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Report Description
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.
The ‘AI-based Drug Discovery Market (2nd Edition): Distribution by Drug Discovery Steps (Target Identification / Validation, Hit Generation / Lead Identification and Lead Optimization), Therapeutic Area (Oncological Disorders, CNS Disorders, Infectious Diseases, Respiratory Disorders, Cardiovascular Disorders, Endocrine Disorders, Gastrointestinal Disorders, Musculoskeletal Disorders, Immunological Disorders, Dermatological Disorders and Others) and Key Geographies (North America, Europe, Asia-Pacific, Latin America, MENA and Rest of the World): Industry Trends and Global Forecasts, 2022-2035’ report features an extensive study of the current landscape and future potential of the players engaged in offering AI-based services, platforms and tools for the discovery of novel drug candidates. The study features an in-depth analysis, highlighting the capabilities of AI-based drug discovery service / technology providers. Amongst other elements, the report features:
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:
Contents
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 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
10.6. Leading Patents: Analysis by Number of Citations
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
12.4. 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 Discovery, 2022-2035
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
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 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 Trend Distribution of Funding Instances, 2006-2022
Figure 9.2 Funding and Investment Analysis: Cumulative 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 14.1 Overall Cost Saving Potential Associated with Use of AI-based Solutions in Drug Discovery, 2022-2035 (USD Billion)
Figure 14.2 Likely Cost Savings: Distribution by Drug Discovery Steps, 2022 and 2035 (USD Billion)
Figure 14.3 Likely Cost Savings During Target Identification / Validation, 2022-2035 (USD Billion)
Figure 14.4 Likely Cost Savings During Hit Generation / Lead Identification, 2022-2035 (USD Billion)
Figure 14.5 Likely Cost Savings During Lead Optimization, 20222035 (USD Billion)
Figure 14.6 Likely Cost Savings: Distribution by Target Therapeutic Area, 2022 and 2035 (USD Billion)
Figure 14.7 Likely Cost Savings for Drugs Targeting Oncological Disorders, 2022-2035 (USD Billion)
Figure 14.8 Likely Cost Savings for Drugs Targeting Neurological Disorders, 2022-2035 (USD Billion)
Figure 14.9 Likely Cost Savings for Drugs Targeting Infectious Diseases, 2022-2035 (USD Billion)
Figure 14.10 Likely Cost Savings for Drugs Targeting Respiratory Disorders, 2022-2035 (USD Billion)
Figure 14.11 Likely Cost Savings for Drugs Targeting Cardiovascular Disorders, 2022-2035 (USD Billion)
Figure 14.12 Likely Cost Savings for Drugs Targeting Endocrine Disorders, 2022-2035 (USD Billion)
Figure 14.13 Likely Cost Savings for Drugs Targeting Gastrointestinal Disorders, 2022-2035 (USD Billion)
Figure 14.14 Likely Cost Savings for Drugs Targeting Musculoskeletal Disorders, 2022-2035 (USD Billion)
Figure 14.15 Likely Cost Savings for Drugs Targeting Immunological Disorders, 2022-2035 (USD Billion)
Figure 14.16 Likely Cost Savings for Drugs Targeting Dermatological Disorders, 2022-2035 (USD Billion)
Figure 14.17 Likely Cost Savings for Drugs Targeting Other Disorders, 2022-2035 (USD Billion)
Figure 14.18 Likely Cost Savings: Distribution by Geography, 2022 and2035 (USD Billion)
Figure 14.19 Likely Cost Savings in North America, 2022-2035 (USD Billion)
Figure 14.20 Likely Cost Savings in Europe, 2022-2035 (USD Billion)
Figure 14.21 Likely Cost Savings in Asia Pacific, 2022-2035 (USD Billion)
Figure 14.22 Likely Cost Savings in MENA, 2022-2035 (USD Billion)
Figure 14.23 Likely Cost Savings in Latin America, 2022-2035 (USD Billion)
Figure 14.24 Likely Cost Savings in Rest of the World, 2022-2035 (USD Billion)
Figure 15.1 Global AI-based Drug Discovery Market, 2022-2035 (USD Billion)
Figure 15.2 AI-based Drug Discovery Market: Distribution by Drug Discovery Steps, 2022 and 2035 (USD Billion)
Figure 15.3 AI-based Drug Discovery Market for Target Identification / Validation, 2022-2035 (USD Billion)
Figure 15.4 AI-based Drug Discovery Market for Hit Generation / Lead Identification, 2022-2035 (USD Billion)
Figure 15.5 AI-based Drug Discovery Market for Lead Optimization, 2022-2035 (USD Billion)
Figure 15.6 AI-based Drug Discovery Market: Distribution by Target Therapeutic Area, 2022 and 2035 (USD Billion)
Figure 15.7 AI-based Drug Discovery Market for Oncological Disorders, 2022-2035 (USD Billion)
Figure 15.8 AI-based Drug Discovery Market for Neurological Disorders 2022-2035 (USD Billion)
Figure 15.9 AI-based Drug Discovery Market for Infectious Diseases, 2022-2035 (USD Billion)
Figure 15.10 AI-based Drug Discovery Market for Respiratory Disorders, 2022-2035 (USD Billion)
Figure 15.11 AI-based Drug Discovery Market for Cardiovascular Disorders, 2022-2035 (USD Billion)
Figure 15.12 AI-based Drug Discovery Market for Endocrine Disorders, 2022-2035 (USD Billion)
Figure 15.13 AI-based Drug Discovery Market for Gastrointestinal Disorders, 2022-2035 (USD Billion)
Figure 15.14 AI-based Drug Discovery Market for Musculoskeletal Disorders, 2022-2035 (USD Billion)
Figure 15.15 AI-based Drug Discovery Market for Immunological Disorders, 2022-2035 (USD Billion)
Figure 15.16 AI-based Drug Discovery Market for Dermatological Disorders, 2022-2035 (USD Billion)
Figure 15.17 AI-based Drug Discovery Market for Other Disorders, 2022-2035 (USD Billion)
Figure 15.18 AI-based Drug Discovery Market: Distribution by Geography, 2022-2035 (USD Billion)
Figure 15.19 AI-based Drug Discovery Market in North America, 2022-2035 (USD Billion)
Figure 15.20 AI-based Drug Discovery Market in the US, 2022-2035 (USD Billion)
Figure 15.21 AI-based Drug Discovery Market in Canada, 2022-2035 (USD Billion)
Figure 15.22 AI-based Drug Discovery Market in Europe, 2022-2035 (USD Billion)
Figure 15.23 AI-based Drug Discovery Market in UK, 2022-2035 (USD Billion)
Figure 15.24 AI-based Drug Discovery Market in France, 2022-2035 (USD Billion)
Figure 15.25 AI-based Drug Discovery Market in Germany, 2022-2035 (USD Billion)
Figure 15.26 AI-based Drug Discovery Market in Spain, 2022-2035 (USD Billion)
Figure 15.27 AI-based Drug Discovery Market in Italy, 2022-2035 (USD Billion)
Figure 15.28 AI-based Drug Discovery Market in Rest of Europe, 2022 - 2035 (USD Billion)
Figure 15.29 AI-based Drug Discovery Market in Asia Pacific, 2022-2035 (USD Billion)
Figure 15.30 AI-based Drug Discovery Market in China, 2022-2035 (USD Billion)
Figure 15.31 AI-based Drug Discovery Market in India, 2022-2035 (USD Billion)
Figure 15.32 AI-based Drug Discovery Market in Japan, 2022-2035 (USD Billion)
Figure 15.33 AI-based Drug Discovery Market in Australia, 2022-2035 (USD Billion)
Figure 15.34 AI-based Drug Discovery Market in South Korea, 2022-2035 (USD Billion)
Figure 15.35 AI-based Drug Discovery Market in MENA, 2022-2035 (USD Billion)
Figure 15.36 AI-based Drug Discovery Market in Saudi Arabia, 2022-2035 (USD Billion)
Figure 15.37 AI-based Drug Discovery Market in UAE, 2022-2035 (USD Billion)
Figure 15.38 AI-based Drug Discovery Market in Iran, 2022-2035 (USD Billion)
Figure 15.39 AI-based Drug Discovery Market in Latin America, 2022-2035 (USD Billion)
Figure 15.40 AI-based Drug Discovery Market in Argentina, 2022-2035 (USD Billion)
Figure 15.41 AI-based Drug Discovery Market in Rest of the World, 2022-2035 (USD Billion)
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
Table 4.1 AI-based Drug Discovery Services / 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 Services / Technology Providers: Information on Type of AI Technology and Drug Discovery Steps
Table 4.3 AI-based Drug Discovery Services / Technology Providers: Information on Type of Drug Molecule and Target Therapeutic Area
Table 5.1 Leading AI-based Drug Discovery Services / 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: List of Partnerships and Collaborations, 2009-2022
Table 9.1 AI-based Drug Discovery: 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 12.1. Company Valuation Analysis: Scoring Sheet
Table 12.2. 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 Most Active Players: Distribution by Number of Platforms / Tools Offered
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 Agreement
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, Pre 2015-2022
Table 18.26 Funding and Investment Analysis: Cumulative Year-wise Distribution of Amount Invested Pre-2015-2022, (USD Million),
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 Type of Investor (USD Million)
Table 18.33 Most Active Players: Distribution by Number of Funding Instances
Table 18.34 Most Active Players: Distribution by Amount Raised
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 Application Year
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: Cumulative Year-wise Distribution by Type of Applicant
Table 18.43 Leading Industry Players: Distribution by Number of Patents
Table 18.44 Leading Non-Industry Players: Distribution by Number of Patents
Table 18.45 Leading Patent Assignees: Distribution by Number of Patents
Table 18.46 Patent Analysis: Distribution by Age
Table 18.47 Patent Analysis: Patent Valuation
Table 18.48 Overall Cost Saving Potential Associated with Use of AI-based Solutions in Drug Discovery, 2022-2035 (USD Billion)
Table 18.49 Likely Cost Savings: Distribution by Drug Discovery Steps, 2022-2035 (USD Billion)
Table 18.50 Likely Cost Savings During Target Identification / Validation, 2022-2035 (USD Billion)
Table 18.51 Likely Cost Savings During Hit Generation / Lead Identification, 2022-2035 (USD Billion)
Table 18.52 Likely Cost Savings During Lead Optimization, 2022-2035 (USD Billion)
Table 18.53 Likely Cost Savings: Distribution by Target Therapeutic Area, 2022-2035 (USD Billion)
Table 18.54 Likely Cost Savings for Drugs Targeting Oncological Disorders, 2022-2035 (USD Billion)
Table 18.55 Likely Cost Savings for Drugs Targeting Neurological Disorders, 2022-2035 (USD Billion)
Table 18.56 Likely Cost Savings for Drugs Targeting Infectious Diseases, 2022-2035 (USD Billion)
Table 18.57 Likely Cost Savings for Drugs Targeting Respiratory Disorders, 2022-2035 (USD Billion)
Table 18.58 Likely Cost Savings for Drugs Targeting Cardiovascular Disorders, 2022-2035 (USD Billion)
Table 18.59 Likely Cost Savings for Drugs Targeting Endocrine Disorders, 2022-2035 (USD Billion)
Table 18.60 Likely Cost Savings for Drugs Targeting Gastrointestinal Disorders, 2022-2035 (USD Billion)
Table 18.61 Likely Cost Savings for Drugs Targeting Musculoskeletal Disorders, 2022-2035 (USD Billion)
Table 18.62 Likely Cost Savings for Drugs Targeting Immunological Disorders, 2022-2035 (USD Billion)
Table 18.63 Likely Cost Savings for Drugs Targeting Dermatological Disorders, 2022-2035 (USD Billion)
Table 18.64 Likely Cost Savings for Drugs Targeting Other Disorders, 2022-2035 (USD Billion)
Table 18.65 Likely Cost Savings: Distribution by Geography, 2022 and 2035 (USD Billion)
Table 18.66 Likely Cost Savings in North America, 2022-2035 (USD Billion)
Table 18.67 Likely Cost Savings in Europe, 2022-2035 (USD Billion)
Table 18.68 Likely Cost Savings in Asia Pacific, 2022-2035 (USD Billion)
Table 18.69 Likely Cost Savings in MENA, 2022-2035 (USD Billion)
Table 18.70 Likely Cost Savings in Latin America, 2022-2035 (USD Billion)
Table 18.71 Likely Cost Savings in Rest of the World, 2022-2035 (USD Billion)
Table 18.72 Global AI-based Drug Discovery Market, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.73 AI-based Drug Discovery Market: Distribution by Drug Discovery Steps, 2022 and 2035 (USD Billion)
Table 18.74 AI-based Drug Discovery Market for Target Identification / Validation, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.75 AI-based Drug Discovery Market for Hit Generation / Lead Identification, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.76 AI-based Drug Discovery Market for Lead Optimization, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.77 AI-based Drug Discovery Market: Distribution by Target Therapeutic Area, 2022 and 2035 (USD Billion)
Table 18.78 AI-based Drug Discovery Market for Oncological Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.79 AI-based Drug Discovery Market for Neurological Disorders Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.80 AI-based Drug Discovery Market for Infectious Diseases, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.81 AI-based Drug Discovery Market for Respiratory Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.82 AI-based Drug Discovery Market for Cardiovascular Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.83 AI-based Drug Discovery Market for Endocrine Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.84 AI-based Drug Discovery Market for Gastrointestinal Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.85 AI-based Drug Discovery Market for Musculoskeletal Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.86 AI-based Drug Discovery Market for Immunological Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.87 AI-based Drug Discovery Market for Dermatological Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.88 AI-based Drug Discovery Market for Other Disorders, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.89 AI-based Drug Discovery Market: Distribution by Geography, 2022-2035 (USD Billion)
Table 18.90 AI-based Drug Discovery Market in North America, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.91 AI-based Drug Discovery Market in the US, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.92 AI-based Drug Discovery Market in Canada, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.93 AI-based Drug Discovery Market in Europe, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.94 AI-based Drug Discovery Market in UK, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.94 AI-based Drug Discovery Market in France, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.95 AI-based Drug Discovery Market in Germany, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.96 AI-based Drug Discovery Market in Spain, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.97 AI-based Drug Discovery Market in Italy, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.98 AI-based Drug Discovery Market in Rest of Europe, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.99 AI-based Drug Discovery Market in Asia Pacific, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.100 AI-based Drug Discovery Market in China, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.101 AI-based Drug Discovery Market in India, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.102 AI-based Drug Discovery Market in Japan, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.103 AI-based Drug Discovery Market in Australia, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.104 AI-based Drug Discovery Market in South Korea, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.105 AI-based Drug Discovery Market in MENA, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.106 AI-based Drug Discovery Market in Saudi Arabia, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.107 AI-based Drug Discovery Market in UAE, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.108 AI-based Drug Discovery Market in Iran, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.109 AI-based Drug Discovery Market in Latin America, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.110 AI-based Drug Discovery Market in Argentina, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
Table 18.111 AI-based Drug Discovery Market in Rest of the World, Conservative, Base and Optimistic Scenarios, 2022-2035 (USD Billion)
The following companies / institutes / government bodies and organizations have been mentioned in this report.