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基於人工智能的藥物發現市場（第 2 版）：按藥物發現步驟（目標識別/驗證、命中創建/先導識別、先導優化）、治療領域（腫瘤疾病、中樞神經系統疾病）分佈

市場調查報告書

商品編碼

1071810

基於人工智能的藥物發現市場（第 2 版）：按藥物發現步驟（目標識別/驗證、命中創建/先導識別、先導優化）、治療領域（腫瘤疾病、中樞神經系統疾病）分佈

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

出版日期: 2022年04月26日 | 出版商:

Roots Analysis | 英文 335 Pages | 商品交期: 最快1-2個工作天內

價格

簡介目錄圖表

洞察範例

本報告調查了全球基於人工智能的藥物發現市場，並概述了市場，以及人工智能在醫療保健中的應用、人工智能在藥物發現中的應用、人工智能引入的問題、藥物發現步驟、治療領域的趨勢以及競爭形勢。，以及進入市場的公司概況。

章節概述
人工智能
人工智能的一個子集
- 機器學習
數據科學
人工智能在醫療保健中的應用
- 藥物發現
- 疾病預測、診斷和治療
- 製造和供應鏈運營
- 營銷
- 臨床試驗
藥物發現中的人工智能
- 通路和靶點的識別
- 命中或線索識別
- 讀取優化
- 類藥物化合物的合成
在藥物發現過程中使用 AI 的好處
與採用人工智能相關的問題
結束語

第4章競爭態勢

章節概述
基於人工智能的藥物發現：整體市場狀況

第 5 章企業簡介：北美基於 AI 的藥物發現提供商

章節概述
原子級
生物合成
合作製藥
循環
InveniAI
遞歸製藥
Valo 健康

第 6 章企業概況概述：歐洲基於 AI 的藥物發現提供商

章節概述
Aiforia Technologies
化學生活
深度物質
知識
MAbSilico
優化
Senshin Health

第7章企業概況概述：亞太地區基於人工智能的藥物發現提供商

章節概述
3BIGS
格羅
Insilico Medicine
銳眼

第 8 章夥伴關係和合作

章節概述
合作模式
基於 AI 的藥物發現：夥伴關係與合作

第九章融資與投資分析

章節概述
資金類型
基於人工智能的藥物發現：融資和投資

第10章專利分析

章節概述
範圍及調查方法
基於人工智能的藥物發現：專利分析
基於人工智能的藥物發現：專利基準
基於人工智能的藥物發現：專利評估

第11章波特五力分析

章節概述
假設是一種研究方法
主要參數
結束語

第十二章企業評價分析

章節概述
企業評價分析：關鍵參數
調查方法
企業評價分析：Roots analysis unique score

第 13 章領先技術公司的基於人工智能的醫療保健計劃

第14章成本降低分析

第15章市場預測

章節概述
重要的先決條件和研究方法
2022-2035 年全球基於 AI 的藥物發現市場

第16章結論

第 17 章執行洞察力

第18章附錄一：聚合數據

第十九章附錄二：公司和組織名單

簡介目錄圖表

Title:
AI-based Drug Discovery Market (2^nd 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.

Example Insights:

Some playrers have managed to establish strong competitive positions; in future, we expect multiple acquisitions to take place wherein the relative valuation of a firm is likely to be a key determinant.

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 (2^nd 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?

Chapter Outlines

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

1. PREFACE

1.1. Scope of the Report
1.2. Research Methodology
1.3. Key Questions Answered
1.4. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION

3.1. Chapter Overview
3.2. Artificial Intelligence
3.3. Subsets of AI
- 3.3.1. Machine Learning
  - 3.3.1.1. Supervised Learning
  - 3.3.1.2. Unsupervised Learning
  - 3.3.1.3. Reinforced / Reinforcement Learning
  - 3.3.1.4. Deep Learning
  - 3.3.1.5. Natural Language Processing (NLP)
3.4. Data Science
3.5. Applications of AI in Healthcare
- 3.5.1. Drug Discovery
- 3.5.2. Disease Prediction, Diagnosis and Treatment
- 3.5.3. Manufacturing and Supply Chain Operations
- 3.5.4. Marketing
- 3.5.5. Clinical Trials
3.6. AI in Drug Discovery
- 3.6.1. Identification of Pathway and Target
- 3.6.2. Identification of Hit or Lead
- 3.6.3. Lead Optimization
- 3.6.4. Synthesis of Drug-Like Compounds
3.7. Advantages of Using AI in the Drug Discovery Process
3.8. Challenges Associated with the Adoption of AI
3.9. Concluding Remarks

4. COMPETITIVE LANDSCAPE

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

5. COMPANY PROFILES: AI-BASED DRUG DISCOVERY PROVIDERS IN NORTH AMERICA

5.1. Chapter Overview
5.2. Atomwise
- 5.2.1. Company Overview
- 5.2.2. AI-based Drug Discovery Technology Portfolio
- 5.2.3. Recent Developments and Future Outlook
5.3. BioSyntagma
- 5.3.1. Company Overview
- 5.3.2. AI-based Drug Discovery Technology Portfolio
- 5.3.3. Recent Developments and Future Outlook
5.4. Collaborations Pharmaceuticals
- 5.4.1. Company Overview
- 5.4.2. AI-based Drug Discovery Technology Portfolio
- 5.4.3. Recent Developments and Future Outlook
5.5. Cyclica
- 5.5.1. Company Overview
- 5.5.2. AI-based Drug Discovery Technology Portfolio
- 5.5.3. Recent Developments and Future Outlook
5.6. InveniAI
- 5.6.1. Company Overview
- 5.6.2. AI-based Drug Discovery Technology Portfolio
- 5.6.3. Recent Developments and Future Outlook
5.7. Recursion Pharmaceuticals
- 5.7.1. Company Overview
- 5.7.2. AI-based Drug Discovery Technology Portfolio
- 5.7.3. Recent Developments and Future Outlook
5.8. Valo Health
- 5.8.1. Company Overview
- 5.8.2. AI-based Drug Discovery Technology Portfolio
- 5.8.3. Recent Developments and Future Outlook

6. COMPANY PROFILES: AI-BASED DRUG DISOCVERY SERVICE PROVIDERS IN EUROPE

6.1. Chapter Overview
6.2. Aiforia Technologies
- 6.2.1. Company Overview
- 6.2.2. AI-based Drug Discovery Technology Portfolio
- 6.2.3. Recent Developments and Future Outlook
6.3. Chemalive
- 6.3.1. Company Overview
- 6.3.2. AI-based Drug Discovery Technology Portfolio
- 6.3.3. Recent Developments and Future Outlook
6.4. DeepMatter
- 6.4.1. Company Overview
- 6.4.2. AI-based Drug Discovery Technology Portfolio
- 6.4.3. Recent Developments and Future Outlook
6.5. Exscientia
- 6.5.1. Company Overview
- 6.5.2. AI-based Drug Discovery Technology Portfolio
- 6.5.3. Recent Developments and Future Outlook
6.6. MAbSilico
- 6.6.1. Company Overview
- 6.6.2. AI-based Drug Discovery Technology Portfolio
- 6.6.3. Recent Developments and Future Outlook
6.7. Optibrium
- 6.7.1. Company Overview
- 6.7.2. AI-based Drug Discovery Technology Portfolio
- 6.7.3. Recent Developments and Future Outlook
6.8. Sensyne Health
- 6.8.1. Company Overview
- 6.8.2. AI-based Drug Discovery Technology Portfolio
- 6.8.3. Recent Developments and Future Outlook

7. COMPANY PROFILES: AI-BASED DRUG DISOCVERY SERVICE PROVIDERS IN ASIA PACIFIC

7.1. Chapter Overview
7.2. 3BIGS
- 7.2.1. Company Overview
- 7.2.2. AI-based Drug Discovery Technology Portfolio
- 7.2.3. Recent Developments and Future Outlook
7.3. Gero
- 7.3.1. Company Overview
- 7.3.2. AI-based Drug Discovery Technology Portfolio
- 7.3.3. Recent Developments and Future Outlook
7.4. Insilico Medicine
- 7.4.1. Company Overview
- 7.4.2. AI-based Drug Discovery Technology Portfolio
- 7.4.3. Recent Developments and Future Outlook
7.5. KeenEye
- 7.5.1. Company Overview
- 7.5.2. AI-based Drug Discovery Technology Portfolio
- 7.5.3. Recent Developments and Future Outlook

8. PARTNERSHIPS AND COLLABORATIONS

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

9. FUNDING AND INVESTMENT ANALYSIS

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

10. PATENT ANALYSIS

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

11. PORTER'S FIVE FORCES ANALYSIS

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

12. COMPANY VALUATION ANALYSIS

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

13. AI-BASED HEALTHCARE INITIATIVES OF TECHNOLOGY GIANTS

13.1 Chapter Overview
- 13.1.1. Amazon Web Services
- 13.1.2. Microsoft
- 13.1.3. Intel
- 13.1.4. Alibaba Cloud
- 13.1.5. Siemens
- 13.1.6. Google
- 13.1.7. IBM

14. COST SAVING ANALYSIS

14.1. Chapter Overview
14.2. Key Assumptions and Methodology
14.3. Overall Cost Saving Potential Associated with Use of AI-based Solutions in Drug
- 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

簡介目錄圖表

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)
Read More

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