Dublin, May 12, 2022 (GLOBE NEWSWIRE) -- The "AI-based Drug Discovery Market: Distribution by Drug Discovery Steps, Therapeutic Area and Key Geographies: Industry Trends and Global Forecasts, 2022-2035" report has been added to ResearchAndMarkets.com's offering.
This 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.
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.
Amongst other elements, the report features:
- A detailed overview of the overall landscape of companies offering AI-based services, platforms and tools for drug discovery, along with information on several relevant parameters, such as their year of establishment, company size (in terms of employee count), location of headquarters (North America, Europe, Asia-Pacific and Rest of the World) and type of company (service providers, technology providers and in-house players). The chapter also covers details related to the type of AI technology (artificial intelligence (undefined), deep learning, machine learning (undefined), natural language processing, data science, reinforcement learning, supervised learning and unsupervised learning), drug discovery steps (target discovery / identification / validation, lead identification / optimization / generation and ADME / toxicity testing), type of drug molecule (small molecules, biologics and both) and target therapeutic area (oncological disorders, neurological disorders, infectious diseases, immunological disorders, cardiovascular disorders, rare diseases, metabolic disorders, respiratory disorders, gastrointestinal disorders, musculoskeletal disorders, dermatological disorders, hematological disorders, ophthalmic disorders and other disorders).
- Elaborate profiles of prominent players (shortlisted based on a proprietary criterion) engaged in AI-based drug discovery domain, across North America, Europe and Asia-Pacific. Each profile provides an overview of the company, featuring information on the year of establishment, number of employees, location of their headquarters, key executives, details related to its AI-based drug discovery technology portfolio, recent developments and an informed future outlook.
- An analysis of partnerships inked between stakeholders engaged in this domain, during the period 2009-2022, covering research and development agreements, technology access / utilization agreements, acquisitions, technology licensing agreements, joint ventures / mergers, technology integration agreements, service agreements and other related agreements. Further, the partnership activity in this domain has been analyzed based on various parameters, such as year of partnership, type of partnership, target therapeutic area, focus area, type of partner company and most active players (in terms of number of partnerships). It also highlights the regional distribution of the partnership activity witnessed in this market.
- A detailed analysis of various investments, such as grants, awards, seed financing, venture capital financing, debt financing, capital raised from IPOs and subsequent offerings, that were undertaken by players engaged in this domain, during the period 2006-2022.
- An in-depth analysis of the various patents that have been filed / granted related to AI-based drug discovery technologies, from 2019 to February 2022, taking into consideration parameters, such as application year, geographical region, CPC symbols, emerging focus areas, type of applicant and leading players (in terms of size of intellectual property portfolio). It also includes a patent benchmarking analysis and a detailed valuation analysis.
- A qualitative analysis, highlighting the five competitive forces prevalent in this domain, including threats for new entrants, bargaining power of drug developers, bargaining power of AI-based drug discovery companies, threats of substitute technologies and rivalry among existing competitors.
- An elaborate valuation analysis of companies that are involved in the AI-based drug discovery market, based on our proprietary, multi-variable dependent valuation model to estimate the current valuation / net worth of industry players.
- An insightful analysis highlighting the likely cost saving potential associated with the use of AI in the drug discovery sector, based on information gathered from close to 15 countries, taking into consideration various parameters, such as pharmaceutical R&D expenditure, drug discovery expenditure / budget and adoption of AI across various drug discovery steps.
Key Questions Answered
- Who are the leading players engaged in the AI-based drug discovery market?
- Which of the key AI technologies are presently being most commonly adopted by drug discovery focused companies?
- What is the likely valuation / net worth of companies engaged in this domain?
- What is the likely cost saving potential associated with the use of AI in the drug discovery process?
- How is the intellectual property landscape for AI-based drug discovery technologies likely to evolve in the foreseen future?
- Which partnership models are most commonly adopted by stakeholders engaged in this industry?
- What is the overall trend of funding and investments within this domain?
- How is the current and future opportunity likely to be distributed across key market segments?
Key Topics Covered:
1. PREFACE
2. EXECUTIVE SUMMARY
3. INTRODUCTION
4. COMPETITIVE LANDSCAPE
4.1. Chapter Overview
4.2. AI-based Drug Discovery: Overall Market Landscape
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
9. FUNDING AND INVESTMENT ANALYSIS
9.1. Chapter Overview
9.2. Types of Funding
9.3. AI-based Drug Discovery: Funding and Investments
10. PATENT ANALYSIS
10.1. Chapter Overview
10.2. Scope and Methodology
10.3. AI-based Drug Discovery: Patent Analysis
10.4. AI-based Drug Discovery: Patent Benchmarking
10.5. AI-based Drug Discovery: Patent Valuation
11. PORTER'S FIVE FORCES ANALYSIS
11.1. Chapter Overview
11.2. Methodology and Assumptions
11.3. Key Parameters
11.4. 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: Publisher Proprietary Scores
13. AI-BASED HEALTHCARE INITIATIVES OF TECHNOLOGY GIANTS
14. COST SAVING ANALYSIS
15. MARKET FORECAST
16. CONCLUSION
17. EXECUTIVE INSIGHTS
18. APPENDIX I: TABULATED DATA
19. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS
For more information about this report visit https://www.researchandmarkets.com/r/oatw30
