2020-2030 (USD Million)
  • Table 14.75. AI-based Drug Discovery Market for Aging Associated Disorders, 2020-2030 (USD Million)
  • Table 14.76. AI-based Drug Discovery Market for Other Rare Disorders, 2020-2030 (USD Million)
  • Table 14.77. AI-based Drug Discovery Market for Others, 2020-2030 (USD Million)
  • Table 14.78. AI-based Drug Discovery Market: Distribution by End User, 2020-2030 (USD Million)
  • Table 14.79. AI-based Drug Discovery Market for Pharmaceutical / Biotechnology Companies, 2020-2030 (USD Million)
  • Table 14.80. AI-based Drug Discovery Market for CROs, 2020-2030 (USD Million)
  • Table 14.81. AI-based Drug Discovery Market for Academic Institutes / Organizations, 2020-2030 (USD Million)

List Of Companies

The following companies and organizations have been mentioned in the report.

  • 1. 3BIGS
  • 2. 3W Partners
  • 3. 6 Dimensions Capital
  • 4. 8VC
  • 5. 99andBeyond
  • 6. A2A Pharmaceuticals
  • 7. Abalone Bio
  • 8. AbbVie
  • 9. AbCellera
  • 10. Abstract Ventures
  • 11. Accelerate Long Island
  • 12. Accenture
  • 13. Accutar Biotech
  • 14. Acellera
  • 15. Acequia Capital
  • 16. AcuraStem
  • 17. Adagene
  • 18. ADC Therapeutics
  • 19. ADEL
  • 20. Advantage Capital
  • 21. AdynXX
  • 22. Agent Capital
  • 23. AGORANO
  • 24. AI Therapeutics
  • 25. Ai-biopharma
  • 26. Aigenpulse
  • 27. Air Street Capital
  • 28. Ajou University
  • 29. Akashi Therapeutics
  • 30. Albany Molecular Research
  • 31. A-Level Capital
  • 32. Alexandria Real Estate Equities
  • 33. Alibaba Cloud
  • 34. Allergan
  • 35. Alliance for Clinical Trials in Oncology
  • 36. Alloy Therapeutics
  • 37. Almac Diagnostic Services
  • 38. Almirall
  • 39. Alphanosos
  • 40. ALS Association
  • 41. ALS Investment Fund
  • 42. Altos Ventures
  • 43. Amadeus Capital Partners
  • 44. Amadeus Capital Partners
  • 45. Amazon Web Services
  • 46. Amazon Web Services (AWS)
  • 47. AME Cloud Ventures
  • 48. American Society of Clinical Oncology
  • 49. Amgen
  • 50. Amgen Ventures
  • 51. Amidi Group
  • 52. Amplify Partners
  • 53. Amplitude
  • 54. Anagenesis Biotechnologies
  • 55. Andreessen Horowitz
  • 56. Angel CoFund
  • 57. Anima Biotech
  • 58. Ansa Biotechnologies
  • 59. Antiverse
  • 60. ApexQubit
  • 61. Aqemia
  • 62. Arbutus Biopharma
  • 63. ARCH Venture Partners
  • 64. ArcTern Ventures
  • 65. Arctoris
  • 66. Ardigen
  • 67. Arpeggio Biosciences
  • 68. Artis Ventures
  • 69. Arzeda
  • 70. Asset Management Ventures
  • 71. Astellas Pharma
  • 72. Astia Angels
  • 73. AstraZeneca
  • 74. AstraZeneca's Centre for Genomics Research
  • 75. ATAI Life Sciences
  • 76. Atinum Investment
  • 77. Atlantic Labs
  • 78. Atlas Venture
  • 79. Atomico
  • 80. Atomwise
  • 81. Atrius Health
  • 82. AUM Biosciences
  • 83. Auransa
  • 84. Aurinvest
  • 85. Auvergne-Rhône-Alpes regional council
  • 86. AVIC Trust
  • 87. AxoSim
  • 88. B Capital Group
  • 89. BABEL Ventures
  • 90. Baidu Ventures
  • 91. Baillie Gifford
  • 92. Balderton Capital
  • 93. Bangarang Group
  • 94. Battelle Center for Science, Engineering, and Public Policy, Ohio State University
  • 95. Bavarian Nordic
  • 96. Bayer
  • 97. Baylor College of Medicine Human Genome Sequencing Center (BCM-HGSC)
  • 98. Beiersdorf
  • 99. Benevolent AI
  • 100. BERG
  • 101. Better Ventures
  • 102. Bezos Expeditions
  • 103. Big Data Institute
  • 104. BigHat Biosciences
  • 105. Bill & Melinda Gates Foundation
  • 106. BioAge Labs
  • 107. Bioeconomy Capital
  • 108. BioFocus DPI
  • 109. BioInvent International
  • 110. Biomea Healthcare
  • 111. BioMotiv
  • 112. Bionano Genomics
  • 113. BioNTech
  • 114. Biorelate
  • 115. Bios Partners
  • 116. BioSymetrics
  • 117. biotx.ai
  • 118. BioVentures Investors
  • 119. Bioverge
  • 120. Biovista
  • 121. BioXcel Therapeutics
  • 122. BlackRock
  • 123. Block.one
  • 124. Bloomberg Beta
  • 125. Blue Bear Ventures
  • 126. bluebird bio
  • 127. Boehringer Ingelheim
  • 128. Bold Capital Partners
  • 129. Bpifrance
  • 130. Brace Pharma Capital
  • 131. Brain Canada
  • 132. Breakout Labs
  • 133. BridgeBio Pharma
  • 134. Brigham and Women's Hospital
  • 135. Brightspark Ventures
  • 136. Bristol-Myers Squibb
  • 137. Broad Institute
  • 138. btov Partners
  • 139. Builders VC
  • 140. Bulba Ventures
  • 141. Busolantix Investment
  • 142. BVF Partners
  • 143. C4X Discovery
  • 144. Caffeinated Capital
  • 145. Calibr
  • 146. California Institute of Biomedical Research
  • 147. Cambia Health Solutions
  • 148. Cambridge Cancer Genomics
  • 149. Cambridge Research Centre
  • 150. Cancer Genetics
  • 151. Cantos Ventures
  • 152. CARB-X
  • 153. CareDx
  • 154. CaroCure
  • 155. Casdin Capital
  • 156. Catalio Capital Management
  • 157. Catapult Ventures
  • 158. Cathay Innovation
  • 159. Causaly
  • 160. CB Lux
  • 161. CECS
  • 162. Celgene
  • 163. Cellarity
  • 164. Celsius Therapeutics
  • 165. Center for the Advancement of Science in Space
  • 166. CENTOGENE
  • 167. Centre for the Development of Industrial Technology (CDTI)
  • 168. Cerebras
  • 169. Cerevel Therapeutics
  • 170. Charcot-Marie-Tooth Association
  • 171. Charles River Laboratories
  • 172. ChemAlive
  • 173. ChemAxon
  • 174. ChemDiv
  • 175. ChemPass
  • 176. ChemSpace
  • 177. Chiesi Farmaceutici
  • 178. Children's Tumor Foundation
  • 179. China Canada Angels Alliance
  • 180. China International Capital Corporation
  • 181. China Life Healthcare Fund
  • 182. China Oncology Focus
  • 183. Chinese Academy of Medical Sciences
  • 184. Cigna Ventures
  • 185. City Hill Ventures
  • 186. Civilization Ventures
  • 187. CJ HealthCare
  • 188. Claremont Creek Ventures
  • 189. Clarus Ventures
  • 190. Cleveland Clinic
  • 191. CLI Ventures
  • 192. Climate-KIC Accelerator
  • 193. Cloud Pharmaceuticals
  • 194. CMT Research Foundation
  • 195. Collaborations Pharmaceuticals
  • 196. Collaborative Drug Discovery
  • 197. Collective Scientific
  • 198. Colt Ventures
  • 199. ConcertAI
  • 200. Conifer Point Pharmaceuticals
  • 201. Cormorant Asset Management
  • 202. Cosine
  • 203. Cota Capital
  • 204. CPP Investments
  • 205. CQDM - Consortium de recherche biopharmaceutique
  • 206. Creative Destruction Lab
  • 207. Cresset
  • 208. CrystalGenomics
  • 209. CTI Life Sciences Fund
  • 210. Cultivian Sandbox Ventures
  • 211. CVC
  • 212. CVS Health
  • 213. Cyclica
  • 214. CytoReason
  • 215. Daewoong Pharmaceutical
  • 216. Danhua Venture Capital
  • 217. Dante Labs
  • 218. Data4cure
  • 219. Dataspora
  • 220. Datavant
  • 221. DCVC
  • 222. DEARGEN
  • 223. Deep Genomics
  • 224. Deep Knowledge Ventures
  • 225. DeepCure
  • 226. DeepMatter
  • 227. DeepTrait
  • 228. Deerfield Management
  • 229. Delin Ventures
  • 230. Denali Therapeutics
  • 231. Denovicon Therapeutics
  • 232. Denovium
  • 233. Department of Health and Social Care
  • 234. DEXSTR
  • 235. Diamond Light Source
  • 236. DNAnexus
  • 237. DNDi
  • 238. Dolby Family Ventures
  • 239. Dow AgroSciences
  • 240. Drive Capital
  • 241. Droia Oncology Ventures
  • 242. DSC Investment
  • 243. Dualogics
  • 244. Dynamk Capital
  • 245. Dyno Therapeutics
  • 246. Echo Health Ventures
  • 247. EcoR1 Capital
  • 248. EDBI
  • 249. EIC Accelerator
  • 250. Eight Roads
  • 251. Elad Gil
  • 252. Elaia
  • 253. Elevian
  • 254. Eli Lilly
  • 255. Elsevier
  • 256. Elucidata
  • 257. Embark Ventures
  • 258. Empire State Development
  • 259. Empirico
  • 260. Enamine
  • 261. Endogena Therapeutics
  • 262. Endure Capital
  • 263. Engine Biosciences
  • 264. Enterprise Ireland
  • 265. Envisagenics
  • 266. Epic Capital Management
  • 267. Epic Ventures
  • 268. Erasca
  • 269. e-therapeutics
  • 270. Euretos
  • 271. European Investment Bank
  • 272. European Union
  • 273. Eurostars
  • 274. Evaxion Biotech
  • 275. Evotec
  • 276. Ewha Womans University
  • 277. Excelra
  • 278. Executive Agency for Small and Medium-sized Enterprises (EASME)
  • 279. Exscientia
  • 280. Felicis Ventures
  • 281. Fidelity Asia Fund
  • 282. Fidelity Biosciences
  • 283. Fifty Years
  • 284. Financière Boscary
  • 285. FinLab
  • 286. First Round Capital
  • 287. First Star Ventures
  • 288. Flagship Pioneering
  • 289. Flybridge Capital Partners
  • 290. FMC
  • 291. Foresite Capital
  • 292. Forma Therapeutics
  • 293. Formic Ventures
  • 294. Foundation for Angelman Syndrome Therapeutics (FAST)
  • 295. Founders Factory
  • 296. Founders Fund
  • 297. Fountain Therapeutics
  • 298. Fox Chase Cancer Center
  • 299. F-Prime Capital
  • 300. FREES FUND
  • 301. Frontier Medicines
  • 302. FundersClub
  • 303. Future Ventures
  • 304. G3 Therapeutics
  • 305. Galapagos
  • 306. Gatehouse Bio
  • 307. GC Pharma
  • 308. Geisinger
  • 309. Genedata
  • 310. Genentech
  • 311. General Atlantic
  • 312. General Catalyst
  • 313. Genesen
  • 314. Genesis Therapeutics
  • 315. Genialis
  • 316. Genmab
  • 317. Genomatica
  • 318. Genome Biologics
  • 319. Genome Institute of Singapore
  • 320. Genomenon
  • 321. Genomics England
  • 322. Genuity Science
  • 323. Gero
  • 324. Gi Global Health Fund
  • 325. Gilead Sciences
  • 326. GlaxoSmithKline
  • 327. Global Brain
  • 328. Global Founders Capital
  • 329. GM&C Life Sciences Fund
  • 330. GNS Healthcare
  • 331. Golden Ventures
  • 332. Google
  • 333. Google Ventures
  • 334. Gopher Asset Management
  • 335. Gordian Biotechnology
  • 336. Government of Canada
  • 337. Government of Switzerland
  • 338. GP Healthcare Capital
  • 339. GPG Ventures
  • 340. Grand Challenges Canada
  • 341. Green Park & Golf Ventures
  • 342. GreenSky Capital
  • 343. Gritstone Oncology
  • 344. GT Healthcare Capital Partners
  • 345. Gustave Roussy
  • 346. Hafnium Labs
  • 347. Hanhai Studio
  • 348. Harbour Antibodies
  • 349. Harbour BioMed
  • 350. Harris & Harris Group
  • 351. HCS
  • 352. Health Wildcatters
  • 353. HealthInc
  • 354. Healx
  • 355. Heritage Provider Network
  • 356. Hewlett Packard Enterprise (HPE)
  • 357. Hibiskus Biopharma
  • 358. Hike Ventures
  • 359. Hinge Therapeutics
  • 360. Hiventures Investment Fund
  • 361. HOF Capital
  • 362. HotSpot Therapeutics
  • 363. Huadong Medicine
  • 364. Human Capital
  • 365. Hyperplane Venture Capital
  • 366. IA Ventures
  • 367. IBM
  • 368. Ichor Biologics
  • 369. IDG Capital
  • 370. IIT Kharagpur
  • 371. Iktos
  • 372. IMM Investment
  • 373. Immunocure Discovery Solutions
  • 374. InfoChem
  • 375. InnoPharmaScreen
  • 376. Innophore
  • 377. Innoplexus
  • 378. Innospark Ventures
  • 379. Innova31
  • 380. Innovate NY Fund
  • 381. Innovate UK
  • 382. Innovation Endeavors
  • 383. Innovation Fund Denmark
  • 384. Innovative Medicines Initiative (IMI)
  • 385. Inovia Capital
  • 386. inSili.com
  • 387. Insiliance
  • 388. Insilico Medicine
  • 389. Insitro
  • 390. Institut Carnot CALYM
  • 391. Institut Gustave Roussy
  • 392. Institut Pasteur Korea
  • 393. Institute of Cancer Research, London
  • 394. Institute of Materia Medica
  • 395. Intel
  • 396. Intel Capital
  • 397. Intellegens
  • 398. IntelliCyt
  • 399. Intermountain Ventures
  • 400. Interprotein
  • 401. Intuition Systems
  • 402. InveniAI
  • 403. InVivo AI
  • 404. Invus
  • 405. Ionis
  • 406. IP Group
  • 407. IPF Partners
  • 408. IQVIA
  • 409. Ireland Strategic Investment Fund
  • 410. I-Stem
  • 411. IT-Translation
  • 412. Janssen Pharmaceutica
  • 413. Jiangsu Chia Tai Fenghai Pharmaceutical
  • 414. Jiangsu Hansoh Pharmaceutical Group
  • 415. JLABS
  • 416. Johns Hopkins School of Medicine
  • 417. Johns Hopkins University
  • 418. Johnson & Johnson
  • 419. Johnson & Johnson Innovation - JJDC
  • 420. Juvena Therapeutics
  • 421. Juvenescence
  • 422. JW Pharmaceutical
  • 423. K Cube Ventures
  • 424. K9 Ventures
  • 425. KB Securities
  • 426. Kebotix
  • 427. Keio University
  • 428. KemPharm
  • 429. Khosla Ventures
  • 430. Kindred Capital
  • 431. Kinetic Discovery
  • 432. King Star Capital
  • 433. King's College London
  • 434. Korea Atomic Energy Research Institute (KAERI)
  • 435. Korea Development Bank
  • 436. Korea Fixed-Income Investment Advisory
  • 437. Korea Investment Partners
  • 438. Korea Research Institute of Chemical Technology (KRICT)
  • 439. Ksilink
  • 440. KTB Network
  • 441. La Financiere Gaspard
  • 442. Labcyte
  • 443. LabGenius
  • 444. LabKey
  • 445. Lansdowne Partners
  • 446. Lantern Pharma
  • 447. LanzaTech
  • 448. Laurion Capital Management
  • 449. Lawrence Livermore National Laboratory
  • 450. Laxai Life Sciences
  • 451. LB Investment
  • 452. Leaps by Bayer
  • 453. LEO Pharma
  • 454. Lhasa
  • 455. LifeForce Capital
  • 456. LifeSci Venture Partners
  • 457. Lightspeed Venture Partners
  • 458. Lilly Asia Ventures
  • 459. Linguamatics
  • 460. LMU University Hospital
  • 461. Lodo Therapeutics
  • 462. Long Island Emerging technologies Fund (LIETF)
  • 463. Longevity Fund
  • 464. Loup Ventures
  • 465. Lundbeck
  • 466. Lux Capital
  • 467. Luxembourg Centre for Systems Biomedicine (LCSB)
  • 468. M12
  • 469. MAbSilico
  • 470. Macroceutics
  • 471. Magnetic Ventures
  • 472. Manchester Tech Trust Angels
  • 473. Mannin Research
  • 474. Marathon Venture Capital
  • 475. MaRS Catalyst Fund
  • 476. Maruho
  • 477. Massachusetts Life Sciences Center
  • 478. MassBiologics
  • 479. Maxygen
  • 480. MBC BioLabs
  • 481. McQuibban Lab
  • 482. MDS Foundation
  • 483. Medchemica
  • 484. Medical Prognosis Institute
  • 485. Medirita
  • 486. Memorial Sloan Kettering Cancer
  • 487. Menlo Ventures
  • 488. Menten AI
  • 489. Merck
  • 490. Merck Accelerator
  • 491. Mercury Fund
  • 492. Meridian Street Capital
  • 493. Micar Innovation
  • 494. Michael J. Fox Foundation
  • 495. Microsoft
  • 496. Microsoft Ventures
  • 497. MidCap Financial
  • 498. Mila
  • 499. Mirae Asset Venture Investment
  • 500. MIT delta v
  • 501. Mitsui
  • 502. Molecule
  • 503. Molecule.one
  • 504. Moleculomics
  • 505. Molomics
  • 506. Monsanto Growth Ventures
  • 507. MPM Capital
  • 508. MRL Ventures Fund
  • 509. Mubadala Capital
  • 510. Multiple Myeloma Research Foundation
  • 511. Muscular Dystrophy Association
  • 512. Muscular Dystrophy UK
  • 513. myTomorrows
  • 514. Nan Fung Life Sciences
  • 515. Nanna Therapeutics
  • 516. Nashville Biosciences
  • 517. National Cancer Institute
  • 518. National Center for Advancing Translational Sciences ( NCATS )
  • 519. National Center for Research and Development
  • 520. National Institute of Neurological Disorders and Stroke (NINDS)
  • 521. National Institute on Aging (NIA)
  • 522. National Institutes of Health
  • 523. National Institutes of Small Business Technology Transfer
  • 524. National Instrumentation Center for Environmental Management
  • 525. National Research Council Canada
  • 526. National Science Foundation
  • 527. National Science Foundation Small Business Innovation Research (NSF SBIR) program
  • 528. Nektar Therapeutics
  • 529. Nest.Bio Ventures
  • 530. Nestlé
  • 531. Neuropore Therapies
  • 532. NeuroTheryX
  • 533. New Protein Capital
  • 534. New Wave Ventures
  • 535. New World TMT
  • 536. New York Medical College
  • 537. NewDo Venture
  • 538. Nex Cubed
  • 539. nference
  • 540. NJF Capital
  • 541. Nonacus
  • 542. Northpond Ventures
  • 543. Notable Labs
  • 544. Novartis
  • 545. Novo Holdings
  • 546. Novo Nordisk
  • 547. NPIF - Maven Equity Finance
  • 548. Numedii
  • 549. Nuritas
  • 550. NVIDIA
  • 551. O2h Ventures
  • 552. Oak Ridge National Laboratory
  • 553. Obvious Ventures
  • 554. OCA Ventures
  • 555. OccamzRazor
  • 556. Octopus Ventures
  • 557. Olaris
  • 558. Oncologie
  • 559. OncoStatyx
  • 560. One Way Ventures
  • 561. OneThree Biotech
  • 562. Ono Pharmaceutical
  • 563. Optibrium
  • 564. Optum Venture
  • 565. OrbiMed
  • 566. OS Fund
  • 567. OSE Immunotherapeutics
  • 568. OSEO
  • 569. Overkill Ventures
  • 570. OVP Venture Partners
  • 571. OWKIN
  • 572. Oxford Drug Design
  • 573. Panache Ventures
  • 574. PAREXEL
  • 575. Parinvest
  • 576. Parker Institute for Cancer Immunotherapy
  • 577. Parkinson's UK
  • 578. Partner Fund Management
  • 579. Pavilion Capital
  • 580. PEACCEL
  • 581. Pear VC
  • 582. PENDING.AI
  • 583. Pentech Ventures
  • 584. Peptone
  • 585. Peptris Technologies
  • 586. PercayAI
  • 587. Perceptive Advisors
  • 588. Pfizer
  • 589. Pfizer Venture Investments
  • 590. Pharmacelera
  • 591. Pharmavite
  • 592. PharmCADD
  • 593. PharmEnable
  • 594. Pharnext
  • 595. Pharos iBio
  • 596. Phenomic AI
  • 597. PhoreMost
  • 598. Pi Campus
  • 599. PIKAS d.o.o.
  • 600. Plex Research
  • 601. Plug and Play Ventures
  • 602. Polaris Partners
  • 603. Polaris Quantum Biotech
  • 604. Polyclone Bioservices
  • 605. Porton
  • 606. PostEra
  • 607. PrecisionLife
  • 608. Predictive Oncology
  • 609. Prefix Capital
  • 610. Presight Capital
  • 611. Primary Venture Partners
  • 612. Prime Movers Lab
  • 613. Primordial Genetics
  • 614. Prism Pharma
  • 615. Promega
  • 616. Propagator Ventures
  • 617. ProteinQure
  • 618. ProteiQ Biosciences
  • 619. QIAGEN
  • 620. Qiming Venture Partners
  • 621. Quantitative Medicine
  • 622. Qulab
  • 623. RA Capital Management
  • 624. Radical Ventures
  • 625. Rahko
  • 626. Ramen Ventures
  • 627. RaQualia Pharma
  • 628. Real Ventures
  • 629. RealHealthData
  • 630. Recursion Pharmaceuticals
  • 631. Redalpine
  • 632. Redbiotec
  • 633. Redmile Group
  • 634. Redpoint Ventures
  • 635. Refactor Capital
  • 636. Regeneron Pharmaceuticals
  • 637. Regional Cancer Centre (RCC)
  • 638. Relation Therapeutics
  • 639. Relay Therapeutics
  • 640. Remedium AI
  • 641. RenalytixAI
  • 642. Reneo Capital
  • 643. Renren
  • 644. ReproCell
  • 645. Repurpose.AI
  • 646. Research Triangle Park
  • 647. Resonant Therapeutics
  • 648. Reverie Labs
  • 649. ReviveMed
  • 650. Rigetti Computing
  • 651. Rising Tide
  • 652. Rivas Capital
  • 653. Roche
  • 654. Romulus Capital
  • 655. Rough Draft Ventures
  • 656. RT Partners
  • 657. Samsara BioCapital
  • 658. Sanabil Investments
  • 659. Sanofi
  • 660. Sanofi
  • 661. Santen Pharmaceutical
  • 662. Saphetor
  • 663. Sapio Sciences
  • 664. Sapir Venture Partners
  • 665. Sarepta Therapeutics
  • 666. SARomics Biostructures
  • 667. Saverna Therapeutics
  • 668. Schrödinger
  • 669. SciFi VC
  • 670. Scripps Research
  • 671. Sea Lane Ventures
  • 672. Searchbolt
  • 673. Selvita
  • 674. Sema4
  • 675. SEngine Precision Medicine
  • 676. Seoul National University
  • 677. Sequoia Capital
  • 678. Sequoia China
  • 679. Seraph Group
  • 680. Serra Ventures
  • 681. Servier
  • 682. Siemens
  • 683. SIG
  • 684. Sinequa
  • 685. Sinopia Biosciences
  • 686. Sinovation Ventures
  • 687. Sirenas
  • 688. SK Biopharmaceuticals
  • 689. SK Holdings
  • 690. Smilegate Investment
  • 691. Sofinnova Partners
  • 692. SoftBank Ventures
  • 693. SoftTech VC
  • 694. Solasta Ventures
  • 695. SolveBio
  • 696. SOM Biotech
  • 697. Soma Capital
  • 698. SOSV
  • 699. SparkBeyond
  • 700. Spektron Systems
  • 701. Spring Discovery
  • 702. Square 1 Bank
  • 703. SR One
  • 704. SRI International
  • 705. Stage Venture Partners
  • 706. Standigm
  • 707. Stanford University
  • 708. StarFinder
  • 709. Startupbootcamp
  • 710. StartX Fund
  • 711. Stemmore
  • 712. StemoniX
  • 713. Stonehaven
  • 714. Structura Biotechnology
  • 715. Sunfish Partners
  • 716. Sunwest Bank
  • 717. Susa Ventures
  • 718. Sustainable Conversion Ventures
  • 719. Sutter Health
  • 720. SV Angel
  • 721. Synsight
  • 722. Syntekabio
  • 723. Synthelis
  • 724. Systems Oncology
  • 725. Taisho Pharmaceutical
  • 726. Takeda Development Center Americas
  • 727. Takeda Pharmaceutical
  • 728. Tanabe Research Laboratories
  • 729. Tanarra
  • 730. TARA Biosystems
  • 731. Tasly Pharmaceutical
  • 732. Tavistock Group
  • 733. TB Alliance
  • 734. Team Builder Ventures
  • 735. Techammer
  • 736. TechU
  • 737. Tekla Capital Management
  • 738. Temasek Holdings
  • 739. TenOneTen Ventures
  • 740. Terra Magnum Capital Partners
  • 741. TeselaGen
  • 742. Teva Pharmaceuticals
  • 743. TF Bioinformatics
  • 744. The Buck Institute and
  • 745. The Column Group
  • 746. The Cure Parkinson's Trust (CPT)
  • 747. The Edge Software Consultancy
  • 748. The Longevity Fund
  • 749. The Partnership Fund for New York City
  • 750. The Pritzker Organization
  • 751. The Yozma Group Korea
  • 752. THERAMetrics
  • 753. Third Kind Venture Capital
  • 754. Third Rock Ventures
  • 755. Three Lakes Partners
  • 756. Threshold Ventures
  • 757. Tillotts Pharma
  • 758. Timewise Investment
  • 759. Top Technology Ventures
  • 760. Topspin Fund
  • 761. Toyohashi University of Technology
  • 762. TPG Biotech
  • 763. TPG Capital
  • 764. Transcriptic
  • 765. Transilico
  • 766. Translational Medicine Accelerator
  • 767. Trinitas Capital
  • 768. True Ventures
  • 769. Truffle Capital
  • 770. TSVC
  • 771. Turbine.AI
  • 772. Twin Ventures
  • 773. Two Sigma Ventures
  • 774. twoXAR Pharmaceuticals
  • 775. U.S. Securities and Exchange Commission (SEC)
  • 776. UC Riverside
  • 777. UK Biobank
  • 778. Uncork Capital
  • 779. Uni-innovate group
  • 780. Universal Materials Incubator
  • 781. University College London
  • 782. University Health Network
  • 783. University Hospital Institute Méditerranée Infection
  • 784. University of California
  • 785. University of Chicago
  • 786. University of Connecticut
  • 787. University of Groningen
  • 788. University of Kentucky
  • 789. University of Leeds
  • 790. University of Manitoba
  • 791. University of Miami
  • 792. University of Michigan College of Pharmacy
  • 793. University of Michigan Life Sciences
  • 794. University of Minnesota
  • 795. University of Nottingham
  • 796. University of Oxford
  • 797. University of Pittsburgh
  • 798. University of Toronto
  • 799. University of Wisconsin-Milwaukee Research Foundation
  • 800. University of North Carolina
  • 801. Unnatural Products
  • 802. UPPthera
  • 803. Upsher-Smith Laboratories
  • 804. VantAI
  • 805. Verge Genomics
  • 806. VeriSIM
  • 807. Versant Ventures
  • 808. Viking Global Investors
  • 809. Village Global
  • 810. Vingyani
  • 811. Vir Biotechnology
  • 812. VisVires New Protein
  • 813. Vium
  • 814. Vlaams Instituut voor Biotechnologie (VIB)
  • 815. VYASA Analytics
  • 816. Watson Fund
  • 817. Wave
  • 818. Wheatsheaf Group
  • 819. WI Harper
  • 820. Wild Basin Investments
  • 821. Wiley
  • 822. Wisecube
  • 823. Woodford Investment Management
  • 824. WorldQuant Ventures
  • 825. WRF Capital
  • 826. WuXi AppTec
  • 827. WuXi Biologics
  • 828. Wuxi Biortus Biosciences
  • 829. WuXi Venture Capital
  • 830. X-37
  • 831. X-Chem
  • 832. XtalPi
  • 833. Y Combinator
  • 834. Yael Capital
  • 835. Yale School of Medicine
  • 836. YITU Technology
  • 837. Yonsei University College of Medicine
  • 838. Yuhan
  • 839. YunFeng Capital
  • 840. Zastra
  • 841. ZebiAI
  • 842. ZhenFund
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