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AI-based Drug Discovery Market

AI-based Drug Discovery Market (2nd Edition): Distribution by Drug Discovery Steps (Target Identification / Validation, Hit Generation / Lead Identification, Lead Optimization), Therapeutic Area (Oncological Disorders, CNS Disorders, Infectious Diseases, Respiratory Disorders, Cardiovascular Disorders, Endocrine Disorders, Gastrointestinal Disorders, Musculoskeletal Disorders, Immunological Disorders, Dermatological Disorders and Others) and Key Geographies (North America, Europe, Asia-Pacific, Latin America, MENA, and RoW): Industry Trends and Global Forecasts, 2022-2035

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Example Insights

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Overview

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

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

Scope of the Report

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

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

The opinions and insights presented in the report were also influenced by discussions held with senior stakeholders in the industry. The report features detailed transcripts of interviews held with the following individuals:

  • Steve Yemm (Chief Commercial Officer, Aigenpulse) and Satnam Surae (Chief Product Officer, Aigenpulse)
  • Ed Addison (Co-founder, Chairman and Chief Executive Officer, Cloud Pharmaceuticals)
  • Bo Ram Beck (Head Researcher, DEARGEN)
  • Simon Haworth (Chief Executive Officer, Intelligent Omics)
  • Immanuel Lerner (Chief Executive Officer and Co-Founder, Pepticom)

Key Questions Answered

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

Contents

Chapter Outlines

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

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

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

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

Chapter 6 consists of detailed profiles of the prominent players (shortlisted based on a proprietary criterion) that are engaged in AI-based drug discovery domain in Europe. Each profile provides an overview of the company, its AI-based drug discovery technology portfolio and details on recent developments, as well as an informed future outlook.
Chapter 7 consists of detailed profiles of the prominent players (shortlisted based on a proprietary criterion) that are engaged in AI-based drug discovery domain in Asia-Pacific. Each profile provides an overview of the company, its AI-based drug discovery technology portfolio and details on recent developments, as well as an informed future outlook.

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

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

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

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

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

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

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

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

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

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

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

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

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. Chapter Overview
3.2. Artificial Intelligence
3.3. Subsets of AI
3.3.1. Machine Learning
3.3.1.1. Supervised Learning
3.3.1.2. Unsupervised Learning
3.3.1.3. Reinforced / Reinforcement Learning
3.3.1.4. Deep Learning
3.3.1.5. Natural Language Processing (NLP)

3.4. Data Science
3.5. Applications of AI in Healthcare
3.5.1. Drug Discovery
3.5.2. Disease Prediction, Diagnosis and Treatment
3.5.3. Manufacturing and Supply Chain Operations
3.5.4. Marketing
3.5.5. Clinical Trials

3.6. AI in Drug Discovery
3.6.1. Identification of Pathway and Target
3.6.2. Identification of Hit or Lead
3.6.3. Lead Optimization
3.6.4. Synthesis of Drug-Like Compounds

3.7. Advantages of Using AI in the Drug Discovery Process
3.8. Challenges Associated with the Adoption of AI
3.9. Concluding Remarks

4. COMPETITIVE LANDSCAPE
4.1. Chapter Overview
4.2. AI-based Drug Discovery: Overall Market Landscape
4.2.1. Analysis by Year of Establishment
4.2.2. Analysis by Company Size
4.2.3. Analysis by Location of Headquarters
4.2.4. Analysis by Type of Company
4.2.5. Analysis by Type of AI Technology
4.2.6. Analysis by Drug Discovery Steps
4.2.7. Analysis by Type of Drug Molecule
4.2.8. Analysis by Drug Development Initiatives
4.2.9. Analysis by Technology Licensing Option
4.2.10. Analysis by Target Therapeutic Area
4.2.11. Key Players: Analysis by Number of Platforms / Tools Available

5. COMPANY PROFILES: AI-BASED DRUG DISCOVERY PROVIDERS IN NORTH AMERICA
5.1. Chapter Overview
5.2. Atomwise
5.2.1. Company Overview
5.2.2. AI-based Drug Discovery Technology Portfolio
5.2.3. Recent Developments and Future Outlook

5.3. BioSyntagma
5.3.1. Company Overview
5.3.2. AI-based Drug Discovery Technology Portfolio
5.3.3. Recent Developments and Future Outlook

5.4. Collaborations Pharmaceuticals
5.4.1. Company Overview
5.4.2. AI-based Drug Discovery Technology Portfolio
5.4.3. Recent Developments and Future Outlook

5.5. Cyclica
5.5.1. Company Overview
5.5.2. AI-based Drug Discovery Technology Portfolio
5.5.3. Recent Developments and Future Outlook

5.6. InveniAI
5.6.1. Company Overview
5.6.2. AI-based Drug Discovery Technology Portfolio
5.6.3. Recent Developments and Future Outlook

5.7. Recursion Pharmaceuticals
5.7.1. Company Overview
5.7.2. AI-based Drug Discovery Technology Portfolio
5.7.3. Recent Developments and Future Outlook

5.8. Valo Health
5.8.1. Company Overview
5.8.2. AI-based Drug Discovery Technology Portfolio
5.8.3. Recent Developments and Future Outlook

6. COMPANY PROFILES: AI-BASED DRUG DISOCVERY SERVICE PROVIDERS IN EUROPE
6.1. Chapter Overview
6.2. Aiforia Technologies
6.2.1. Company Overview
6.2.2. AI-based Drug Discovery Technology Portfolio
6.2.3. Recent Developments and Future Outlook

6.3. Chemalive
6.3.1. Company Overview
6.3.2. AI-based Drug Discovery Technology Portfolio
6.3.3. Recent Developments and Future Outlook

6.4. DeepMatter
6.4.1. Company Overview
6.4.2. AI-based Drug Discovery Technology Portfolio
6.4.3. Recent Developments and Future Outlook

6.5. Exscientia
6.5.1. Company Overview
6.5.2. AI-based Drug Discovery Technology Portfolio
6.5.3. Recent Developments and Future Outlook

6.6. MAbSilico
6.6.1. Company Overview
6.6.2. AI-based Drug Discovery Technology Portfolio
6.6.3. Recent Developments and Future Outlook

6.7. Optibrium
6.7.1. Company Overview
6.7.2. AI-based Drug Discovery Technology Portfolio
6.7.3. Recent Developments and Future Outlook

6.8. Sensyne Health
6.8.1. Company Overview
6.8.2. AI-based Drug Discovery Technology Portfolio
6.8.3. Recent Developments and Future Outlook

7. COMPANY PROFILES: AI-BASED DRUG DISOCVERY SERVICE PROVIDERS IN ASIA PACIFIC
7.1. Chapter Overview
7.2. 3BIGS
7.2.1. Company Overview
7.2.2. AI-based Drug Discovery Technology Portfolio
7.2.3. Recent Developments and Future Outlook

7.3. Gero
7.3.1. Company Overview
7.3.2. AI-based Drug Discovery Technology Portfolio
7.3.3. Recent Developments and Future Outlook

7.4. Insilico Medicine
7.4.1. Company Overview
7.4.2. AI-based Drug Discovery Technology Portfolio
7.4.3. Recent Developments and Future Outlook

7.5. KeenEye
7.5.1. Company Overview
7.5.2. AI-based Drug Discovery Technology Portfolio
7.5.3. Recent Developments and Future Outlook

8. PARTNERSHIPS AND COLLABORATIONS
8.1. Chapter Overview
8.2. Partnership Models
8.3. AI-based Drug Discovery: Partnerships and Collaborations
8.3.1. Analysis by Year of Partnership
8.3.2. Analysis by Type of Partnership
8.3.3. Analysis by Year and Type of Partnership
8.3.4. Analysis by Target Therapeutic Area
8.3.5. Analysis by Focus Area
8.3.6. Analysis by Year of Partnership and Focus Area
8.3.7. Analysis by Type of Partner Company
8.3.8. Analysis by Type of Partnership and Type of Partner Company
8.3.9. Most Active Players: Analysis by Number of Partnerships
8.3.10. Analysis by Region
8.3.11.1. Intercontinental and Intracontinental Deals
8.3.11.2. International and Local Deals

9. FUNDING AND INVESTMENT ANALYSIS
9.1. Chapter Overview
9.2. Types of Funding
9.3. AI-based Drug Discovery: Funding and Investments
9.3.1. Analysis of Number of Funding Instances by Year
9.3.2. Analysis of Amount Invested by Year
9.3.3. Analysis by Type of Funding
9.3.4. Analysis of Amount Invested and Type of Funding
9.3.5. Analysis of Amount Invested by Company Size
9.3.6. Analysis by Type of Investor
9.3.7. Analysis of Amount Invested by Type of Investor
9.3.8. Most Active Players: Analysis by Number of Funding Instances
9.3.9. Most Active Players: Analysis by Amount Invested
9.3.10. Most Active Investors: Analysis by Number of Funding Instances
9.3.11. Analysis of Amount Invested by Geography
9.3.11.1. Analysis by Region 
9.3.11.2. Analysis by Country 

10. PATENT ANALYSIS
10.1. Chapter Overview
10.2. Scope and Methodology
10.3. AI-based Drug Discovery: Patent Analysis
10.3.1 Analysis by Application Year
10.3.2. Analysis by Geography
10.3.3. Analysis by CPC Symbols
10.3.4. Analysis by Emerging Focus Areas
10.3.5. Analysis by Type of Applicant
10.3.6. Leading Players: Analysis by Number of Patents

10.4. AI-based Drug Discovery: Patent Benchmarking
10.4.1. Analysis by Patent Characteristics

10.5. AI-based Drug Discovery: Patent Valuation

11. PORTER’S FIVE FORCES ANALYSIS
11.1. Chapter Overview
11.2. Methodology and Assumptions
11.3. Key Parameters
11.3.1. Threats of New Entrants
11.3.2. Bargaining Power of Drug Developers
11.3.3. Bargaining Power of Companies Using AI for Drug Discovery
11.3.4. Threats of Substitute Technologies
11.3.5. Rivalry Among Existing Competitors
11.4. Concluding Remarks

12. COMPANY VALUATION ANALYSIS
12.1. Chapter Overview
12.2. Company Valuation Analysis: Key Parameters
12.3. Methodology
12.3.1. Employee Score
12.3.2. Experience Score
12.3.3. Portfolio Strength / Uniqueness Score
12.3.4. Funding Score
12.3.5. Partnerships Score
12.3.6. Weighted Average Score
12.4. Company Valuation Analysis: Roots Analysis Proprietary Scores

13. AI-BASED HEALTHCARE INITIATIVES OF TECHNOLOGY GIANTS
13.1 Chapter Overview
13.1.1. Amazon Web Services
13.1.2. Microsoft
13.1.3. Intel
13.1.4. Alibaba Cloud
13.1.5. Siemens
13.1.6. Google
13.1.7. IBM

14. COST SAVING ANALYSIS
14.1. Chapter Overview
14.2. Key Assumptions and Methodology
14.3. Overall Cost Saving Potential Associated with Use of AI-based Solutions in Drug
14.3.1. Likely Cost Savings: Analysis by Drug Discovery Steps, 2022-2035
14.3.1.1. Likely Cost Savings During Target Identification / Validation, 2022-2035
14.3.1.2. Likely Cost Savings During Hit Generation / Lead Identification, 2022-2035
14.3.1.3. Likely Cost Savings During Lead Optimization, 2022-2035

14.3.2. Likely Cost Savings: Analysis by Target Therapeutic Area, 2022-2035
14.3.2.1. Likely Cost Savings for Drugs Targeting Oncological Disorders, 2022-2035
14.3.2.2. Likely Cost Savings for Drugs Targeting Neurological Disorders, 2022-2035
14.3.2.3. Likely Cost Savings for Drugs Targeting Infectious Diseases, 2022-2035
14.3.2.4. Likely Cost Savings for Drugs Targeting Respiratory Disorders, 2022-2035
14.3.2.5. Likely Cost Savings for Drugs Targeting Cardiovascular Disorders, 2022-2035
14.3.2.6. Likely Cost Savings for Drugs Targeting Endocrine Disorders, 2022-2035
14.3.2.7. Likely Cost Savings for Drugs Targeting Gastrointestinal Disorders, 2022-2035
14.3.2.8. Likely Cost Savings for Drugs Targeting Musculoskeletal Disorders, 2022-2035
14.3.2.9. Likely Cost Savings for Drugs Targeting Immunological Disorders, 2022-2035
14.3.2.10. Likely Cost Savings for Drugs Targeting Dermatological Disorders, 2022-2035
14.3.2.11. Likely Cost Savings for Drugs Targeting Other Disorders, 2022-2035

14.3.3. Likely Cost Savings: Analysis by Geography, 2022-2035
14.3.3.1. Likely Cost Savings in North America, 2022-2035
14.3.3.2. Likely Cost Savings in Europe, 2022-2035
14.3.3.3. Likely Cost Savings in Asia Pacific, 2022-2035
14.3.3.4. Likely Cost Savings in MENA, 2022-2035
14.3.3.5. Likely Cost Savings in Latin America, 2022-2035
14.3.3.6. Likely Cost Savings in Rest of the World, 2022-2035

15. MARKET FORECAST
15.1. Chapter Overview
15.2. Key Assumptions and Methodology

15.3. Global AI-based Drug Discovery Market, 2022-2035
15.3.1. AI-based Drug Discovery Market: Distribution by Drug Discovery Steps, 2022-2035
15.3.1.1. AI-based Drug Discovery Market for Target Identification / Validation, 2022-2035
15.3.1.2. AI-based Drug Discovery Market for Hit Generation / Lead Identification, 2022-2035
15.3.1.3. AI-based Drug Discovery Market for Lead Optimization, 2022-2035

15.3.2. AI-based Drug Discovery Market: Distribution by Target Therapeutic Area, 2022-2035
15.3.2.1. AI-based Drug Discovery Market for Oncological Disorders, 2022-2035
15.3.2.2. AI-based Drug Discovery Market for Neurological Disorders, 2022-2035
15.3.2.3. AI-based Drug Discovery Market for Infectious Diseases, 2022-2035
15.3.2.4. AI-based Drug Discovery Market for Respiratory Disorders, 2022-2035
15.3.2.5. AI-based Drug Discovery Market for Cardiovascular Disorders, 2022-2035
15.3.2.6. AI-based Drug Discovery Market for Endocrine Disorders, 2022-2035
15.3.2.7. AI-based Drug Discovery Market for Gastrointestinal Disorders, 2022-2035
15.3.2.8. AI-based Drug Discovery Market for Musculoskeletal Disorders, 2022-2035
15.3.2.9. AI-based Drug Discovery Market for Immunological Disorders, 2022-2035
15.3.2.10. AI-based Drug Discovery Market for Dermatological Disorders, 2022-2035
15.3.2.11. AI-based Drug Discovery Market for Other Disorders, 2022-2035

15.3.3. AI-based Drug Discovery Market: Distribution by Geography, 2022-2035
15.3.3.1. AI-based Drug Discovery Market in North America, 2022-2035
15.3.3.1.1. AI-based Drug Discovery Market in the US, 2022-2035
15.3.3.1.2. AI-based Drug Discovery Market in Canada, 2022-2035

15.3.3.2. AI-based Drug Discovery Market in Europe, 2022-2035
15.3.3.2.1. AI-based Drug Discovery Market in the UK, 2022-2035
15.3.3.2.2. AI-based Drug Discovery Market in France, 2022-2035
15.3.3.2.3. AI-based Drug Discovery Market in Germany, 2022-2035
15.3.3.2.4. AI-based Drug Discovery Market in Spain, 2022-2035
15.3.3.2.5. AI-based Drug Discovery Market in Italy, 2022-2035
15.3.3.2.6. AI-based Drug Discovery Market in Rest of Europe, 2022-2035

15.3.3.3. AI-based Drug Discovery Market in Asia Pacific, 2020-2035
15.3.3.3.1. AI-based Drug Discovery Market in China, 2022-2035
15.3.3.3.2. AI-based Drug Discovery Market in India, 2022-2035
15.3.3.3.3. AI-based Drug Discovery Market in Japan, 2022-2035
15.3.3.3.4. AI-based Drug Discovery Market in Australia, 2022-2035
15.3.3.3.5. AI-based Drug Discovery Market in South Korea, 2022-2035

15.3.3.4. AI-based Drug Discovery Market in MENA, 2022-2035
15.3.3.4.1. AI-based Drug Discovery Market in Saudi Arabia, 2022-2035
15.3.3.4.2. AI-based Drug Discovery Market in UAE, 2022-2035
15.3.3.4.3. AI-based Drug Discovery Market in Iran, 2022-2035

15.3.3.5. AI-based Drug Discovery Market in Latin America, 2022-2035
15.3.3.5.1. AI-based Drug Discovery Market in Argentina, 2022-2035

15.3.3.6. AI-based Drug Discovery Market in Rest of the World, 2022-2035

16. CONCLUSION

17. EXECUTIVE INSIGHTS
17.1. Chapter Overview
17.2. Aigenpulse
17.2.1. Company Snapshot
17.2.2. Interview Transcript: Steve Yemm (Chief Commercial Officer) and Satnam Surae (Chief Product Officer)

17.3. Cloud Pharmaceuticals
17.3.1. Company Snapshot
17.3.2. Interview Transcript: Ed Addison (Co-founder, Chairman and Chief Executive Officer)

17.4. DEARGEN
17.4.1. Company Snapshot
17.4.2. Interview Transcript: Bo Ram Beck (Head Researcher)

17.5. Intelligent Omics
17.5.1. Company Snapshot
17.5.2. Interview Transcript: Simon Haworth (Chief Executive Officer)

17.6. Pepticom
17.6.1. Company Snapshot
17.6.2. Interview Transcript: Immanuel Lerner (Chief Executive Officer, Co-Founder)
17.7. Sage-N Research
17.7.1. Company Snapshot
17.7.2. Interview Transcript: David Chiang (Chairman)

18. APPENDIX I: TABULATED DATA

19. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

List Of Figures

Figure 2.1 Executive Summary: Overall Market Landscape
Figure 2.2 Executive Summary: Partnerships and Collaborations
Figure 2.3 Executive Summary: Funding and Investment Analysis
Figure 2.4 Executive Summary: Patent Analysis
Figure 2.5 Executive Summary: Cost Saving Analysis
Figure 2.6 Executive Summary: Market Forecast
Figure 3.1 Evolution of AI
Figure 3.2. Key Segments of AI
Figure 3.3. Interconnection between Data Science, Artificial Intelligence and Big Data
Figure 3.4. Applications of AI
Figure 4.1. AI-based Drug Discovery: Distribution by Year of Establishment
Figure 4.2. AI-based Drug Discovery: Distribution by Company Size
Figure 4.3. AI-based Drug Discovery: Distribution by Location of Headquarters (Region-Wise)
Figure 4.4. AI-based Drug Discovery: Distribution by Location of Headquarters (Country-Wise)
Figure 4.5. AI-based Drug Discovery: Distribution by Company Size and Location of Headquarters
Figure 4.6. AI-based Drug Discovery: Distribution by Type of Company
Figure 4.7. AI-based Drug Discovery: Distribution by Type of AI Technology
Figure 4.8. AI-based Drug Discovery: Distribution by Drug Discovery Steps
Figure 4.9. AI-based Drug Discovery: Distribution by Type of Drug Molecule
Figure 4.10. AI-based Drug Discovery: Distribution by Drug Development Initiatives
Figure 4.11. AI-based Drug Discovery: Distribution by Technology Licensing Option
Figure 4.12. AI-based Drug Discovery: Distribution by Target Therapeutic Area
Figure 4.13. Key Players: Distribution by Number of Platforms / Tools Available
Figure 8.1 Partnerships and Collaborations: Cumulative Year-wise Trend
Figure 8.2 Partnerships and Collaborations: Distribution by Type of Partnership
Figure 8.3 Partnerships and Collaborations: Distribution by Year and Type of Partnership
Figure 8.4 Partnerships and Collaborations: Distribution by Target Therapeutic Area
Figure 8.5 Partnerships and Collaborations: Distribution by Focus Area
Figure 8.6 Partnerships and Collaborations: Distribution by Year of Partnership and Focus Area
Figure 8.7 Partnerships and Collaborations: Distribution by Type of Partner Company
Figure 8.8 Partnerships and Collaborations: Distribution by Type of Partner Company and Type of Partnerships
Figure 8.9 Most Active Players: Distribution by Number of Partnerships
Figure 8.10 Partnerships and Collaborations: Distribution of Intercontinental and Intracontinental Deals
Figure 8.11 Partnerships and Collaborations: Distribution of International and Local Deals
Figure 9.1 Funding and Investment Analysis: Cumulative Year-wise Distribution of Funding Instances, 2006-2022
Figure 9.2 Funding and Investment Analysis: Cumulative Year-wise Distribution of Amount Invested (USD Million), 2006-2022
Figure 9.3 Funding and Investment Analysis: Distribution of Instances by Type of Funding
Figure 9.4 Funding and Investment Analysis: Distribution of Amount Invested by Type of Funding (USD Million)
Figure 9.6 Funding and Investment Analysis: Distribution of Amount Invested by Company Size (USD Million)
Figure 9.7 Funding and Investment Analysis: Distribution of Number of Funding Instances by Type of Investor
Figure 9.8 Funding and Investment Analysis: Distribution of Amount Invested by Type of Investor (USD Million)
Figure 9.9 Most Active Players: Distribution by Number of Funding Instances
Figure 9.10 Most Active Players: Distribution by Amount Invested
Figure 9.11 Most Active Investors: Distribution by Number of Funding Instances
Figure 9.12 Funding and Investment: Distribution of Amount Invested by Region (USD Million)
Figure 9.13 Funding and Investment: Distribution of Amount Invested by Country (USD Million)
Figure 10.1 Patent Analysis: Distribution by Type of Patent
Figure 10.2 Patent Analysis: Distribution by Application Year
Figure 10.3 Patent Analysis: Distribution by Location of Patent Jurisdiction (Region-wise)
Figure 10.4 Patent Analysis: Distribution by Location of Patent Jurisdiction (Country-wise)
Figure 10.5 Patent Analysis: Distribution by CPC Symbols
Figure 10.6 Patent Analysis: Emerging Focus Area
Figure 10.7 Patent Analysis: Cumulative Year-wise Distribution by Type of Applicant
Figure 10.8 Leading Industry Players: Distribution by Number of Patents
Figure 10.9 Leading Non-Industry Players: Distribution by Number of Patents
Figure 10.10 Leading Patent Assignees: Distribution by Number of Patents
Figure 10.11 Patent Benchmarking: Distribution of Leading Industry Players by Patent Characteristics (CPC Symbols)
Figure 10.12 Patent Analysis: Distribution by Age
Figure 10.13 Patent Analysis: Patent Valuation
Figure 11.1 Porters Five Forces: Key Parameters
Figure 11.2 Porters Five Forces: Harvey Ball Analysis
Figure 12.1 Company Valuation Analysis: Categorization by Employee Score
Figure 12.2 Company Valuation Analysis: Categorization by Experience Score
Figure 12.3 Company Valuation Analysis: Categorization by Portfolio Strength / Uniqueness Score
Figure 12.4 Company Valuation Analysis: Categorization by Funding Score
Figure 12.5 Company Valuation Analysis: Categorization by Partnership Score
Figure 12.6 Company Valuation Analysis: Categorization by Weighted Average Score
Figure 14.1 Overall Cost Saving Potential Associated with Use of AI-based Solutions in Drug Discovery, 2022-2035 (USD Million)
Figure 14.2 Likely Cost Savings: Distribution by Drug Discovery Steps, 2022-2035 (USD Million)
Figure 14.3 Likely Cost Savings During Target Identification / Validation, 2022-2035 (USD Million)
Figure 14.4 Likely Cost Savings During Hit Generation / Lead Identification, 2022-2035 (USD Million)
Figure 14.5 Likely Cost Savings During Lead Optimization, 2022-2035 (USD Million)
Figure 14.6 Likely Cost Savings: Distribution by Target Therapeutic Area, 2022-2035 (USD Million)
Figure 14.7 Likely Cost Savings for Drugs Targeting Oncological Disorders, 2022-2035 (USD Million)
Figure 14.8 Likely Cost Savings for Drugs Targeting Neurological Disorders, 2022-2035 (USD Million)
Figure 14.9 Likely Cost Savings for Drugs Targeting Infectious Diseases, 2022-2035 (USD Million)
Figure 14.10 Likely Cost Savings for Drugs Targeting Respiratory Disorders, 2022-2035 (USD Million)
Figure 14.11 Likely Cost Savings for Drugs Targeting Cardiovascular Disorders, 2022-2035 (USD Million)
Figure 14.12 Likely Cost Savings for Drugs Targeting Endocrine Disorders, 2022-2035 (USD Million)
Figure 14.13 Likely Cost Savings for Drugs Targeting Gastrointestinal Disorders, 2022-2035 (USD Million)
Figure 14.14 Likely Cost Savings for Drugs Targeting Musculoskeletal Disorders, 2022-2035 (USD Million)
Figure 14.15 Likely Cost Savings for Drugs Targeting Immunological Disorders, 2022-2035 (USD Million)
Figure 14.16 Likely Cost Savings for Drugs Targeting Dermatological Disorders, 2022-2035 (USD Million)
Figure 14.17 Likely Cost Savings for Drugs Targeting Other Disorders, 2022-2035 (USD Million)
Figure 14.18 Likely Cost Savings: Distribution by Geography, 2022-2035 (USD Million)
Figure 14.19 Likely Cost Savings in North America, 2022-2035 (USD Million)
Figure 14.20 Likely Cost Savings in Europe, 2022-2035 (USD Million)
Figure 14.21 Likely Cost Savings in Asia Pacific, 2022-2035 (USD Million)
Figure 14.22 Likely Cost Savings in MENA, 2022-2035 (USD Million)
Figure 14.23 Likely Cost Savings in Latin America, 2022-2035 (USD Million)
Figure 14.24 Likely Cost Savings in Rest of the World, 2022-2035 (USD Million)
Figure 15.1 Global AI-based Drug Discovery Market, 2022-2035 (USD Million)
Figure 15.2 AI-based Drug Discovery Market: Distribution by Drug Discovery Steps, 2022-2035 (USD Million)
Figure 15.3 AI-based Drug Discovery Market for Target Identification / Validation, 2022-2035 (USD Million)
Figure 15.4 AI-based Drug Discovery Market for Hit Generation / Lead Identification, 2022-2035 (USD Million)
Figure 15.5 AI-based Drug Discovery Market for Lead Optimization, 2022-2035 (USD Million)
Figure 15.6 AI-based Drug Discovery Market: Distribution by Target Therapeutic Area, 2022-2035 (USD Million)
Figure 15.7 AI-based Drug Discovery Market for Oncological Disorders, 2022-2035 (USD Million)
Figure 15.8 AI-based Drug Discovery Market for Neurological Disorders 2022-2035 (USD Million)
Figure 15.9 AI-based Drug Discovery Market for Infectious Diseases, 2022-2035 (USD Million)
Figure 15.10 AI-based Drug Discovery Market for Respiratory Disorders, 2022-2035 (USD Million)
Figure 15.11 AI-based Drug Discovery Market for Cardiovascular Disorders, 2022-2035 (USD Million)
Figure 15.12 AI-based Drug Discovery Market for Endocrine Disorders, 2022-2035 (USD Million)
Figure 15.13 AI-based Drug Discovery Market for Gastrointestinal Disorders, 2022-2035 (USD Million)
Figure 15.14 AI-based Drug Discovery Market for Musculoskeletal Disorders, 2022-2035 (USD Million)
Figure 15.15 AI-based Drug Discovery Market for Immunological Disorders, 2022-2035 (USD Million)
Figure 15.16 AI-based Drug Discovery Market for Dermatological Disorders, 2022-2035 (USD Million)
Figure 15.17 AI-based Drug Discovery Market for Other Disorders, 2022-2035 (USD Million)
Figure 15.18 AI-based Drug Discovery Market: Distribution by Geography, 2022-2035 (USD Million)
Figure 15.19 AI-based Drug Discovery Market in North America, 2022-2035 (USD Million)
Figure 15.20 AI-based Drug Discovery Market in the US, 2022-2035 (USD Million)
Figure 15.21 AI-based Drug Discovery Market in Canada, 2022-2035 (USD Million)
Figure 15.22 AI-based Drug Discovery Market in Europe, 2022-2035 (USD Million)
Figure 15.23 AI-based Drug Discovery Market in UK, 2022-2035 (USD Million)
Figure 15.24 AI-based Drug Discovery Market in France, 2022-2035 (USD Million)
Figure 15.25 AI-based Drug Discovery Market in Germany, 2022-2035 (USD Million)
Figure 15.26 AI-based Drug Discovery Market in Spain, 2022-2035 (USD Million)
Figure 15.27 AI-based Drug Discovery Market in Italy, 2022-2035 (USD Million)
Figure 15.28 AI-based Drug Discovery Market in Rest of Europe, 2022 - 2035 (USD Million)
Figure 15.29 AI-based Drug Discovery Market in Asia Pacific, 2022-2035 (USD Million)
Figure 15.30 AI-based Drug Discovery Market in China, 2022-2035 (USD Million)
Figure 15.31 AI-based Drug Discovery Market in India, 2022-2035 (USD Million)
Figure 15.32 AI-based Drug Discovery Market in Japan, 2022-2035 (USD Million)
Figure 15.33 AI-based Drug Discovery Market in Australia, 2022-2035 (USD Million)
Figure 15.34 AI-based Drug Discovery Market in South Korea, 2022-2035 (USD Million)
Figure 15.35 AI-based Drug Discovery Market in MENA, 2022-2035 (USD Million)
Figure 15.36 AI-based Drug Discovery Market in Saudi Arabia, 2022-2035 (USD Million)
Figure 15.37 AI-based Drug Discovery Market in UAE, 2022-2035 (USD Million)
Figure 15.38 AI-based Drug Discovery Market in Iran, 2022-2035 (USD Million)
Figure 15.39 AI-based Drug Discovery Market in Latin America, 2022-2035 (USD Million)
Figure 15.40 AI-based Drug Discovery Market in Argentina, 2022-2035 (USD Million)
Figure 15.41 AI-based Drug Discovery Market in Rest of the World, 2022-2035 (USD Million)
Figure 16.1 Concluding Remarks: Current Market Landscape
Figure 16.2 Concluding Remarks: Partnerships and Collaborations
Figure 16.3 Concluding Remarks: Funding and Investments
Figure 16.4 Concluding Remarks: Patent Analysis
Figure 16.5 Concluding Remarks: Company Valuation Analysis
Figure 16.6 Concluding Remarks: Cost Saving Analysis
Figure 16.7 Concluding Remarks: Market Forecast

List Of Tables

Table 4.1 AI-based Drug Discovery Service / Technology Providers: Information on Year of Establishment, Company Size, Location of Headquarters, Number and Name of Platforms / Tools Available
Table 4.2 AI-based Drug Discovery Service / Technology Providers: Information on Type of AI Technology and Drug Discovery Steps
Table 4.3 AI-based Drug Discovery Service / Technology Providers: Information on Type of Drug Molecule and Target Therapeutic Area
Table 5.1 Leading AI-based Drug Discovery Service / Technology Providers in North America
Table 5.2 Atomwise: Company Snapshot
Table 5.3 Atomwise: AI-based Drug Discovery Technologies
Table 5.4 Atomwise: Recent Developments and Future Outlook
Table 5.5 BioSyntagma: Company Snapshot
Table 5.6 BioSyntagma: AI-based Drug Discovery Technologies
Table 5.7 BioSyntagma: Recent Developments and Future Outlook
Table 5.8 Collaborations Pharmaceuticals: Company Snapshot
Table 5.9 Collaborations Pharmaceuticals: AI-based Drug Discovery Technologies
Table 5.10 Collaborations Pharmaceuticals: Recent Developments and Future Outlook
Table 5.11 Cyclica: Company Snapshot
Table 5.12 Cyclica: AI-based Drug Discovery Technologies
Table 5.13 Cyclica: Recent Developments and Future Outlook
Table 5.14 InveniAI: Company Snapshot
Table 5.15 InveniAI: AI-based Drug Discovery Technologies
Table 5.16 InveniAI: Recent Developments and Future Outlook
Table 5.17 Recursion Pharmaceuticals: Company Snapshot
Table 5.18 Recursion Pharmaceuticals: AI-based Drug Discovery Technologies
Table 5.19 Recursion Pharmaceuticals: Recent Developments and Future Outlook
Table 5.20 Valo Health: Company Snapshot
Table 5.21 Valo Health: AI-based Drug Discovery Technologies
Table 5.22 Valo Health: Recent Developments and Future Outlook
Table 6.1 Leading AI-based Drug Discovery Service / Technology Providers in Europe
Table 6.2 Aiforia Technologies: Company Snapshot
Table 6.3 Aiforia Technologies: AI-based Drug Discovery Technologies
Table 6.4 Aiforia Technologies: Recent Developments and Future Outlook
Table 6.5 Chemalive: Company Snapshot
Table 6.6 Chemalive: AI-based Drug Discovery Technologies
Table 6.7 Chemalive: Recent Developments and Future Outlook
Table 6.8 DeepMatter: Company Snapshot
Table 6.9 DeepMatter: AI-based Drug Discovery Technologies
Table 6.10 DeepMatter: Recent Developments and Future Outlook
Table 6.11 Exscientia: Company Snapshot
Table 6.12 Exscientia: AI-based Drug Discovery Technologies
Table 6.13 Exscientia: Recent Developments and Future Outlook
Table 6.14 MAbSilico: Company Snapshot
Table 6.15 MAbSilico: AI-based Drug Discovery Technologies
Table 6.16 MAbSilico: Recent Developments and Future Outlook
Table 6.17 Optibrium: Company Snapshot
Table 6.18 Optibrium: AI-based Drug Discovery Technologies
Table 6.19 Optibrium: Recent Developments and Future Outlook
Table 6.20 Sensyne Health: Company Snapshot
Table 6.21 Sensyne Health: AI-based Drug Discovery Technologies
Table 6.22 Sensyne Health: Recent Developments and Future Outlook
Table 7.1 Leading AI-based Drug Discovery Service / Technology Providers in Asia Pacific
Table 7.2 3BIGS: Company Snapshot
Table 7.3 3BIGS: AI-based Drug Discovery Technologies
Table 7.4 3BIGS: Recent Developments and Future Outlook
Table 7.5 Gero: Company Snapshot
Table 7.6 Gero: AI-based Drug Discovery Technologies
Table 7.7 Gero: Recent Developments and Future Outlook
Table 7.8 Insilico Medicine: Company Snapshot
Table 7.9 Insilico Medicine: AI-based Drug Discovery Technologies
Table 7.10 Insilico Medicine: Recent Developments and Future Outlook
Table 7.11 KeenEye: Company Snapshot
Table 7.12 KeenEye: AI-based Drug Discovery Technologies
Table 7.13 KeenEye: Recent Developments and Future Outlook
Table 8.1 AI-based Drug Discovery Service / Technology Providers: List of Partnerships and Collaborations, 2009-2022
Table 9.1 AI-based Drug Discovery Service / Technology Providers: List of Funding and Investments, 2006-2022
Table 10.1 Patent Analysis: Prominent CPC Symbols
Table 10.2 Patent Analysis: Most Popular CPC Symbols
Table 10.3 Patent Analysis: List of Top CPC Symbols
Table 10.4 Patent Analysis: Summary of Benchmarking Analysis
Table 10.5 Patent Analysis: Categorization based on Weighted Valuation Scores
Table 10.6 Patent Portfolio: List of Leading Patents (in terms of Highest Relative Valuation)
Table 10.7 Patent Portfolio: List of Leading Patents (in terms of Number of Citations)
Table 11.1. Company Valuation Analysis: Weighted Average Score
Table 11.2. Company Valuation Analysis: Estimated Valuation
Table 17.1 Aigenpulse: Company Snapshot
Table 17.2 Cloud Pharmaceuticals: Company Snapshot
Table 17.3 DEARGEN: Company Snapshot
Table 17.4 Intelligent Omics: Company Snapshot
Table 17.5 Pepticom: Company Snapshot
Table 17.6 Sage-N Research: Company Snapshot
Table 18.1 AI-based Drug Discovery: Distribution by Year of Establishment
Table 18.2 AI-based Drug Discovery: Distribution by Company Size
Table 18.3 AI-based Drug Discovery: Distribution by Location of Headquarters (Region-Wise)
Table 18.4 AI-based Drug Discovery: Distribution by Location of Headquarters (Country-Wise)
Table 18.5 AI-based Drug Discovery: Distribution by Company Size and Location of Headquarters
Table 18.6 AI-based Drug Discovery: Distribution by Type of Company
Table 18.7 AI-based Drug Discovery: Distribution by Type of AI Technology
Table 18.8 AI-based Drug Discovery: Distribution by Drug Discovery Steps
Table 18.9 AI-based Drug Discovery: Distribution by Type of Drug Molecule
Table 18.10. AI-based Drug Discovery: Distribution by Drug Development Initiatives
Table 18.11 AI-based Drug Discovery: Distribution by Technology Licensing Option
Table 18.12 AI-based Drug Discovery: Distribution by Target Therapeutic Area
Table 18.13 AI-based Drug Discovery: Distribution by Number of Platforms / Tools Available
Table 18.14 Partnerships and Collaborations: Cumulative Year-wise Trend
Table 18.15 Partnerships and Collaborations: Distribution by Type of Partnership
Table 18.16 Partnerships and Collaborations: Distribution by Year and Type of Partnership
Table 18.17 Partnerships and Collaborations: Distribution by Target Therapeutic Area
Table 18.18 Partnerships and Collaborations: Distribution by Focus Area
Table 18.19 Partnerships and Collaborations: Distribution by Year of Partnership and Focus Area
Table 18.20 Partnerships and Collaborations: Distribution by Type of Partner Company
Table 18.21 Partnerships and Collaborations: Distribution by Type of Partner Company and Type of Partnerships
Table 18.22 Most Active Players: Distribution by Number of Partnerships
Table 18.23 Partnerships and Collaborations: Distribution of Intercontinental and Intracontinental Deals
Table 18.24 Partnerships and Collaborations: Distribution of International and Local Deals
Table 18.25 Funding and Investment Analysis: Cumulative Year-wise Distribution of Funding Instances, 2006-2022
Table 18.26 Funding and Investment Analysis: Cumulative Year-wise Distribution of Amount Invested (USD Million), 2006-2022
Table 18.27 Funding and Investment Analysis: Distribution of Instances by Type of Funding
Table 18.28 Funding and Investment Analysis: Distribution of Amount Invested by Type of Funding (USD Million)
Table 18.29 Funding and Investment Analysis: Distribution of Amount Invested by Year and Type of Funding (USD Million)
Table 18.30 Funding and Investment Analysis: Distribution of Amount Invested by Company Size (USD Million)
Table 18.31 Funding and Investment Analysis: Distribution of Funding Instances by Type of Investor
Table 18.32 Funding and Investment Analysis: Distribution of Amount Invested by Investor (USD Million)
Table 18.33 Most Active Players: Distribution by Number of Funding Instances
Table 18.34 Most Active Players: Distribution by Amount Invested
Table 18.35 Most Active Investors: Distribution by Number of Funding Instances
Table 18.36 Funding and Investment: Distribution of Amount Invested by Region (USD Million)
Table 18.37 Funding and Investment: Distribution of Amount Invested by Geography (Country-wise) (USD Million)
Table 18.38 Patent Analysis: Distribution by Type of Patent
Table 18.39 Patent Analysis: Distribution by Year of Publication
Table 18.40 Patent Analysis: Distribution by Location of Patent Jurisdiction (Region-wise)
Table 18.41 Patent Analysis: Distribution by Location of Patent Jurisdiction (Country-wise)
Table 18.42 Patent Analysis: Distribution by CPC Symbols
Table 18.43 Patent Analysis: Emerging Focus Area
Table 18.44 Patent Analysis: Cumulative Year-wise Distribution by Type of Applicant
Table 18.45 Leading Industry Players: Distribution by Number of Patents
Table 18.46 Leading Non-Industry Players: Distribution by Number of Patents
Table 18.47 Leading Patent Assignees: Distribution by Number of Patents
Table 18.48 Patent Benchmarking: Distribution of Leading Industry Player by Patent Characteristics (CPC Symbols)
Table 18.49 Patent Analysis: Distribution by Age
Table 18.50 Patent Analysis: Patent Valuation
Table 18.51 Company Valuation Analysis: Categorization by Employee Score
Table 18.52 Company Valuation Analysis: Categorization by Experience Score
Table 18.53 Company Valuation Analysis: Categorization by Portfolio Score
Table 18.54 Company Valuation Analysis: Categorization by Partnerships Score
Table 18.55 Company Valuation Analysis: Categorization by Funding Score
Table 18.56 Company Valuation Analysis: Categorization by Weighted Average Score
Table 18.57 Overall Cost Saving Potential Associated with Use of AI-based Solutions in Drug Discovery, 2022-2035 (USD Million)
Table 18.58 Likely Cost Savings: Distribution by Drug Discovery Steps, 2022-2035 (USD Million)
Table 18.59 Likely Cost Savings During Target Identification / Validation, 2022-2035 (USD Million)
Table 18.60 Likely Cost Savings During Hit Generation / Lead Identification, 2022-2035 (USD Million)
Table 18.61 Likely Cost Savings During Lead Optimization, 2022-2035 (USD Million)
Table 18.62 Likely Cost Savings: Distribution by Target Therapeutic Area, 2022-2035 (USD Million)
Table 18.63 Likely Cost Savings for Drugs Targeting Oncological Disorders, 2022-2035 (USD Million)
Table 18.64 Likely Cost Savings for Drugs Targeting Neurological Disorders, 2022-2035 (USD Million)
Table 18.65 Likely Cost Savings for Drugs Targeting Infectious Diseases, 2022-2035 (USD Million)
Table 18.66 Likely Cost Savings for Drugs Targeting Respiratory Disorders, 2022-2035 (USD Million)
Table 18.67 Likely Cost Savings for Drugs Targeting Cardiovascular Disorders, 2022-2035 (USD Million)
Table 18.68 Likely Cost Savings for Drugs Targeting Endocrine Disorders, 2022-2035 (USD Million)
Table 18.69 Likely Cost Savings for Drugs Targeting Gastrointestinal Disorders, 2022-2035 (USD Million)
Table 18.70 Likely Cost Savings for Drugs Targeting Musculoskeletal Disorders, 2022-2035 (USD Million)
Table 18.71 Likely Cost Savings for Drugs Targeting Immunological Disorders, 2022-2035 (USD Million)
Table 18.72 Likely Cost Savings for Drugs Targeting Dermatological Disorders, 2022-2035 (USD Million)
Table 18.73 Likely Cost Savings for Drugs Targeting Other Disorders, 2022-2035 (USD Million)
Table 18.74 Likely Cost Savings: Distribution by Geography, 2022-2035 (USD Million)
Table 18.75 Likely Cost Savings in North America, 2022-2035 (USD Million)
Table 18.76 Likely Cost Savings in Europe, 2022-2035 (USD Million)
Table 18.77 Likely Cost Savings in Asia Pacific, 2022-2035 (USD Million)
Table 18.78 Likely Cost Savings in MENA, 2022-2035 (USD Million)
Table 18.80 Likely Cost Savings in Latin America, 2022-2035 (USD Million)
Table 18.81 Likely Cost Savings in Rest of the World, 2022-2035 (USD Million)
Table 18.82 Global AI-based Drug Discovery Market, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.83 AI-based Drug Discovery Market: Distribution by Drug Discovery Steps, 2022-2035 (USD Million)
Table 18.84 AI-based Drug Discovery Market for Target Identification / Validation, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.85 AI-based Drug Discovery Market for Hit Generation / Lead Identification, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.86 AI-based Drug Discovery Market for Lead Optimization, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.87 AI-based Drug Discovery Market: Distribution by Target Therapeutic Area, 2022-2035 (USD Million)
Table 18.88 AI-based Drug Discovery Market for Oncological Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.89 AI-based Drug Discovery Market for Neurological Disorders Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.90 AI-based Drug Discovery Market for Infectious Diseases, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.90 AI-based Drug Discovery Market for Respiratory Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.91 AI-based Drug Discovery Market for Cardiovascular Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.92 AI-based Drug Discovery Market for Endocrine Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.93 AI-based Drug Discovery Market for Gastrointestinal Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.94 AI-based Drug Discovery Market for Musculoskeletal Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.95 AI-based Drug Discovery Market for Immunological Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.96 AI-based Drug Discovery Market for Dermatological Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.97 AI-based Drug Discovery Market for Other Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.98 AI-based Drug Discovery Market: Distribution by Geography, 2022-2035 (USD Million)
Table 18.99 AI-based Drug Discovery Market in North America, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.100 AI-based Drug Discovery Market in the US, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.101 AI-based Drug Discovery Market in Canada, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.102 AI-based Drug Discovery Market in Europe, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.103 AI-based Drug Discovery Market in UK, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.104 AI-based Drug Discovery Market in France, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.105 AI-based Drug Discovery Market in Germany, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.106 AI-based Drug Discovery Market in Spain, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.107 AI-based Drug Discovery Market in Italy, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.108 AI-based Drug Discovery Market in Rest of Europe, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.109 AI-based Drug Discovery Market in Asia Pacific, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.110 AI-based Drug Discovery Market in China, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.111 AI-based Drug Discovery Market in India, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.112 AI-based Drug Discovery Market in Japan, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.113 AI-based Drug Discovery Market in Australia, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.114 AI-based Drug Discovery Market in South Korea, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.115 AI-based Drug Discovery Market in MENA, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.116 AI-based Drug Discovery Market in Saudi Arabia, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.117 AI-based Drug Discovery Market in UAE, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.118 AI-based Drug Discovery Market in Iran, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.119 AI-based Drug Discovery Market in Latin America, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.120 AI-based Drug Discovery Market in Argentina, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)
Table 18.121 AI-based Drug Discovery Market in Rest of the World, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Million)

List Of Companies

The following companies / institutes / government bodies and organizations have been mentioned in this report.

  1. 3BIGS
  2. 3W Healthcare Fund
  3. 3W Partners
  4. 4B Technologies
  5. 5Y Capital
  6. 6 Dimensions Capital
  7. 8VC
  8. 99andBeyond
  9. A2A Pharmaceuticals
  10. A2i Therapeutics
  11. AARP Foundation
  12. AbbVie
  13. AbCellera
  14. Absci
  15. Abstract Ventures
  16. Accelerate Long Island
  17. Accenture
  18. Accutar Biotech
  19. Acellera
  20. Acequia Capital
  21. Acerand Therapeutics
  22. ACF Investors
  23. AcuraStem
  24. Adagene
  25. Adare Pharma Solutions
  26. ADC Therapeutics
  27. ADEL 
  28. adMare BioInnovations
  29. Advantage Capital
  30. AdventHealth
  31. Adynxx
  32. Aganitha
  33. Agent Capital
  34. AI Therapeutics
  35. Ai Vedam Technologies
  36. AI VIVO
  37. Ai-biopharma
  38. Aiforia
  39. Aigenpulse
  40. Air Street Capital
  41. Ajou University
  42. Akashi Therapeutics
  43. Aktia Nordic Micro Cap
  44. Albany Molecular Research
  45. A-Level Capital
  46. Alexandria Real Estate Equities
  47. Alexandria Venture Investments
  48. Allcyte
  49. Allergan
  50. AllianThera Biopharma
  51. Allosteric Bioscience
  52. Almirall
  53. Alphanosos
  54. ALS Association
  55. ALS Investment Fund
  56. Altos Ventures
  57. Amadeus Capital Partners
  58. AME Cloud Ventures
  59. Amgen Ventures
  60. Amidi
  61. Ampel Biosolutions
  62. Amplify Partners
  63. Amplitude
  64. Amyloid Solution
  65. Anagenesis Biotechnologies
  66. Andreessen Horowitz
  67. Angios
  68. Anima Biotech
  69. Ansa Biotechnologies
  70. Antengene
  71. Antiverse
  72. Apeiron Therapeutics
  73. ApexQubit
  74. APRINOIA Therapeutics
  75. Aqemia
  76. Arbutus Biopharma
  77. ARCH Venture Partners
  78. Arctoris
  79. Ardigen
  80. Aria Pharmaceuticals
  81. Arpeggio
  82. ArrowMark Partners
  83. ARTIS Ventures
  84. Arzeda
  85. Asset Management Ventures
  86. Astellas Pharma
  87. Astia
  88. AstraZeneca
  89. Astrogen
  90. atai Life Sciences 
  91. Ataxia
  92. Atinum Invest­ment
  93. Atlantic Labs
  94. Atlas Venture
  95. Atomico
  96. Atomwise
  97. Atrius Health
  98. AUM Biosciences 
  99. Auransa
  100. Aurinvest
  101. Autism Impact Fund
  102. AV8 Ventures
  103. AVIC Trust
  104. Avidity Partners
  105. B Capital Group
  106. BABEL Ventures
  107. Baidu Ventures
  108. Balderton Capital
  109. Bangarang
  110. BASSETTI
  111. Baupost
  112. Bavarian Nordic
  113. Bayer
  114. Beiersdorf
  115. BenevolentAI
  116. BERG
  117. Better Ventures
  118. Bezos Expeditions
  119. BigHat Biosciences
  120. Bill & Melinda Gates Foundation
  121. BioAge Labs
  122. Bioeconomy Capital
  123. BioFocus DPI
  124. Biogen
  125. BioLizard
  126. Biolojic Design
  127. BioMarin Pharmaceutical
  128. Biomea Fusion
  129. BioMotiv
  130. BioPharmics
  131. Biorelate
  132. Biortus
  133. Bios Partners
  134. BioSymetrics
  135. bioSyntagma
  136. biotx.ai
  137. BioVentures MedTech Funds
  138. Bioverge
  139. Biovista
  140. BioXcel Therapeutics
  141. BlackRock
  142. Bloomberg Beta
  143. Blue Bear Ventures
  144. Blue Oak Pharmaceuticals
  145. bluebird bio
  146. Boehringer Ingelheim
  147. Bold Capital Partners
  148. Bpifrance
  149. Brace Pharma Capital
  150. Brain Canada
  151. Breakout Labs
  152. BridgeBio Pharma
  153. Brigham and Women's Hospital
  154. Brightspark Ventures
  155. Bristol Myers Squibb
  156. btov
  157. Buck Institute for Research on Aging
  158. Builders VC
  159. Bulba Ventures
  160. Busolantix Investment
  161. BVF Partners
  162. C4X Discovery
  163. CaaS Capital Management
  164. Caffeinated Capital
  165. Califia Pharma
  166. California Institute for Biomedical Research
  167. California Institute for Regenerative Medicine
  168. CALYM Carnot Institute
  169. Cambia Health Solutions
  170. Cambridge Angels
  171. Cambridge Cancer Genomics
  172. Cambridge Crystallographic Data Center
  173. Cambridge Enterprise
  174. Cambridge Research for International Research
  175. Cambridge University Hospitals NHS Foundation Trust
  176. Cantos Ventures
  177. CARB-X 
  178. CaroCure
  179. Casdin Capital
  180. Catalio Capital Management
  181. Catapult Ventures
  182. Cathay Innovation
  183. Causaly
  184. CB Clean Lux
  185. CDH Investments
  186. Celgene
  187. Cellarity
  188. Celsius Therapeutics
  189. Center for the Development of Industrial Technology (CDTI)
  190. CENTOGENE
  191. Cerebras
  192. Cerevel Therapeutics
  193. Charcot–Marie–Tooth
  194. Charles River Laboratories
  195. Chartered Group
  196. ChemAlive
  197. Chemaxon 
  198. ChemDiv
  199. ChemPass
  200. Chiesi Farmaceutici
  201. Children's Tumor Foundation (CTF)
  202. China Canada Angels Alliance
  203. China Life Healthcare Fund
  204. China Oncology Focus
  205. Chinese Academy of Medical Sciences
  206. Cigna Ventures
  207. City Hill Ventures
  208. Civilization Ventures
  209. CJ HEALTHCARE
  210. Claremont Creek Ventures
  211. Clarus Ventures
  212. Cleveland Clinic
  213. CLI Ventures
  214. Cloud Pharmaceuticals
  215. CM-CIC Innovation
  216. CMT Research Foundation (CMTRF)
  217. Coatue
  218. Code Ocean
  219. Collaborations Pharmaceuticals
  220. Collaborative Drug Discovery
  221. Collective Scientific 
  222. Colorcon Ventures
  223. Colt Ventures
  224. Computational biology
  225. Congressionally Directed Medical Research Programs (CDMRP)
  226. Conifer Point Pharmaceuticals
  227. Cormorant Asset Management
  228. Cortex Discovery
  229. Cosine
  230. Cota Capital
  231. Courier Therapeutics
  232. COVID-19 Vaccine Corporation (CVC)
  233. CPE
  234. CPP Investments
  235. CQDM
  236. Creative Destruction Lab
  237. Cresset
  238. CRV
  239. CrystalGenomics
  240. CTI Life Sciences Fund
  241. Cultivian Sandbox Ventures
  242. Cyclica
  243. CytoReason
  244. Daewoong Pharmaceuticals
  245. Daily Partners
  246. Danhua Venture Capital (DHVC)
  247. Dante Labs
  248. Data2Discovery
  249. Data4cure
  250. Datavant
  251. DCVC
  252. Deargen
  253. Debiopharm
  254. Deep Genomics
  255. Deep Knowledge Ventures
  256. Deep Track Capital
  257. DeepCure
  258. DeepMatter
  259. DeepTrait
  260. Deerfield Management
  261. DEFTA Partners
  262. Delin Ventures
  263. Denali Therapeutics
  264. Denovicon Therapeutics
  265. Denovium
  266. Department of Health and Social Care (DHSC)
  267. Development Bank of Wales
  268. DEXSTR
  269. Diamond Light Source
  270. Dolby Family Ventures
  271. Dow AgroSciences
  272. Driehaus Capital Management
  273. Drive Capital
  274. Droia Ventures
  275. Drugs for Neglected Diseases initiative (DNDi)
  276. dRx Capital
  277. DSC Investment
  278. Dualogics
  279. Dynamk Capital
  280. Dyno Therapeutics
  281. Echo Health Ventures
  282. EcoR1 Capital
  283. EDBI
  284. Edge Capital
  285. eFlasks
  286. EIC Accelerator
  287. Eight Roads Ventures
  288. EIT Climate-KIC
  289. Elevian
  290. Eli Lilly
  291. Elsevier
  292. Elucidata
  293. Empire State Development (ESD)
  294. Empirico
  295. Enamine
  296. Endogena Therapeutics
  297. Endure Capital
  298. Engine Biosciences
  299. Enterprise Ireland
  300. Envisagenics
  301. Epic Capital Management
  302. EPIC Ventures
  303. Epicombi Therapeutics
  304. Epredia
  305. EQRx
  306. Erasca
  307. e-therapeutics
  308. Euretos
  309. European Bank for Reconstruction and Development (EBRD)
  310. European Investment Bank
  311. European Union
  312. Eurostars
  313. Evaxion Biotech
  314. Everest Medicines
  315. Evotec
  316. Ewha Womans University
  317. Excelra
  318. Executive Agency for Small and Medium-sized Enterprises (EASME)
  319. Exelixis
  320. Exscientia
  321. Facio Therapies
  322. Farallon Capital
  323. Federal Economic Development Agency for Southern Ontario
  324. Felicis Ventures
  325. Ferring Pharmaceuticals
  326. Fidelity Asia Fund
  327. Fidelity Biosciences
  328. Fidelity Management & Research Company
  329. Financière Boscary
  330. FinLab EOS VC
  331. First Round Capital
  332. First Star Ventures
  333. Flagship Pioneering
  334. Foresite Capital
  335. Forma Therapeutics
  336. Formic Ventures
  337. Foundation for Angelman Syndrome Therapeutics (FAST)
  338. Founders Factory
  339. Founders Fund
  340. Fountain Therapeutics
  341. Fox Chase Cancer Center 
  342. F-Prime Capital (Formerly known as Fidelity Biosciences)
  343. Frazier Life Sciences
  344. Frees Fund
  345. Friedreich’s Ataxia Research Alliance (FARA)
  346. Frontier Medicines
  347. FundersClub
  348. Future Ventures
  349. FutuRx
  350. G3 Therapeutics
  351. Galapagos
  352. Gaorong Capital
  353. Garage Capital
  354. Gatehouse Bio
  355. GC Pharma
  356. Genentech
  357. General Atlantic
  358. General Catalyst
  359. Genesen
  360. Genesis Therapeutics
  361. GenFleet Therapeutics
  362. Genialis
  363. GenoKey ApS
  364. Genomatica 
  365. Genome Biologics
  366. Genomics England
  367. Genuity Science
  368. Gero
  369. Gi Global Health Fund LP
  370. Gilead Sciences
  371. GlaxoSmithKline
  372. Global Brain
  373. Global Founders Capital
  374. Global Genomics Series
  375. GM&C Life Sciences Fund
  376. GNS Healthcare
  377. Golden Ventures
  378. Goodman Capital
  379. Google
  380. Gopher Asset Management
  381. Gordian 
  382. Government of Canada 
  383. Government of Switzerland
  384. GP Healthcare Capital
  385. GPG Ventures
  386. Grand Challenges Canada
  387. GrayMatter
  388. Great Ormond Street Hospital
  389. Greater Paris University Hospitals - AP-HP 
  390. Green Park & Golf Ventures
  391. GreenSky Capital
  392. Gritstone Oncology
  393. Grove Ventures
  394. GT Healthcare Capital Partners
  395. Guangdong Institute of Microbiology
  396. Gustave Roussy
  397. GV (formerly known as Google Ventures)
  398. Hafnium Labs
  399. Hampshire Hospitals NHS Foundation Trust
  400. Hana Ventures
  401. Hanmi Pharmaceutical
  402. Hansoh Pharma
  403. Harbour Antibodies
  404. Harbour BioMed
  405. Harris & Harris
  406. Harvard Innovation Labs
  407. Haystack Science
  408. HBM Health­care Invest­ments
  409. HCS Pharma
  410. Health Wildcatters
  411. HealthInc
  412. Healx
  413. Helmholtz Institute for Pharmaceutical Research Saarland (HIPS)
  414. Heritage Provider Network
  415. HEWLETT PACKARD
  416. Hibiskus BioPharma
  417. Hike Ventures
  418. Hinge Therapeutics
  419. Hiventures
  420. HLTH
  421. HOF Capital
  422. HotSpot Therapeutics
  423. Hoxton Ventures
  424. Huadong Medicine
  425. Human Capital
  426. Humonic
  427. Huons
  428. Hyperplane Venture Capital
  429. IA Ventures
  430. IBM Research
  431. IBM TechU
  432. ICHOR
  433. IDG Capital
  434. Iktos
  435. IMM Investment
  436. ImmuMap Services
  437. ImmuneMed
  438. Immunocure
  439. Impact Therapeutics
  440. Index Ventures
  441. IndieBio
  442. Indivumed
  443. Infinity Medical
  444. InfoChem (acquired by DeepMatter)
  445. InnoPharmaScreen
  446. Innophore
  447. Innoplexus
  448. Innospark Ventures
  449. Innova31
  450. Innovate NY Fund
  451. Innovate UK
  452. Innovation Endeavors
  453. Innovation Fund Denmark
  454. Innovative Medicines Initiative (IMI)
  455. Inovia Capital
  456. inSili.com
  457. Insiliance
  458. Insilico Medicine
  459. insitro
  460. Institut Pasteur
  461. Intel Capital
  462. Intellegens
  463. IntelliCyt
  464. Intelligent OMICS
  465. Intermountain Ventures
  466. Interprotein
  467. Intuition Systems
  468. InveniAI
  469. Invetx
  470. Invus
  471. Ionis Pharmaceuticals
  472. IP Group
  473. IPF Partners
  474. IQVIA
  475. Ireland Strategic Investment Fund (ISIF)
  476. IRICoR
  477. Iris Pharma
  478. Irving Investors
  479. I-STEM
  480. IT-translation
  481. IvyCap Ventures
  482. IWAKI SEIYAKU
  483. JADBio
  484. Janssen
  485. Jefferson Health
  486. Jiangsu Chia Tai Fenghai Pharmaceutical (CTFH)
  487. Jiangsu Hengrui Pharmaceuticals
  488. JLABS
  489. Johnson & Johnson
  490. Johnson & Johnson Innovation
  491. Jove Equity Partners
  492. Juvena Therapeutics
  493. Juvenescence
  494. JW Pharmaceutical
  495. K Cube Ventures
  496. Kadmon
  497. Kaiser Permanente
  498. KB Securities
  499. KDB Capital
  500. Kebotix
  501. Keen Eye
  502. Keio University 
  503. KemPharm
  504. Kennedy Krieger Institute
  505. Kester Capital
  506. Khosla Ventures
  507. Kindred
  508. Kinetic Discovery
  509. King Star Capital
  510. King's College London
  511. Kinogen
  512. Koch Dis­rup­tive Tech­nolo­gies
  513. Kodiak Sciences
  514. Korea Atomic Energy Research Institute
  515. Korea Development Bank
  516. Korea Fixed-Income Investment Advisory
  517. Korea Investment Partners
  518. Korea Research Institute of Chemical Technology 
  519. ksilink
  520. KTB Network
  521. Kuano
  522. Kyowa Kirin
  523. La Financiere Gaspard
  524. Labcyte
  525. LabGenius
  526. LabKey
  527. Lake Bleu Capital
  528. Lansdowne Partners
  529. Lantern Pharma
  530. LanzaTech
  531. Laurent and Benon
  532. Laurion Capital Management
  533. Lawrence Livermore National Laboratory
  534. Laxai Life Sciences
  535. LB Investment
  536. Leaps by Bayer
  537. Legend Capital
  538. Leland Stanford Junior University
  539. LEO Pharma
  540. Lequio Pharma
  541. Lhasa
  542. Life Sciences Institute
  543. LifeArc
  544. Lifeforce Capital 
  545. LifeSci NYC 
  546. LifeSci Venture Partners
  547. Lightspeed Venture Partners
  548. Lilly Asia Ventures
  549. Linguamatics
  550. Lodo Therapeutics
  551. Long Island Emerging Technologies Fund (LIETF)
  552. Longevity Fund
  553. Loup Ventures
  554. LSP
  555. Luminous Ventures
  556. Lundbeck
  557. Lux Capital
  558. Luxembourg Centre for Systems Biomedicine (LCSB)
  559. M12 - Microsoft's Venture Fund
  560. MAbSilico
  561. Macroceutics
  562. Madrona Venture
  563. Magnetic Ventures
  564. Maison Capital
  565. Manchester Tech Trust Angels
  566. Mannin Research
  567. Maple Capital Management
  568. Marathon Venture Capital
  569. MaRS Catalyst Fund
  570. Marshall Wace
  571. Maruho
  572. Massachusetts Institute of Technology
  573. Massachusetts Life Sciences Center
  574. MassBiologics
  575. MassChallenge
  576. Maxygen
  577. MBC BioLabs
  578. Medchemica
  579. Medirita
  580. Memorial Sloan Kettering Cancer Center
  581. Menlo Ventures
  582. Menten AI
  583. Merck
  584. Merck Accelerator
  585. Merck KGaA
  586. Mercury Fund
  587. Meridian Street Capital
  588. Micar Innovation
  589. Michigan State University
  590. Microsoft
  591. Mila
  592. Mind the Byte
  593. Mirae Assest Venture Investment
  594. Mirae Asset Cap­i­tal Markets
  595. Mission: Cure
  596. MIT delta v
  597. Mitsui
  598. Model Medicines
  599. Moderna
  600. Molecular Health
  601. Molecule
  602. Molecule.one
  603. Moleculomics
  604. Molomics
  605. Monashee Investment Management
  606. Monsanto Growth Ventures
  607. Morningside
  608. Morpheus Ventures
  609. MPM Capital
  610. MRL Ventures Fund
  611. Mubadala
  612. Multiple Myeloma Research Foundation (MMRF)
  613. Muscular Dystrophy Association
  614. Mutuelle d'Assurances du Corps de Sante Francais (MACSF)
  615. myTomorrows
  616. Nan Fung Life Sciences
  617. Nanna Therapeutics
  618. Nantes University Hospital
  619. National Cancer Institute (NCI)
  620. National Center for Advancing Translational Sciences ( NCATS )
  621. National Centre for Research and Development
  622. National Fertilizers
  623. National Institute of Environmental Health Sciences (NIEHS)
  624. National Institute of General Medical Sciences (NIGMS)
  625. National Institute of Health (NIH)
  626. National Institute of Neurological Disorders and Stroke (NINDS)
  627. National Institute of Standards and Technology (NIST)
  628. National Institute on Aging (NIA)
  629. National Institutes of Health
  630. National Research Council Canada
  631. National Science Foundation (NSF)
  632. National Science Foundation Small Business Innovation Research (NSF SBIR)Program
  633. Nektar Therapeutics
  634. Nest.Bio Ventures
  635. Nestlé
  636. Netabolics
  637. Neuropore Therapies
  638. NeuroTheryX
  639. Neuroventi
  640. New Protein Capital
  641. New Wave Ventures
  642. New World TMT
  643. New York Medical College
  644. New York Ventures
  645. NewDo Venture
  646. Nex Cubed
  647. NineteenGale Therapeutics
  648. NJF Capital
  649. Nonacus
  650. Northern Powerhouse Investment Fund
  651. Notable Labs
  652. Novartis
  653. Novo Holdings
  654. Novo Nordisk
  655. Nucleai
  656. Numedii
  657. Numerate
  658. Nuritas
  659. NVIDIA
  660. O2H Ventures
  661. Oak Ridge National Laboratory
  662. Obvious Ventures
  663. OCA Ventures
  664. OccamzRazor
  665. Octopus Ventures
  666. Olaris
  667. OMNY Health
  668. OncoArendi Therapeutics
  669. Oncologie
  670. Oncology Venture (Acquired by Allarity Therapeutics)
  671. OneThree Biotech
  672. Ono Pharmaceutical
  673. Optibrium
  674. Optum Ventures
  675. OrbiMed
  676. OS Fund
  677. OSE Immunotherapeutics
  678. OSEO
  679. OurCrowd
  680. Overkill Ventures
  681. OVP Venture Partners
  682. Owkin
  683. Oxford Drug Design
  684. Oxford University
  685. Pacific Western Bank
  686. Panache Ventures
  687. PAQ Therapeutics
  688. Parinvest
  689. Parker Institute for Cancer Immunotherapy
  690. Parkinson’s UK
  691. ParticleX
  692. Partner Fund Management
  693. Pavilion Capital
  694. PEACCEL
  695. Pear VC
  696. Pending.AI
  697. Pentech Ventures
  698. Pepticom
  699. Peptilogics
  700. Peptone
  701. Peptris Technologies
  702. PercayAI
  703. Perceptive Advisors
  704. Pfizer
  705. Pfizer Venture Investments
  706. Pharmacelera
  707. Pharmavite
  708. PharmCADD
  709. PharmEnable
  710. Pharnext
  711. Pharos iBio
  712. Phenomic AI
  713. Phi-X Capital
  714. Phoenix Venture Partners
  715. PhoreMost
  716. Pi Campus
  717. PICC Capital
  718. Pictet
  719. Pikas
  720. Pivotal bioVenture Partners
  721. Plex
  722. Plug and Play Tech Center
  723. Polaris Partners
  724. Polaris Quantum Biotech
  725. Polyclone
  726. Polycystic Kidney Disease Charity
  727. Porton
  728. PostEra
  729. PrecisionLife
  730. Predictive Oncology
  731. Prefix Capital
  732. President International Development
  733. Presight Capital
  734. Primary Venture Partners
  735. Primavera Capital
  736. Prime Movers Lab
  737. Primordial Genetics
  738. PROSILICO
  739. ProteinQure
  740. ProteiQ Biosciences
  741. Pub­lic Sec­tor Pen­sion Invest­ment Board (PSP Invest­ments)
  742. QIAGEN
  743. Qiming Venture Partners
  744. Quantitative Medicine
  745. Quiet Capital
  746. QuLab
  747. RA Capital Management
  748. Radical ventures
  749. Ramen Ventures
  750. RaQualia Pharma
  751. Real Ventures
  752. RealHealthData
  753. Receptor.AI
  754. Recursion Pharmaceuticals
  755. Red Cell Partners
  756. Redalpine
  757. Redbiotec
  758. Redmile Group
  759. Redpoint Ventures
  760. Refactor Capital
  761. Regional Cancer Centre (RCC)
  762. Regional Council of Auvergne-Rhône-Alpes
  763. Rejuveron Life Sciences
  764. Relation Therapeutics
  765. Relay Therapeutics
  766. RELIEF THERAPEUTICS
  767. Remedium AI
  768. Reneo Capital
  769. Renren
  770. Repare Therapeutics
  771. REPROCELL
  772. Repurpose.AI
  773. Research Triangle Park
  774. Resonant Therapeutics
  775. Revelation Partners
  776. Reverie Labs
  777. ReviveMed
  778. Ridgeback Capital Investments
  779. Rigetti Computing
  780. Rising Tide Fund
  781. Rivas Capital
  782. Roche
  783. Rock Springs Capital
  784. Romulus Capital
  785. Rough Draft Ventures
  786. RRJ Capital
  787. RT Partners
  788. RwHealth
  789. Ryerson University
  790. Saehan Venture Capital
  791. Sage Partners
  792. Sage-N Research
  793. Saint Louis University
  794. Samsara BioCapital
  795. Samyang
  796. Sanabil Investments
  797. Sanford Burnham Prebys
  798. Sanofi
  799. Santen Pharmaceutical
  800. Sapir Venture Partners
  801. Sarepta Therapeutics
  802. SARomics Biostructures
  803. Saverna Therapeutics
  804. SciFi Technology Systems
  805. Scottish Mortgage Investment Trust
  806. Scripps Research
  807. SD Biosensor
  808. Sea Lane Ventures
  809. Searchbolt
  810. Sedec Therapeutics
  811. Selvita
  812. Sema4
  813. SEngine Precision Medicine
  814. Sensyne Health
  815. Sentauri
  816. Sequoia Capital
  817. Seraph Group
  818. Serra Ventures
  819. Servier
  820. Seventure Partners
  821. Sheba
  822. Sheffield Institute
  823. Shionogi
  824. Shivom
  825. Shuimu BioSciences
  826. Sidney Kimmel Comprehensive Cancer Center
  827. SIG
  828. SignalChem Lifesciences
  829. Signet Therapeutics
  830. Silexon
  831. Sinequa
  832. Singleron Biotechnologies
  833. Sino Biopharmaceutical
  834. Sinopia Biosciences
  835. Sinovation Ventures
  836. Sirenas
  837. SK Biopharmaceuticals 
  838. SK Chemicals
  839. Small Business Innovation Research (SBIR)
  840. Smilegate Investment
  841. Socium
  842. Sofinnova Partners
  843. SoftBank Ventures
  844. SoftBank Vision Fund
  845. Solasta Ventures
  846. Solve ME/CFS Initiative
  847. SOM Biotech
  848. Sookmyung Women’s University
  849. Sosei Heptares
  850. SOSV
  851. SparkBeyond
  852. Sparta
  853. Spektron Systems
  854. Sphera Funds Management
  855. Spring Discovery
  856. SR One
  857. SR One Capital Management
  858. Sravathi AI
  859. SRI International
  860. Stage Venture Partners
  861. Standigm
  862. Stanford University School of Medicine
  863. StarFinder Capital Fund
  864. Startupbootcamp
  865. StartX
  866. STEM&More
  867. StemoniX
  868. Stonehaven
  869. Structura Biotechnology
  870. Sunfish Partners
  871. Sunwest Bank
  872. Susa Ventures
  873. Sustainable Conversion Ventures
  874. SV Angel
  875. SyndicateRoom
  876. SYNSIGHT
  877. Syntekabio
  878. Synthelis
  879. SYSTEMS ONCOLOGY
  880. T. Rowe Price
  881. Tachyon
  882. Taisho Pharmaceutical
  883. Takeda
  884. Tanabe Research Laboratories
  885. Tanarra Credit Partners
  886. TARA Biosystems 
  887. Tasly Biopharmaceuticals
  888. Tavistock Group
  889. TB Alliance
  890. Team Builder Ventures
  891. Tech Incubation Program for Startup (TIPS)
  892. Techammer
  893. Tekla Capital Management
  894. Temasek
  895. Tencent
  896. TenOneTen ventures
  897. Tensor Ventures
  898. Terra Magnum Capital Partners
  899. Terray Therapeutics
  900. TeselaGen
  901. Teva Pharmaceutical
  902. TF Bioinformatics
  903. The BioCollective
  904. The Column Group
  905. The Cure Parkinson’s Trust (CPT)
  906. The Edge Software Consultancy
  907. The Heritage Group
  908. The Institute of Cancer Research
  909. The Johns Hopkins University School of Medicine
  910. The Michael J. Fox Foundation for Parkinson's Research (MJFF)
  911. The Pritzker Organization 
  912. The Royal Wolverhampton NHS Trust
  913. The Syndicate
  914. Third Kind Venture Capital (3KVC)
  915. Third Rock Ventures
  916. Three Lakes Partners
  917. Tillotts Pharma
  918. Timewise Investment
  919. TLV Partners
  920. Top Technology Ventures
  921. Topspin Fund
  922. Toyohashi University of Technology
  923. Transcriptic
  924. Transilico
  925. Trinitas Capital Management
  926. True Ventures
  927. Truffle Capital
  928. TS Investment
  929. TSVC
  930. Turbine
  931. Twin Ventures
  932. Two Sigma Ventures
  933. U.S. Securities and Exchange Commission
  934. UCB Pharma
  935. Uncork Capital
  936. United States Department of Health and Human Services
  937. Universal Materials Incubator
  938. University College London
  939. University Hospital Institute Méditerranée Infection
  940. University of Barcelona
  941. University of California San Diego
  942. University of Cambridge
  943. University of Chicago
  944. University of Connecticut
  945. University of Dundee
  946. University of Florida
  947. University of Groningen
  948. University of Kentucky 
  949. University of Leeds
  950. University of Manitoba
  951. University of Miami
  952. University of Michigan College of Pharmacy
  953. University of Milan
  954. University of Minnesota
  955. University of Nottingham
  956. University of Oxford
  957. University of Pittsburgh
  958. University of Toronto
  959. University of Wisconsin-Milwaukee Research Foundation
  960. University of North Carolina
  961. Unnatural Products
  962. UPPthera 
  963. Upsher Smith
  964. Usynova Pharmaceuticals
  965. Valence Discovery 
  966. Valo
  967. VantAI
  968. Vector Institute
  969. Vectr Ventures
  970. Verge Genomics
  971. VeriSIM
  972. Versant Ventures
  973. Vertex Ventures
  974. VIB
  975. Viking Global Investors
  976. Village Global
  977. Vingyani
  978. VisVires New Protein
  979. Vium
  980. Viva BioInnovator
  981. Vyant Bio (Formerly known as Cancer Genetics)
  982. Warburg Pincus
  983. Watson Foundation
  984. Wave
  985. West Lake Pharmaceutical Services
  986. Wheatsheaf
  987. WI Harper
  988. Wild Basin Investments
  989. Wisecube
  990. Woodford Investment Management
  991. Woodline Partners
  992. WorldQuant Ventures
  993. Wren Capital
  994. WRF Capital
  995. WuXi AppTec
  996. WuXi Biologics
  997. WuXi Healthcare Ventures
  998. Wuyuan Capital
  999. X-37
  1000. Xbiome
  1001. X-Chem
  1002. XenoTherapeutics
  1003. XtalPi
  1004. Y Combinator
  1005. Yael Capital
  1006. Yahui Investment
  1007. Yale School of Medicine
  1008. Yayi Capital
  1009. YiMei Capital
  1010. YITU Technology
  1011. Yonsei University College of Medicine
  1012. YOZMA GROUP KOREA
  1013. Yuhan Corporation
  1014. Yunfeng Capital
  1015. Zastra
  1016. ZebiAI
  1017. ZhenFund
  1018. Zymergen

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