Global AI-based Drug Discovery Markets 2020-2030: Focus on Deep Learning and Machine Learning

Dublin, IRELAND


Dublin, Feb. 18, 2021 (GLOBE NEWSWIRE) -- The "AI-based Drug Discovery Market: Focus on Deep Learning and Machine Learning, 2020-2030" report has been added to ResearchAndMarkets.com's offering.

The "AI-based Drug Discovery Market: Focus on Machine Learning and Deep Learning, 2020-2030" report features an extensive study of the current market landscape and future potential of the players engaged in offering AI-based services, platforms and tools for the discovery of novel drug candidates. The study presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this domain.

The drug discovery process, which includes the identification of a relevant biological target and a corresponding pharmacological lead, is crucial to the clinical success of a drug candidate.

Considering the growing complexity of modern pharmacology, the discovery of viable therapeutic candidates is very demanding, both in terms of capital investment and time. In fact, according to a study conducted by Tufts Center for the Study of Drug Development, it was estimated that a prescription drug requires around 10 years and over USD 2.5 billion in capital investment, while traversing from the bench to the market. Around one-third of the aforementioned expenditure is incurred during the drug discovery phase alone.

Moreover, it is well-known that only a small proportion of pharmacological leads identified during the discovery stages are actually translated into viable product candidates for clinical studies. Currently, experts believe that close to 90% of the product candidates fail to make it past the clinical stage of development. This high attrition rate has long been attributed to the legacy drug discovery process, which is more of a trial-and-error paradigm.

In attempts to address the concerns associated with rising capital requirements in drug discovery, and prevent late stage failure of drug development programs, stakeholders in the pharmaceutical industry are currently exploring the implementation of Artificial Intelligence (AI) based tools in order to better inform drug development operations using available chemical and biological data.

A detailed review of the current market landscape of companies that claim to offer AI-based services, platforms and tools for drug discovery. It includes information on year of establishment, company size (in terms of number of employees), location of headquarters, number of AI-based platforms/tools available, type of AI technology used, drug discovery steps for which the company has expertise involving the use of AI (target identification/validation, lead identification/optimization and ADMET studies), type of drug molecule handled (small molecules, biologics and both), drug development initiatives undertaken by the firm and target therapeutic area.

One of the key objectives of this report was to estimate the existing market size and the future growth potential within the AI-based drug discovery market. We have developed informed estimates on the financial evolution of the market, over the period 2020-2030.

Key Questions Answered

  • Who are the leading players engaged in the AI-based drug discovery market?
  • Which 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?
  • 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
1.1. Scope of the Report
1.2. Research Methodology
1.3. Key Questions Answered
1.4. Chapter Outlines

2. EXECUTIVE SUMMARY

3. INTRODUCTION
3.1. Humans, Machines and Intelligence
3.2. Artificial Intelligence
3.3. Subsets of AI
3.4. Data Science
3.5. Applications of AI in the Healthcare Industry
3.6. Steps Involved in the Drug Discovery Process
3.6.1. Pathway or Target Identification
3.6.2. Hit or Lead Identification
3.6.3. Lead Optimization
3.6.4. Synthesis of Drug-like Compounds
3.7. Advantages of Using AI in Drug Discovery
3.8. Challenges Related to the Adoption of AI in Drug Discovery Operations
3.9. Future Perspectives

4. MARKET LANDSCAPE
4.1. Chapter Overview
4.2. AI-based Drug Discovery: List of Companies
4.3. Logo Landscape: Analysis by Company Size and Drug Discovery Steps
4.4. World Map Representation: Regional Analysis by Number of Solutions
4.5. Grid Representation: Analysis by Drug Discovery Steps, Type of Drug Molecule and Geography

5. COMPANY PROFILES
5.1. Chapter Overview
5.2. 3BIGS
5.3. Atomwise
5.4. ChemAlive
5.5. Collaboration Pharmaceuticals
5.6. Cyclica
5.7. DeepMatter
5.8. Exscientia
5.9. Insilico Medicine
5.10. InveniAI
5.11. MabSilico
5.12. Optibrium
5.13. Recursion Pharmaceuticals

6. AI-BASED HEALTHCARE INITIATIVES OF TECHNOLOGY GIANTS
6.1. Chapter Overview
6.2. AI-based Healthcare Initiatives of Technology Giants
6.2.1. Amazon Web Services
6.2.2. Alibaba Cloud
6.2.3. Google
6.2.4. IBM
6.2.5. Intel
6.2.6. Microsoft
6.2.7. Siemens

7. PARTNERSHIPS AND COLLABORATIONS
7.1. Chapter Overview
7.2. Types of Partnership Models
7.3. AI-based Drug Discovery: Partnerships and Collaborations

8. FUNDING AND INVESTMENT ANALYSIS
8.1. Chapter Overview
8.2. Types of Funding
8.3. AI-based Drug Discovery: Funding and Investment Analysis

9. COMPANY VALUATION ANALYSIS
9.1. Chapter Overview
9.2. Methodology
9.3. Company Valuation Analysis: Key Parameters
9.3.1. Twitter Followers Score
9.3.2. Google Hits Score
9.3.3. Partnerships Score
9.3.4. Portfolio Strength/Uniqueness Score
9.3.5. Weighted Average Score
9.4. Company Valuation Analysis: Proprietary Scores

10. COST SAVING ANALYSIS
10.1. Chapter Overview
10.2. Key Assumptions and Methodology
10.3. Overall Cost Saving Potential of Using AI-based Solutions in Drug Discovery, 2020-2030
10.3.1. Cost Saving Potential: Analysis by Drug Discovery Steps, 2020-2030
10.3.2. Likely Cost Savings: Analysis by Geography, 2020-2030

11. MARKET FORECAST
11.1. Chapter Overview
11.2. Key Assumptions and Methodology
11.3. Global AI-based Drug Discovery Market, 2020-2030

12. CONCLUSION

13. EXECUTIVE INSIGHTS
13.1 Chapter Overview
13.2 Aigenpulse
13.3 Cloud Pharmaceuticals
13.4 DEARGEN

14. APPENDIX I: TABULATED DATA

15. APPENDIX II: LIST OF COMPANIES AND ORGANIZATIONS

For more information about this report visit https://www.researchandmarkets.com/r/23n2zs

 

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