As per the report, the AI in pharmaceutical market size was US$ 701.6 million in 2020. It is projected to grow US$ 9,241.34 million by 2030 at a CAGR of 29.4% in the forecast period.
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Market Overview
A recent study by Precedence Research on the AI in pharmaceutical market offers a forecast for 2021 and 2030. The study analyzes crucial trends that are currently determining the growth of the AI in pharmaceutical market. This report explicates on vital dynamics such as the drivers, restraints, and opportunities for key market players, along with key stakeholders as well as emerging players associated with the manufacturing of AI in pharmaceutical. The study also provides the dynamics that are responsible for influencing the future status of this market over the forecast period.
A detailed assessment of the AI in pharmaceutical market value chain analysis, business execution, and supply chain analysis across regional markets has been covered in the report. A list of prominent companies operating in the AI in pharmaceutical market along with their product portfolio enhances the reliability of this comprehensive research study.
Highlights of the Report:
- Market Penetration: Comprehensive information on the product portfolios of the top players in the market.
- Product Development/Innovation: Detailed insights on the upcoming technologies, R&D activities, and product launches in the market
- Competitive Assessment: In-depth assessment of the market strategies, geographic and business segments of the leading players in the market
- Market Development: Comprehensive information about emerging markets. This report analyzes the market for various segments across geographies
- Market Diversification: Exhaustive information about new products, untapped geographies, recent developments, and investments in the market
Key Points
- Primary Research
- Secondary Research
- Market Size Estimation
- Market Dynamics
- Drivers
- Restraints
- Opportunities
- Challenges
- Macroeconomic Indicators
- Technology Trends and Assessment
- Market Factor Analysis
- Porter’s Five Forces Analysis
- Bargaining Power Of Suppliers
- Bargaining Power Of Buyers
- Threat Of New Entrants
- Threat Of Substitutes
- Intensity Of Rivalry
- Value Chain Analysis
- Investment Feasibility Analysis
- Pricing Analysis
Research Methodology
Secondary Research
It involves company databases such as Hoover's: This assists us recognize financial information, the structure of the market participants and industry's competitive landscape.
The secondary research sources referred in the process are as follows:
- Governmental bodies, and organizations creating economic policies
- National and international social welfare institutions
- Company websites, financial reports and SEC filings, broker and investor reports
- Related patent and regulatory databases
- Statistical databases and market reports
- Corporate Presentations, news, press release, and specification sheet of Manufacturers
Primary Research
Primary research includes face-to-face interviews, online surveys, and telephonic interviews.
- Means of primary research: Email interactions, telephonic discussions and Questionnaire-based research etc.
- In order to validate our research findings and analysis we conduct primary interviews of key industry participants. Insights from primary respondents help in validating the secondary research findings. It also develops Research Team’s expertise and market understanding.
Industry participants involved in this research study include:
- CEOs, VPs, market intelligence managers
- Procuring and national sales managers technical personnel, distributors and resellers
- Research analysts and key opinion leaders from various domains
Competition Landscape
In the final section of the report on the global AI in pharmaceutical market, a 'dashboard view' of the companies has been provided to compare the current market scenario and the contribution of companies to the global AI in pharmaceutical market. Moreover, the report is primarily designed to provide clients with an objective and detailed comparative assessment of key providers specific to segments of the global market. Report audiences can gain segment-specific manufacturer insights to identify and evaluate key competitors based on an in-depth assessment of their capabilities and success in the AI in pharmaceutical marketplace.
Some of the prominent players in the global AI in pharmaceutical market include:
- IBM Corporation
- Exscientia
- Deep Genomics
- Cloud Pharmaceuticals, Inc.
- Microsoft Corporation
- NVIDIA Corporation
- Insilico Medicine
- Alphabet Inc.
- Atomwise, Inc.
- Biosymetrics
- Euretos
- BenevolentAI
Market Segmentation
Segments of the global AI in pharmaceutical market have been analyzed in terms of their market share to understand an individual segment’s relative contribution to market growth. This detailed level of information is important for identifying key trends in the global AI in pharmaceutical market.
Market Segments as below:
By Technology
- Natural Language Processing
- Context-Aware Processing
- Deep Learning
- Querying Method
By Drug Type
- Large Molecules
- Small Molecules
By Application
- Drug Discovery
- Clinical Trial
- Research & Development
- Others
By Geography
- North America
- U.S.
- Canada
- Europe
- U.K.
- Germany
- France
- Asia Pacific
- China
- India
- Japan
- South Korea
- Rest of the World
Table of Content
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on AI in Pharmaceutical Market
5.1. COVID-19 Landscape: AI in Pharmaceutical Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global AI in Pharmaceutical Market, By Technology
8.1. AI in Pharmaceutical Market, by Technology Type, 2021-2030
8.1.1. Natural Language Processing
8.1.1.1. Market Revenue and Forecast (2019-2030)
8.1.2. Context-Aware Processing
8.1.2.1. Market Revenue and Forecast (2019-2030)
8.1.3. Deep Learning
8.1.3.1. Market Revenue and Forecast (2019-2030)
8.1.4. Querying Method
8.1.4.1. Market Revenue and Forecast (2019-2030)
Chapter 9. Global AI in Pharmaceutical Market, By Application
9.1. AI in Pharmaceutical Market, by Application, 2021-2030
9.1.1. Drug Discovery
9.1.1.1. Market Revenue and Forecast (2019-2030)
9.1.2. Clinical Trial
9.1.2.1. Market Revenue and Forecast (2019-2030)
9.1.3. Research & Development
9.1.3.1. Market Revenue and Forecast (2019-2030)
9.1.4. Others
9.1.4.1. Market Revenue and Forecast (2019-2030)
Chapter 10. Global AI in Pharmaceutical Market, By Drug
10.1. AI in Pharmaceutical Market, by Drug, 2021-2030
10.1.1. Large Molecules
10.1.1.1. Market Revenue and Forecast (2019-2030)
10.1.2. Small Molecules
10.1.2.1. Market Revenue and Forecast (2019-2030)
Chapter 11. Global AI in Pharmaceutical Market, Regional Estimates and Trend Forecast
11.1. North America
11.1.1. Market Revenue and Forecast, by Technology (2019-2030)
11.1.2. Market Revenue and Forecast, by Application (2019-2030)
11.1.3. Market Revenue and Forecast, by Drug (2019-2030)
11.1.4. U.S.
11.1.4.1. Market Revenue and Forecast, by Technology (2019-2030)
11.1.4.2. Market Revenue and Forecast, by Application (2019-2030)
11.1.4.3. Market Revenue and Forecast, by Drug (2019-2030)
11.1.5. Rest of North America
11.1.5.1. Market Revenue and Forecast, by Technology (2019-2030)
11.1.5.2. Market Revenue and Forecast, by Application (2019-2030)
11.1.5.3. Market Revenue and Forecast, by Drug (2019-2030)
11.2. Europe
11.2.1. Market Revenue and Forecast, by Technology (2019-2030)
11.2.2. Market Revenue and Forecast, by Application (2019-2030)
11.2.3. Market Revenue and Forecast, by Drug (2019-2030)
11.2.4. UK
11.2.4.1. Market Revenue and Forecast, by Technology (2019-2030)
11.2.4.2. Market Revenue and Forecast, by Application (2019-2030)
11.2.4.3. Market Revenue and Forecast, by Drug (2019-2030)
11.2.5. Germany
11.2.5.1. Market Revenue and Forecast, by Technology (2019-2030)
11.2.5.2. Market Revenue and Forecast, by Application (2019-2030)
11.2.5.3. Market Revenue and Forecast, by Drug (2019-2030)
11.2.6. France
11.2.6.1. Market Revenue and Forecast, by Technology (2019-2030)
11.2.6.2. Market Revenue and Forecast, by Application (2019-2030)
11.2.6.3. Market Revenue and Forecast, by Drug (2019-2030)
11.2.7. Rest of Europe
11.2.7.1. Market Revenue and Forecast, by Technology (2019-2030)
11.2.7.2. Market Revenue and Forecast, by Application (2019-2030)
11.2.7.3. Market Revenue and Forecast, by Drug (2019-2030)
11.3. APAC
11.3.1. Market Revenue and Forecast, by Technology (2019-2030)
11.3.2. Market Revenue and Forecast, by Application (2019-2030)
11.3.3. Market Revenue and Forecast, by Drug (2019-2030)
11.3.4. India
11.3.4.1. Market Revenue and Forecast, by Technology (2019-2030)
11.3.4.2. Market Revenue and Forecast, by Application (2019-2030)
11.3.4.3. Market Revenue and Forecast, by Drug (2019-2030)
11.3.5. China
11.3.5.1. Market Revenue and Forecast, by Technology (2019-2030)
11.3.5.2. Market Revenue and Forecast, by Application (2019-2030)
11.3.5.3. Market Revenue and Forecast, by Drug (2019-2030)
11.3.6. Japan
11.3.6.1. Market Revenue and Forecast, by Technology (2019-2030)
11.3.6.2. Market Revenue and Forecast, by Application (2019-2030)
11.3.6.3. Market Revenue and Forecast, by Drug (2019-2030)
11.3.7. Rest of APAC
11.3.7.1. Market Revenue and Forecast, by Technology (2019-2030)
11.3.7.2. Market Revenue and Forecast, by Application (2019-2030)
11.3.7.3. Market Revenue and Forecast, by Drug (2019-2030)
11.4. MEA
11.4.1. Market Revenue and Forecast, by Technology (2019-2030)
11.4.2. Market Revenue and Forecast, by Application (2019-2030)
11.4.3. Market Revenue and Forecast, by Drug (2019-2030)
11.4.4. GCC
11.4.4.1. Market Revenue and Forecast, by Technology (2019-2030)
11.4.4.2. Market Revenue and Forecast, by Application (2019-2030)
11.4.4.3. Market Revenue and Forecast, by Drug (2019-2030)
11.4.5. North Africa
11.4.5.1. Market Revenue and Forecast, by Technology (2019-2030)
11.4.5.2. Market Revenue and Forecast, by Application (2019-2030)
11.4.5.3. Market Revenue and Forecast, by Drug (2019-2030)
11.4.6. South Africa
11.4.6.1. Market Revenue and Forecast, by Technology (2019-2030)
11.4.6.2. Market Revenue and Forecast, by Application (2019-2030)
11.4.6.3. Market Revenue and Forecast, by Drug (2019-2030)
11.4.7. Rest of MEA
11.4.7.1. Market Revenue and Forecast, by Technology (2019-2030)
11.4.7.2. Market Revenue and Forecast, by Application (2019-2030)
11.4.7.3. Market Revenue and Forecast, by Drug (2019-2030)
11.5. Latin America
11.5.1. Market Revenue and Forecast, by Technology (2019-2030)
11.5.2. Market Revenue and Forecast, by Application (2019-2030)
11.5.3. Market Revenue and Forecast, by Drug (2019-2030)
11.5.4. Brazil
11.5.4.1. Market Revenue and Forecast, by Technology (2019-2030)
11.5.4.2. Market Revenue and Forecast, by Application (2019-2030)
11.5.4.3. Market Revenue and Forecast, by Drug (2019-2030)
11.5.5. Rest of LATAM
11.5.5.1. Market Revenue and Forecast, by Technology (2019-2030)
11.5.5.2. Market Revenue and Forecast, by Application (2019-2030)
11.5.5.3. Market Revenue and Forecast, by Drug (2019-2030)
Chapter 12. Company Profiles
12.1. IBM Corporation
12.1.1. Company Overview
12.1.2. Product Offerings
12.1.3. Financial Performance
12.1.4. Recent Initiatives
12.2. Exscientia
12.2.1. Company Overview
12.2.2. Product Offerings
12.2.3. Financial Performance
12.2.4. Recent Initiatives
12.3. Deep Genomics
12.3.1. Company Overview
12.3.2. Product Offerings
12.3.3. Financial Performance
12.3.4. Recent Initiatives
12.4. Cloud Pharmaceuticals, Inc.
12.4.1. Company Overview
12.4.2. Product Offerings
12.4.3. Financial Performance
12.4.4. Recent Initiatives
12.5. Microsoft Corporation
12.5.1. Company Overview
12.5.2. Product Offerings
12.5.3. Financial Performance
12.5.4. Recent Initiatives
12.6. NVIDIA Corporation
12.6.1. Company Overview
12.6.2. Product Offerings
12.6.3. Financial Performance
12.6.4. Recent Initiatives
12.7. Insilico Medicine
12.7.1. Company Overview
12.7.2. Product Offerings
12.7.3. Financial Performance
12.7.4. Recent Initiatives
12.8. Alphabet Inc.
12.8.1. Company Overview
12.8.2. Product Offerings
12.8.3. Financial Performance
12.8.4. Recent Initiatives
12.9. Atomwise, Inc.
12.9.1. Company Overview
12.9.2. Product Offerings
12.9.3. Financial Performance
12.9.4. Recent Initiatives
12.10. Biosymetrics
12.10.1. Company Overview
12.10.2. Product Offerings
12.10.3. Financial Performance
12.10.4. Recent Initiatives
12.11. Euretos
12.11.1. Company Overview
12.11.2. Product Offerings
12.11.3. Financial Performance
12.11.4. Recent Initiatives
12.12. BenevolentAI
12.12.1. Company Overview
12.12.2. Product Offerings
12.12.3. Financial Performance
12.12.4. Recent Initiatives
Chapter 13. Research Methodology
13.1. Primary Research
13.2. Secondary Research
13.3. Assumptions
Chapter 14. Appendix
14.1. About Us
14.2. Glossary of Terms
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