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

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    November 2020

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AI-based-Drug-Discovery-Market-Distribution-By-Geography AI-based-Drug-Discovery-Market-Market-Forcast  

 

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?

Contents

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

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 Company

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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