AI in 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
The AI in drug discovery market is expected to reach USD 0.74 billion by 2022 anticipated to grow at a CAGR of 25% during the forecast period 2022-2035.
The journey of discovering and developing new therapeutic options faces significant hurdles, mainly due to a trial-and-error process resulting in a small fraction of leads becoming viable for clinical studies. It's estimated that roughly 90% of these prospects don't advance, leading to considerable financial strain. Bringing a new prescription drug to market typically spans 10 to 15 years and costs between $1 to $2 billion, with a substantial portion allocated to the discovery phase alone. To tackle these challenges, the pharmaceutical sector is turning to Artificial Intelligence (AI) tools to revolutionize drug discovery and development. AI, especially deep learning algorithms, can analyze extensive clinical and biological data to guide modern drug discovery. These tools sift through scientific literature, electronic health records, and clinical trial data to offer insights for target identification, hit generation, and lead optimization.
At present, AI-powered tools such as deep learning, supervised and unsupervised learning, natural language processing, and machine learning are extensively utilized in healthcare for drug discovery. The objective is to enhance R&D efficiency and decrease clinical setbacks by forecasting safety and efficacy in early developmental phases. Around 210 AI drug discovery companies provide related services, with over $10 billion invested in this sector over the last five years. Notably, half of this investment occurred in the last two years, signaling a growing interest. Additionally, there have been approximately 440 partnerships between industry and academic entities to advance AI-based solutions for drug discovery. The robust initiatives in this domain indicate potential market expansion in the forecasted period for stakeholders engaged in this emerging industry.
Report Coverage
The journey of discovering and developing new therapeutic options faces significant hurdles, mainly due to a trial-and-error process resulting in a small fraction of leads becoming viable for clinical studies. It's estimated that roughly 90% of these prospects don't advance, leading to considerable financial strain. Bringing a new prescription drug to market typically spans 10 to 15 years and costs between $1 to $2 billion, with a substantial portion allocated to the discovery phase alone. To tackle these challenges, the pharmaceutical sector is turning to Artificial Intelligence (AI) tools to revolutionize drug discovery and development. AI, especially deep learning algorithms, can analyze extensive clinical and biological data to guide modern drug discovery. These tools sift through scientific literature, electronic health records, and clinical trial data to offer insights for target identification, hit generation, and lead optimization.
At present, AI-powered tools such as deep learning, supervised and unsupervised learning, natural language processing, and machine learning are extensively utilized in healthcare for drug discovery. The objective is to enhance R&D efficiency and decrease clinical setbacks by forecasting safety and efficacy in early developmental phases. Around 210 AI drug discovery companies provide related services, with over $10 billion invested in this sector over the last five years. Notably, half of this investment occurred in the last two years, signaling a growing interest. Additionally, there have been approximately 440 partnerships between industry and academic entities to advance AI-based solutions for drug discovery. The robust initiatives in this domain indicate potential market expansion in the forecasted period for stakeholders engaged in this emerging industry.
Report Coverage
- The report examines the AI in drug discovery market, focusing on drug discovery steps, therapeutic area and key geographies.
- It analyzes factors impacting market growth, such as drivers, constraints, opportunities, and challenges.
- Evaluation of potential benefits and hurdles within the market, providing insights into the competitive landscape for major industry players.
- Revenue forecasts for market segments across six major regions.
- Comprehensive analysis covering AI-centric companies specializing in drug discovery services, platforms, and tools. Parameters include company details like establishment year, employee count, headquarters location (North America, Europe, Asia-Pacific, Rest of the World), and categorization (service providers, technology providers, in-house players). Additionally, it encompasses AI technology types, drug discovery phases, types of drug molecules, and targeted therapeutic areas.
- Detailed profiles of leading AI drug discovery companies in North America, Europe, and Asia-Pacific. Profiles encompass establishment year, employee count, headquarters location, key executives, AI-based drug discovery technology portfolio, recent developments, and future prospects.
- Examination of partnerships between stakeholders involved in AI-driven drug discovery from 2009-2022, covering various agreement types (research and development, technology access/utilization, acquisitions, licensing, joint ventures, mergers, service agreements) and analyzing partnership trends based on various parameters.
- In-depth analysis of investments (grants, awards, financing rounds, IPOs, subsequent offerings) made in AI drug discovery companies from 2006-2022.
- Evaluation of patents filed/granted from 2019 to February 2022, considering application year, geographical region, CPC symbols, emerging focus areas, applicant types, and leading players regarding intellectual property portfolios.
- Qualitative assessment of competitive forces in the AI in drug discovery market, including threats for new entrants, bargaining power of drug developers, AI-based drug discovery companies, substitute technologies, and rivalry among existing competitors.
- Detailed valuation analysis using a proprietary, multi-variable dependent valuation model to estimate the current net worth of AI drug discovery industry players.
- Insightful analysis estimating potential cost savings associated with AI adoption in drug discovery across approximately 15 countries, considering pharmaceutical R&D expenditure, drug discovery budgets, and AI adoption across various discovery steps.
- Atomwise
- BioSyntagma
- Collaborations Pharmaceuticals
- Cyclica
- InveniAI
- Recursion Pharmaceuticals
- Valo Health
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
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
