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The drug discovery process, which includes the identification of a relevant biological target and a corresponding pharmacological lead, is crucial to the clinical success of a drug candidate. Considering the growing complexity of modern pharmacology, the discovery of viable therapeutic candidates is very demanding, both in terms of capital investment and time. In fact, according to a study conducted by Tufts Center for the Study of Drug Development, it was estimated that a prescription drug requires around 10 years and over USD 2.5 billion in capital investment, while traversing from the bench to the market. Around one-third of the aforementioned expenditure is incurred during the drug discovery phase alone. Moreover, it is well-known that only a small proportion of pharmacological leads identified during the discovery stages are actually translated into viable product candidates for clinical studies. Currently, experts believe that close to 90% of the product candidates fail to make it past the clinical stage of development. This high attrition rate has long been attributed to the legacy drug discovery process, which is more of a trial-and-error paradigm. In attempts to address the concerns associated with rising capital requirements in drug discovery, and prevent late stage failure of drug development programs, stakeholders in the pharmaceutical industry are currently exploring the implementation of Artificial Intelligence (AI) based tools in order to better inform drug development operations using available chemical and biological data.
Over time, AI-based tools have been gradually deployed across various processes, including drug discovery, within the healthcare sector. The predictive power of AI is primarily based on the processing and analysis of large volumes of clinical / medical data, which is now being leveraged to better inform modern drug discovery efforts. In this context, deep learning algorithms have demonstrated the ability to cross-reference published scientific literature (structured data) with electronic health records available in public medical data banks and clinical trial information (unstructured data), in order to generate actionable insights for target identification, hit generation and lead optimization. In other words, the use of AI-enabled technologies in drug discovery operations is likely to not only improve overall R&D productivity, but also reduce clinical failure with accurate predictions of a drug candidate’s safety and efficacy during the early stages of product development. Although the adoption of such advanced tools is still limited in mainstream drug development programs, it is worth mentioning that, in the last five years alone, close to USD 5 billion was invested into companies that are developing AI-based solution for drug discovery applications. Interestingly, ~50% of the aforementioned amount was invested in the last two years. Therefore, we are led to believe that the opportunity for stakeholders in this niche, but upcoming industry is likely to grow at a commendable pace in the foreseen future.
The “AI-based Drug Discovery Market: Focus on Machine Learning and Deep Learning, 2020-2030” report features an extensive study of the current market landscape and future potential of the players engaged in offering AI-based services, platforms and tools for the discovery of novel drug candidates. The study presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this domain. Amongst other elements, the report features:
One of the key objectives of this report was to estimate the existing market size and the future growth potential within the AI-based drug discovery market. We have developed informed estimates on the financial evolution of the market, over the period 2020-2030. The report also provides details on the likely distribution of the current and forecasted opportunity across [A] geographical regions (North America (the US and Canada), Europe (the UK, France, Germany, Spain, Italy and other European countries), Asia Pacific (China, India, Japan, Australia and South Korea)), [B] drug discovery steps (target identification, target validation, hit identification, lead identification and lead optimization), [C] therapeutic areas (oncological disorders, neurological disorders / CNS disorders, infectious diseases, immunological disorders, cardiovascular disorders, metabolic disorders and others) and [D] end users (pharmaceutical / biotechnology companies, and academic institutes). In order to account for future uncertainties and to add robustness to our forecast model, we have provided three scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry’s growth.
The opinions and insights presented in the report were also influenced by discussions held with multiple stakeholders in this domain. The report features detailed transcripts of interviews held with the following individuals (in alphabetical order):
All actual figures have been sourced and analyzed from publicly available information forums. Financial figures mentioned in this report are in USD, unless otherwise specified.
Chapter 2 is an executive summary of the key insights captured in our research. It offers a high-level view on the current state of the AI-based drug discovery market and its likely evolution in the short-mid term and long term.
Chapter 3 is an introductory chapter that presents details on the digital revolution in the healthcare industry. It elaborates on the applications of artificial intelligence and its subsets, including machine learning (supervised learning, unsupervised learning, reinforcement learning, deep learning, natural language processing). The chapter also provides an overview of the importance of data science. It further emphasizes on the applications of AI in the healthcare sector, along with detailed information on areas, including drug discovery, drug manufacturing, drug marketing, diagnosis and treatment, and clinical trials. Additionally, it features detailed information on the different steps involved in the overall drug discovery process. Further, it highlights the advantages and challenges related to the use of AI in drug discovery.
Chapter 4 provides an assessment of the current market landscape of companies that claim to offer AI-based services, platforms and tools for drug discovery. It includes information on year of establishment, company size (in terms of number of employees), location of headquarters, number of AI-based platforms / tools available, type of AI technology used, drug discovery steps for which the company has expertise involving the use of AI (target identification / validation, lead identification / optimization and ADMET studies), type of drug molecule handled (small molecules, biologics and both), drug development initiatives undertaken by the firm and target therapeutic area.It also provides information on the contemporary trends, which have been presented using three schematic representations, including [A] a logo landscape highlighting the distribution of companies based on expertise across drug discovery steps and company size (in terms of number of employees), [B] a world map representation highlighting the distribution of companies based on availability of number of AI-based platforms / tools, and [C] an insightful grid analysis presenting the distribution of companies based on the type of drug molecules handled, expertise across drug discovery steps and geographical presence.
Chapter 5 provides elaborate profiles of key players that are engaged in the AI-based drug discovery domain. Each company profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and key members of the executive team), details related to their respective portfolio of platforms / tools, recent developments and an informed future outlook.
Chapter 6 features brief details related to initiatives undertaken by technology giants in AI-based healthcare sector. The chapter includes information about companies, such as Amazon Web Services, Alibaba Cloud, Google, IBM, Intel, Microsoft and Siemens.
Chapter 7 offers detailed analysis of the partnerships that have been inked by stakeholders engaged in the AI-based drug discovery domain, during the period 2009-2020, including research agreements, research and development agreements, technology access / utilization agreements, technology integration agreements, licensing agreements, acquisitions and other relevant types of deals.
Chapter 8 contains comprehensive analysis of the investments made, including award / grant, seed financing, venture capital financing, debt financing and others, in companies that are involved in AI-based drug discovery.
Chapter 9 provides an elaborate valuation analysis of companies that are involved in the AI-based drug discovery market, based on our proprietary, multi-variable dependent valuation model to estimate the current valuation / net worth of industry players.
Chapter 10 includes an insightful analysis highlighting the likely cost saving potential associated with the use of AI in the drug discovery sector, based on information gathered from close to 15 countries, taking into consideration various parameters, such as pharmaceutical R&D expenditure, drug discovery expenditure / budget and adoption of AI across various drug discovery steps.
Chapter 11 presents a comprehensive market forecast analysis highlighting the future potential of the AI-based drug discovery market till 2030. It features the likely distribution of the market based on [A] geographical regions (North America (US and Canada), Europe (UK, France, Germany, Spain, Italy and other European countries), Asia Pacific (China, India, Japan, Australia and South Korea)), [B] drug discovery steps (target identification, target validation, hit identification, lead identification and lead optimization), [C] therapeutic areas (oncological disorders, neurological disorders / CNS disorders, infectious diseases, immunological disorders, cardiovascular disorders, metabolic disorders and others) and [D] end users (pharmaceutical / biotechnology companies and academic institutes). In order to account for future uncertainties and to add robustness to our forecast model, we have provided three scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry’s growth.
Chapter 12 is a summary of the overall report. It includes key takeaways related to research and analysis from the report in an infographic format.
Chapter 13 is a collection of interview transcripts of discussions held with key stakeholders in this industry. In this chapter, we have presented the details of our conversations held with Bo Ram Beck (Head Researcher, DEARGEN), Ed Addison (Co-founder, Chairman and Chief Executive Officer, Cloud Pharmaceuticals) and Steve Yemm (Chief Commercial Officer, Aigenpulse) and Satnam Surae (Chief Product Officer, Aigenpulse).
Chapter 14 is an appendix, which provides tabulated data and numbers for all the figures provided in the report.
Chapter 15 is an appendix, which provides the list of companies and organizations mentioned in the report.
1. PREFACE
1.1. Scope of the Report
1.2. Research Methodology
1.3. Key Questions Answered
1.4. Chapter Outlines
2. EXECUTIVE SUMMARY
3. INTRODUCTION
3.1. Humans, Machines and Intelligence
3.2. Artificial Intelligence
3.3. Subsets of AI
3.3.1. Machine Learning
3.3.1.1. Supervised Learning
3.3.1.2. Unsupervised Learning
3.3.1.3. Reinforcement Learning
3.3.1.4. Deep Learning
3.3.1.5. Natural Language Processing
3.4. Data Science
3.5. Applications of AI in the Healthcare Industry
3.5.1. Drug Discovery
3.5.2. Drug Manufacturing
3.5.3. Drug Marketing
3.5.4. Diagnosis and Treatment
3.5.5. Clinical Trials
3.6. Steps Involved in the Drug Discovery Process
3.6.1. Pathway or Target Identification
3.6.2. Hit or Lead Identification
3.6.3. Lead Optimization
3.6.4. Synthesis of Drug-like Compounds
3.7. Advantages of Using AI in Drug Discovery
3.8. Challenges Related to the Adoption of AI in Drug Discovery Operations
3.9. Future Perspectives
4. MARKET LANDSCAPE
4.1. Chapter Overview
4.2. AI-based Drug Discovery: List of Companies
4.2.1. Analysis by Year of Establishment
4.2.2. Analysis by Company Size
4.2.3. Analysis by Location of Headquarters
4.2.4. Analysis by Number of Platforms / Tools Available
4.2.5. Analysis by Type of AI Technology
4.2.6. Analysis by Drug Discovery Steps
4.2.7. Analysis by Type of Drug Molecule
4.2.8. Analysis by Drug Development Initiatives
4.2.9. Analysis by Target Therapeutic Area
4.3. Logo Landscape: Analysis by Company Size and Drug Discovery Steps
4.. World Map Representation: Regional Analysis by Number of Solutions
4.5. Grid Representation: Analysis by Drug Discovery Steps, Type of Drug Molecule and Geography
5. COMPANY PROFILES
5.1. Chapter Overview
5.2. 3BIGS
5.2.1. Company Overview
5.2.2. Product / Technology Portfolio
5.2.3. Recent Developments and Future Outlook
5.3. Atomwise
5.3.1. Company Overview
5.3.2. Product / Technology Portfolio
5.3.3. Recent Developments and Future Outlook
5.4. ChemAlive
5.4.1. Company Overview
5.4.2. Product / Technology Portfolio
5.4.3. Recent Developments and Future Outlook
5.5. Collaboration Pharmaceuticals
5.5.1. Company Overview
5.5.2. Product / Technology Portfolio
5.5.3. Recent Developments and Future Outlook
5.6. Cyclica
5.6.1. Company Overview
5.6.2. Product / Technology Portfolio
5.6.3. Recent Developments and Future Outlook
5.7. DeepMatter
5.7.1. Company Overview
5.7.2. Product / Technology Portfolio
5.7.3. Recent Developments and Future Outlook
5.8. Exscientia
5.8.1. Company Overview
5.8.2. Product / Technology Portfolio
5.8.3. Recent Developments and Future Outlook
5.9. Insilico Medicine
5.9.1. Company Overview
5.9.2. Product / Technology Portfolio
5.9.3. Recent Developments and Future Outlook
5.10. InveniAI
5.10.1. Company Overview
5.10.2. Product / Technology Portfolio
5.10.3. Recent Developments and Future Outlook
5.11. MabSilico
5.11.1. Company Overview
5.11.2. Product / Technology Portfolio
5.11.3. Recent Developments and Future Outlook
5.12. Optibrium
5.12.1. Company Overview
5.12.2. Product / Technology Portfolio
5.12.3. Recent Developments and Future Outlook
5.13. Recursion Pharmaceuticals
5.13.1. Company Overview
5.13.2. Product / Technology Portfolio
5.13.3. Recent Developments and Future Outlook
6. AI-BASED HEALTHCARE INITIATIVES OF TECHNOLOGY GIANTS
6.1. Chapter Overview
6.2. AI-based Healthcare Initiatives of Technology Giants
6.2.1. Amazon Web Services
6.2.2. Alibaba Cloud
6.2.3. Google
6.2.4. IBM
6.2.5. Intel
6.2.6. Microsoft
6.2.7. Siemens
7. PARTNERSHIPS AND COLLABORATIONS
7.1. Chapter Overview
7.2. Types of Partnership Models
7.3. AI-based Drug Discovery: Partnerships and Collaborations
7.3.1. Analysis by Year of Partnership
7.3.2. Analysis by Type of Partnership
7.3.3. Analysis by Year and Type of Partnership
7.3.4. Analysis by Type of Partner
7.3.5. Analysis by Target Therapeutic Area
7.3.6. Analysis by Type of Partner
7.3.7. Most Active Players: Analysis by Number of Partnerships
7.3.8. Regional Analysis
7.3.8.1. Intercontinental and Intracontinental Agreements
7.3.8.2. Local and International Agreements
8. FUNDING AND INVESTMENT ANALYSIS
8.1. Chapter Overview
8.2. Types of Funding
8.3. AI-based Drug Discovery: Funding and Investment Analysis
8.3.1. Analysis by Number of Funding Instances
8.3.2. Analysis by Amount Invested
8.3.3. Analysis by Type of Funding
8.3.4. Most Active Companies: Analysis by Number of Funding Instances and Amount Raised
8.3.5. Most Active Investors: Analysis by Number of Funding Instances
8.3.6. Geographical Analysis by Amount Invested
9. COMPANY VALUATION ANALYSIS
9.1. Chapter Overview
9.2. Methodology
9.3. Company Valuation Analysis: Key Parameters
9.3.1. Twitter Followers Score
9.3.2. Google Hits Score
9.3.3. Partnerships Score
9.3.4. Portfolio Strength / Uniqueness Score
9.3.5. Weighted Average Score
9.4. Company Valuation Analysis: Roots Analysis Proprietary Scores
10. COST SAVING ANALYSIS
10.1. Chapter Overview
10.2. Key Assumptions and Methodology
10.3. Overall Cost Saving Potential of Using AI-based Solutions in Drug Discovery, 2020-2030
10.3.1. Cost Saving Potential: Analysis by Drug Discovery Steps, 2020-2030
10.3.1.1. Likely Cost Savings in Target Identification / Validation, 2020-2030
10.3.1.2. Likely Cost Savings in Hit Identification, 2020-2030
10.3.1.3. Likely Cost Savings in Lead Identification / Optimization, 2020-2030
10.3.2. Likely Cost Savings: Analysis by Geography, 2020-2030
10.3.2.1. Likely Cost Savings in North America, 2020-2030
10.3.2.2. Likely Cost Savings in Europe, 2020-2030
10.3.2.3. Likely Cost Savings in Asia Pacific, 2020-2030
10.3.2.4. Likely Cost Savings in Rest of the World, 2020-2030
11. MARKET FORECAST
11.1. Chapter Overview
11.2. Key Assumptions and Methodology
11.3. Global AI-based Drug Discovery Market, 2020-2030
11.3.1. AI-based Drug Discovery Market: Analysis by Geography, 2020-2030
11.3.1.1. AI-based Drug Discovery Market in North America, 2020-2030
11.3.1.1.1. AI-based Drug Discovery Market in US, 2020-2030
11.3.1.1.2. AI-based Drug Discovery Market in Canada, 2020-2030
11.3.1.2. AI-based Drug Discovery Market in Europe, 2020-2030
11.3.1.2.1. AI-based Drug Discovery Market in UK, 2020-2030
11.3.1.2.2. AI-based Drug Discovery Market in France, 2020-2030
11.3.1.2.3. AI-based Drug Discovery Market in Germany, 2020-2030
11.3.1.2.4. AI-based Drug Discovery Market in Spain, 2020-2030
11.3.1.2.5. AI-based Drug Discovery Market in Italy, 2020-2030
11.3.1.2.6. AI-based Drug Discovery Market in Other European Countries, 2020-2030
11.3.1.3. AI-based Drug Discovery Market in Asia Pacific, 2020-2030
11.3.1.3.1. AI-based Drug Discovery Market in China, 2020-2030
11.3.1.3.2. AI-based Drug Discovery Market in India, 2020-2030
11.3.1.3.3. AI-based Drug Discovery Market in Japan, 2020-2030
11.3.1.3.4. AI-based Drug Discovery Market in Australia, 2020-2030
11.3.1.3.5. AI-based Drug Discovery Market in South Korea, 2020-2030
11.3.1.4. AI-based Drug Discovery Market in Rest of the World, 2020-2030
11.3.1.4.1. AI-based Drug Discovery Market in Saudi Arabia, 2020-2030
11.3.1.4.2. AI-based Drug Discovery Market in UAE, 2020-2030
11.3.1.4.3. AI-based Drug Discovery Market in Iran, 2020-2030
11.3.1.4.4. AI-based Drug Discovery Market in Argentina, 2020-2030
11.3.1.5. AI-based Drug Discovery Market in Other Asia Pacific and Rest of the World Regions, 2020-2030
11.3.2. AI-based Drug Discovery Market: Analysis by Drug Discovery Step, 2020-2030
11.3.2.1. AI-based Drug Discovery Market for Target Identification / Validation, 2020-2030
11.3.2.2. AI-based Drug Discovery Market for Hit Identification, 2020-2030
11.3.2.3. AI-based Drug Discovery Market for Lead Identification / Optimization, 2020-2030
11.3.3. AI-based Drug Discovery Market: Analysis by Therapeutic Area, 2020-2030
11.3.3.1. AI-based Drug Discovery Market for Oncological Disorders, 2020-2030
11.3.3.2. AI-based Drug Discovery Market for Neurological Disorders / CNS Disorders, 2020-2030
11.3.3.3. AI-based Drug Discovery Market for Infectious Diseases, 2020-2030
11.3.3.4. AI-based Drug Discovery Market for Cardiovascular Disorders, 2020-2030
11.3.3.5. AI-based Drug Discovery Market for Autoimmune Disorders, 2020-2030
11.3.3.6. AI-based Drug Discovery Market for Metabolic Disorders, 2020-2030
11.3.3.7. AI-based Drug Discovery Market for Lung Disorders, 2020-2030
11.3.3.8. AI-based Drug Discovery Market for Aging Associated Disorders, 2020-2030
11.3.3.9. AI-based Drug Discovery Market for Other Rare Disorders, 2020-2030
11.3.3.10. AI-based Drug Discovery Market for Others, 2020-2030
11.3.4. AI-based Drug Discovery Market: Analysis by End User, 2020-2030
11.3.4.1. AI-based Drug Discovery Market for Pharmaceutical / Biotechnology Companies, 2020-2030
11.3.4.2. AI-based Drug Discovery Market for CROs, 2020-2030
11.3.4.2. AI-based Drug Discovery Market for Academic Institutes / Organizations, 2020-2030
12. CONCLUSION
13. EXECUTIVE INSIGHTS
13.1 Chapter Overview
13.2 Aigenpulse
13.2.1 Company Snapshot
13.2.2 Interview Transcript: Steve Yemm (Chief Commercial Officer) and Satnam Surae (Chief Product Officer)
13.3 Cloud Pharmaceuticals
13.3.1 Company Snapshot
13.3.2 Interview Transcript: Ed Addison (Co-founder, Chairman and Chief Executive Officer)
13.4 DEARGEN
13.4.1 Company Snapshot
13.4.2 Interview Transcript: Bo Ram Beck (Head Researcher)
14. APPENDIX I: TABULATED DATA
15. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS
Figure 3.1 Historical Evolution of AI
Figure 3.2 Types of AI Technology
Figure 3.3 Interconnection between Data Science, Artificial Intelligence and Big Data
Figure 4.1 AI-based Drug Discovery: Distribution by Year of Establishment
Figure 4.2 AI-based Drug Discovery: Distribution by Company Size
Figure 4.3 AI-based Drug Discovery: Distribution by Location of Headquarters
Figure 4.4 AI-based Drug Discovery: Distribution by Number of Platforms / Tools Available
Figure 4.5 AI-based Drug Discovery: Distribution by Type of AI Technology
Figure 4.6 AI-based Drug Discovery: Distribution by Drug Discovery Steps
Figure 4.7 AI-based Drug Discovery: Distribution by Type of Drug Molecule
Figure 4.8 AI-based Drug Discovery: Distribution by Drug Development Initiatives
Figure 4.9 AI-based Drug Discovery: Distribution by Target Therapeutic Area
Figure 4.10 Logo Landscape: Distribution by Company Size and Drug Discovery Steps
Figure 4.11 World Map Representation: Regional Distribution by Number of Solutions
Figure 4.12 Grid Representation: Analysis by Drug Discovery Steps, Type of Drug Molecule and Geography
Figure 7.1 Partnerships and Collaborations: Distribution by Year of Partnership
Figure 7.2 Partnerships and Collaborations: Distribution by Type of Partnership
Figure 7.3 Partnerships and Collaborations: Distribution by Year and Type of Partnership
Figure 7.4 Partnerships and Collaborations: Distribution by Type of Partner
Figure 7.5 Partnerships and Collaborations: Distribution by Target Therapeutic Area
Figure 7.6 Most Active Players: Distribution by Number of Partnerships
Figure 7.7 Partnerships and Collaborations: Distribution by Local and International Agreements
Figure 7.8 World Map Representation: Intercontinental and Intracontinental Agreements
Figure 8.1 Funding and Investments: Distribution by Year and Type of Funding, Pre-2015 - 2020
Figure 8.2 Funding and Investments: Year-wise Trend, Pre-2015 - 2020
Figure 8.3 Funding and Investments: Quarterly Trend by Amount Invested and Number of Funding Instances, Pre-2015 - 2020
Figure 8.4 Funding and Investments: Distribution by Number of Instances and Type of Funding
Figure 8.5 Funding and Investments: Distribution by Amount Invested and Type of Funding
Figure 8.6 Most Active Players: Distribution by Number of Funding Instances and Amount Invested
Figure 8.7 Most Active Investors: Distribution by Number of Funding Instances
Figure 8.8 Funding and Investments: Geographical Distribution by Amount Invested (USD Million)
Figure 9.1 Company Valuation Analysis: Categorization by Twitter Followers Score
Figure 9.2 Company Valuation Analysis: Categorization by Google Hits Score
Figure 9.3 Company Valuation Analysis: Categorization by Partnerships Score
Figure 9.4 Company Valuation Analysis: Categorization by Portfolio Strength / Uniqueness Score
Figure 9.5 Company Valuation Analysis: Categorization by Weighted Average Score
Figure 10.1 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery, 2020-2030 (USD Million)
Figure 10.2 Likely Cost Savings: Distribution by Drug Discovery Steps, 2020-2030 (USD Million)
Figure 10.3 Likely Cost Savings Associated with the Use of AI in Target Identification, 2020-2030 (USD Million)
Figure 10.4 Likely Cost Savings Associated with the Use of AI in Target Validation, 2020-2030 (USD Million)
Figure 10.5 Likely Cost Savings Associated with the Use of AI in Hit Identification, 2020-2030 (USD Million)
Figure 10.6 Likely Cost Savings Associated with the Use of AI in Lead Identification, 2020-2030 (USD Million)
Figure 10.7 Likely Cost Savings Associated with the Use of AI in Lead Optimization, 2020-2030 (USD Million)
Figure 10.8 Likely Cost Savings: Distribution by Geography, 2020-2030 (USD Million)
Figure 10.9 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in North America, 2020-2030 (USD Million)
Figure 10.10 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Europe, 2020-2030 (USD Million)
Figure 10.11 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Asia Pacific, 2020-2030 (USD Million)
Figure 10.12 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Rest of the World, 2020-2030 (USD Million)
Figure 11.1. Global AI-based Drug Discovery Market, 2020-2030 (USD Million)
Figure 11.2. AI-based Drug Discovery Market: Distribution by Geography, 2020-2030 (USD Million)
Figure 11.3. AI-based Drug Discovery Market in North America, 2020-2030 (USD Million)
Figure 11.4. AI-based Drug Discovery Market in the US, 2020-2030 (USD Million)
Figure 11.5. AI-based Drug Discovery Market in Canada, 2020-2030 (USD Million)
Figure 11.6. AI-based Drug Discovery Market in Europe, 2020-2030 (USD Million)
Figure 11.7. AI-based Drug Discovery Market in the UK, 2020-2030 (USD Million)
Figure 11.8. AI-based Drug Discovery Market in France, 2020-2030 (USD Million)
Figure 11.9. AI-based Drug Discovery Market in Germany, 2020-2030 (USD Million)
Figure 11.10. AI-based Drug Discovery Market in Spain, 2020-2030 (USD Million)
Figure 11.11. AI-based Drug Discovery Market in Italy, 2020-2030 (USD Million)
Figure 11.12. AI-based Drug Discovery Market in Other European Countries, 2020-2030 (USD Million)
Figure 11.13. AI-based Drug Discovery Market in Asia Pacific, 2020-2030 (USD Million)
Figure 11.14. AI-based Drug Discovery Market in China, 2020-2030 (USD Million)
Figure 11.15. AI-based Drug Discovery Market in India, 2020-2030 (USD Million)
Figure 11.16. AI-based Drug Discovery Market in Japan, 2020-2030 (USD Million)
Figure 11.17. AI-based Drug Discovery Market in Australia, 2020-2030 (USD Million)
Figure 11.18. AI-based Drug Discovery Market in South Korea, 2020-2030 (USD Million)
Figure 11.19. AI-based Drug Discovery Market in Rest of the World, 2020-2030 (USD Million)
Figure 11.20. AI-based Drug Discovery Market in Saudi Arabia, 2020-2030 (USD Million)
Figure 11.21. AI-based Drug Discovery Market in UAE, 2020-2030 (USD Million)
Figure 11.22. AI-based Drug Discovery Market in Iran, 2020-2030 (USD Million)
Figure 11.23. AI-based Drug Discovery Market in Argentina, 2020-2030 (USD Million)
Figure 11.24. AI-based Drug Discovery Market in Asia Pacific and other Rest of the World Regions, 2020-2030 (USD Million)
Figure 11.25. AI-based Drug Discovery Market: Distribution by Drug Discovery Step, 2020-2030 (USD Million)
Figure 11.26. AI-based Drug Discovery Market for Target Identification, 2020-2030 (USD Million)
Figure 11.27. AI-based Drug Discovery Market for Target Validation, 2020-2030 (USD Million)
Figure 11.28. AI-based Drug Discovery Market for Hit Identification, 2020-2030 (USD Million)
Figure 11.29. AI-based Drug Discovery Market for Lead Identification, 2020-2030 (USD Million)
Figure 11.30. AI-based Drug Discovery Market for Lead Optimization, 2020-2030 (USD Million)
Figure 11.31. AI-based Drug Discovery Market: Distribution by Therapeutic Area, 2020-2030 (USD Million)
Figure 11.32. AI-based Drug Discovery Market for Oncological Disorders, 2020-2030 (USD Million)
Figure 11.33. AI-based Drug Discovery Market for Neurological Disorders / CNS Disorders, 2020-2030 (USD Million)
Figure 11.34. AI-based Drug Discovery Market for Infectious Diseases, 2020-2030 (USD Million)
Figure 11.35. AI-based Drug Discovery Market for Cardiovascular Disorders, 2020-2030 (USD Million)
Figure 11.36. AI-based Drug Discovery Market for Autoimmune Disorders, 2020-2030 (USD Million)
Figure 11.37. AI-based Drug Discovery Market for Metabolic Disorders, 2020-2030 (USD Million)
Figure 11.38. AI-based Drug Discovery Market for Lung Disorders, 2020-2030 (USD Million)
Figure 11.39. AI-based Drug Discovery Market for Aging Associated Disorders, 2020-2030 (USD Million)
Figure 11.40. AI-based Drug Discovery Market for Other Rare Disorders, 2020-2030 (USD Million)
Figure 11.41. AI-based Drug Discovery Market for Others, 2020-2030 (USD Million)
Figure 11.42. AI-based Drug Discovery Market: Distribution by End User, 2020-2030 (USD Million)
Figure 11.43. AI-based Drug Discovery Market for Pharmaceutical / Biotechnology Companies, 2020-2030 (USD Million)
Figure 11.44. AI-based Drug Discovery Market for CROs, 2020-2030 (USD Million)
Figure 11.45. AI-based Drug Discovery Market for Academic Institutes / Organizations, 2020-2030 (USD Million)
Figure 12.1. Concluding Remarks: Current Market Landscape
Figure 12.2. Concluding Remarks: Partnerships and Collaborations
Figure 12.3. Concluding Remarks: Funding and Investments
Figure 12.4. Concluding Remarks: Company Valuation Analysis
Figure 12.5. Concluding Remarks: Cost Saving Analysis
Figure 12.6. Concluding Remarks: Market Forecast
Table 4.1 AI-based Drug Discovery: List of Companies (Information on Year of Establishment, Company Size, Location of Headquarters, Number and Name of Platforms / Tools Available)
Table 4.2 AI-based Drug Discovery: List of Companies (Information on Type of AI Technology)
Table 4.3 AI-based Drug Discovery: List of Companies (Information on Drug Discovery Steps, Type of Drug Molecule, Drug Development Initiatives and Target Therapeutic Area)
Table 5.1 3BIGS: Company Overview
Table 5.2 3BIGS: AI-based Product / Technology Portfolio
Table 5.3 3BIGS: Recent Developments and Future Outlook
Table 5.4 Atomwise: Company Overview
Table 5.5 Atomwise: AI-based Product / Technology Portfolio
Table 5.6 Atomwise: Recent Developments and Future Outlook
Table 5.7 ChemAlive: Company Overview
Table 5.8 ChemAlive: AI-based Product / Technology Portfolio
Table 5.9 ChemAlive: Recent Developments and Future Outlook
Table 5.10 Collaboration Pharmaceuticals: Company Overview
Table 5.11 Collaboration Pharmaceuticals: AI-based Product / Technology Portfolio
Table 5.12 Collaboration Pharmaceuticals: Recent Developments and Future Outlook
Table 5.13 Cyclica: Company Overview
Table 5.14 Cyclica: AI-based Product / Technology Portfolio
Table 5.15 Cyclica: Recent Developments and Future Outlook
Table 5.16 DeepMatter: Company Overview
Table 5.17 DeepMatter: AI-based Product / Technology Portfolio
Table 5.18 DeepMatter: Recent Developments and Future Outlook
Table 5.19 Exscientia: Company Overview
Table 5.20 Exscientia: AI-based Product / Technology Portfolio
Table 5.21 Exscientia: Recent Developments and Future Outlook
Table 5.22 Insilico Medicine: Company Overview
Table 5.23 Insilico Medicine: AI-based Product / Technology Portfolio
Table 5.24 Insilico Medicine: Recent Developments and Future Outlook
Table 5.25 InveniAI: Company Overview
Table 5.26 InveniAI: AI-based Product / Technology Portfolio
Table 5.27 InveniAI: Recent Developments and Future Outlook
Table 5.28 MabSilico: Company Overview
Table 5.29 MabSilico: AI-based Product / Technology Portfolio
Table 5.30 MabSilico: Recent Developments and Future Outlook
Table 5.31 Optibrium: Company Overview
Table 5.32 Optibrium: AI-based Product / Technology Portfolio
Table 5.33 Optibrium: Recent Developments and Future Outlook
Table 5.34 Recursion Pharmaceuticals: Company Overview
Table 5.35 Recursion Pharmaceuticals: AI-based Product / Technology Portfolio
Table 5.36 Recursion Pharmaceuticals: Recent Developments and Future Outlook
Table 7.1 AI-based Drug Discovery: List of Partnerships and Collaborations
Table 8.1. AI-based Drug Discovery: List of Funding and Investments
Table 9.1 Company Valuation Analysis: Weighted Average Score
Table 9.2 Company Valuation Analysis: Estimated Valuation
Table 13.1 Aigenpulse: Company Snapshot
Table 13.2 Cloud Pharmaceuticals: Company Snapshot
Table 13.3 DEARGEN: Company Snapshot
Table 14.1 AI-based Drug Discovery: Distribution by Year of Establishment
Table 14.2 AI-based Drug Discovery: Distribution by Company Size
Table 14.3 AI-based Drug Discovery: Distribution by Location of Headquarters
Table 14.4 AI-based Drug Discovery: Distribution by Number of Platforms / Tools Available
Table 14.5 AI-based Drug Discovery: Distribution by Type of AI Technology
Table 14.6 AI-based Drug Discovery: Distribution by Drug Discovery Steps
Table 14.7 AI-based Drug Discovery: Distribution by Type of Drug Molecule
Table 14.8 AI-based Drug Discovery: Distribution by Drug Development Initiatives
Table 14.9 AI-based Drug Discovery: Distribution by Target Therapeutic Area
Table 14.10 Partnerships and Collaborations: Distribution by Year of Partnership
Table 14.11 Partnerships and Collaborations: Distribution by Type of Partnership
Table 14.12 Partnerships and Collaborations: Distribution by Year and Type of Partnership
Table 14.13 Partnerships and Collaborations: Distribution by Type of Partner
Table 14.14 Partnerships and Collaborations: Distribution by Target Therapeutic Area
Table 14.15 Most Active Players: Distribution by Number of Partnerships
Table 14.16 Partnerships and Collaborations: Distribution by Local and International Agreements
Table 14.17 Funding and Investments: Distribution by Year and Type of Funding, Pre-2015 - 2020
Table 14.18 Funding and Investments: Year-wise Trend, Pre-2015 - 2020
Table 14.19 Funding and Investments: Quarterly Trend by Amount Invested and Number of Funding Instances, Pre-2015 - 2020
Table 14.20 Funding and Investments: Distribution by Number of Instances and Type of Funding
Table 14.21 Funding and Investments: Distribution by Amount Invested and Type of Funding
Table 14.22 Most Active Players: Distribution by Number of Funding Instances and Amount Invested
Table 14.23 Most Active Investors: Distribution by Number of Funding Instances
Table 14.24 Company Valuation Analysis: Sample Dataset
Table 14.25 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery, 2020-2030 (USD Million)
Table 14.26 Likely Cost Savings: Distribution by Drug Discovery Steps, 2020-2030 (USD Million)
Table 14.27 Likely Cost Savings Associated with the Use of AI in Target Identification, 2020-2030 (USD Million)
Table 14.28 Likely Cost Savings Associated with the Use of AI in Target Validation, 2020-2030 (USD Million)
Table 14.29 Likely Cost Savings Associated with the Use of AI in Hit Identification, 2020-2030 (USD Million)
Table 14.30 Likely Cost Savings Associated with the Use of AI in Lead Identification, 2020-2030 (USD Million)
Table 14.31 Likely Cost Savings Associated with the Use of AI in Lead Optimization, 2020-2030 (USD Million)
Table 14.32 Likely Cost Savings: Distribution by Geography, 2020-2030 (USD Million)
Table 14.33 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in North America, 2020-2030 (USD Million)
Table 14.34 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Europe, 2020-2030 (USD Million)
Table 14.35 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Asia Pacific, 2020-2030 (USD Million)
Table 14.36 Likely Cost Savings Associated with the Use of AI-based Solutions in Drug Discovery in Rest of the World, 2020-2030 (USD Million)
Table 14.37. Global AI-based Drug Discovery Market, 2020-2030 (USD Million)
Table 14.38. AI-based Drug Discovery Market: Distribution by Geography, 2020-2030 (USD Million)
Table 14.39. AI-based Drug Discovery Market in North America, 2020-2030 (USD Million)
Table 14.40. AI-based Drug Discovery Market in the US, 2020-2030 (USD Million)
Table 14.41. AI-based Drug Discovery Market in Canada, 2020-2030 (USD Million)
Table 14.42. AI-based Drug Discovery Market in Europe, 2020-2030 (USD Million)
Table 14.43. AI-based Drug Discovery Market in the UK, 2020-2030 (USD Million)
Table 14.44. AI-based Drug Discovery Market in France, 2020-2030 (USD Million)
Table 14.45. AI-based Drug Discovery Market in Germany, 2020-2030 (USD Million)
Table 14.46. AI-based Drug Discovery Market in Spain, 2020-2030 (USD Million)
Table 14.47. AI-based Drug Discovery Market in Italy, 2020-2030 (USD Million)
Table 14.48. AI-based Drug Discovery Market in Other European Countries, 2020-2030 (USD Million)
Table 14.49. AI-based Drug Discovery Market in Asia Pacific, 2020-2030 (USD Million)
Table 14.50. AI-based Drug Discovery Market in China, 2020-2030 (USD Million)
Table 14.51. AI-based Drug Discovery Market in India, 2020-2030 (USD Million)
Table 14.52. AI-based Drug Discovery Market in Japan, 2020-2030 (USD Million)
Table 14.53. AI-based Drug Discovery Market in Australia, 2020-2030 (USD Million)
Table 14.54. AI-based Drug Discovery Market in South Korea, 2020-2030 (USD Million)
Table 14.55. AI-based Drug Discovery Market in Rest of the World, 2020-2030 (USD Million)
Table 14.56. AI-based Drug Discovery Market in Saudi Arabia, 2020-2030 (USD Million)
Table 14.57. AI-based Drug Discovery Market in UAE, 2020-2030 (USD Million)
Table 14.58. AI-based Drug Discovery Market in Iran, 2020-2030 (USD Million)
Table 14.59. AI-based Drug Discovery Market in Argentina, 2020-2030 (USD Million)
Table 14.60. AI-based Drug Discovery Market in Asia Pacific and other Rest of the World Regions, 2020-2030 (USD Million)
Table 14.61. AI-based Drug Discovery Market: Distribution by Drug Discovery Step, 2020-2030 (USD Million)
Table 14.62. AI-based Drug Discovery Market for Target Identification, 2020-2030 (USD Million)
Table 14.63. AI-based Drug Discovery Market for Target Validation, 2020-2030 (USD Million)
Table 14.64. AI-based Drug Discovery Market for Hit Identification, 2020-2030 (USD Million)
Table 14.65. AI-based Drug Discovery Market for Lead Identification, 2020-2030 (USD Million)
Table 14.66. AI-based Drug Discovery Market for Lead Optimization, 2020-2030 (USD Million)
Table 14.67. AI-based Drug Discovery Market: Distribution by Therapeutic Area, 2020-2030 (USD Million)
Table 14.68. AI-based Drug Discovery Market for Oncological Disorders, 2020-2030 (USD Million)
Table 14.69. AI-based Drug Discovery Market for Neurological Disorders / CNS Disorders, 2020-2030 (USD Million)
Table 14.70. AI-based Drug Discovery Market for Infectious Diseases, 2020-2030 (USD Million)
Table 14.71. AI-based Drug Discovery Market for Cardiovascular Disorders, 2020-2030 (USD Million)
Table 14.72. AI-based Drug Discovery Market for Autoimmune Disorders, 2020-2030 (USD Million)
Table 14.73. AI-based Drug Discovery Market for Metabolic Disorders, 2020-2030 (USD Million)
Table 14.74. AI-based Drug Discovery Market for Lung Disorders, 2020-2030 (USD Million)
Table 14.75. AI-based Drug Discovery Market for Aging Associated Disorders, 2020-2030 (USD Million)
Table 14.76. AI-based Drug Discovery Market for Other Rare Disorders, 2020-2030 (USD Million)
Table 14.77. AI-based Drug Discovery Market for Others, 2020-2030 (USD Million)
Table 14.78. AI-based Drug Discovery Market: Distribution by End User, 2020-2030 (USD Million)
Table 14.79. AI-based Drug Discovery Market for Pharmaceutical / Biotechnology Companies, 2020-2030 (USD Million)
Table 14.80. AI-based Drug Discovery Market for CROs, 2020-2030 (USD Million)
Table 14.81. AI-based Drug Discovery Market for Academic Institutes / Organizations, 2020-2030 (USD Million)
The following companies and organizations have been mentioned in the report.