Global AI in Biopharmaceutical Development Market Size is valued at USD 991.0 Million in 2024 and is predicted to reach USD 13,518.8 Million by the year 2034 at a 30.0% CAGR during the forecast period for 2025-2034.
Key Industry Insights & Findings from the Report:
Artificial intelligence (AI) is a technology-based system that utilizes various advanced tools and networks to simulate human intelligence. Simultaneously, it does not appear to be on the verge of totally replacing human physical presence. With greater automation, Artificial Intelligence can manage massive volumes of data. AI employs systems and software that can read and learn from input data to make autonomous decisions to achieve specified goals. In the pharmaceutical industry, its uses are constantly being expanded.
AI encompasses a variety of approach domains, including reasoning, knowledge representation, solution search, and machine learning (ML). It can help with rational drug development, decision-making, determining the best treatment for a patient, including tailored medicines, and managing clinical data for future drug development. In the case of drug discovery, AI can distinguish hit and lead compounds, allowing for faster therapeutic target validation and structural design optimization. The pharma industry faces challenges in overcoming the high attrition rates in drug development. The pharma industry is collaborating with AI industries to overcome challenges, and AI will improve the efficiency of the drug development process.
The biopharma industry has been changed by continuous medical and scientific innovation. New business models have emerged due to the growing demand for greater patient involvement and experience. As a result, AI is becoming increasingly common in the biopharmaceutical business, resulting in market expansion. Also, increasing drug development expenditure and awareness about AI’s capacity to work are significant reasons to elevate the market growth in the upcoming years. In 2021, Health-tech giant Nvidia collaborated with AstraZeneca and the University of Florida’s academic health centre, UF Health, on new artificial intelligence research projects to accelerate drug discovery and improve patient care.
The deficiency of skilled workers is a significant challenge in front of this market. Apart from this, a lack of regulations and ethical issues will hamper the market growth. Increasing use will affect unemployment and rise in competition. In addition, the cost of artificial intelligence devices, applications, and software are the most prominent issues in this AI-based biopharmaceutical development market.
The AI in the biopharmaceutical development market is segmented by type, application and end-user. Based on the types of the market includes monoclonal antibodies, vaccines, and recombinant proteins. Among these, monoclonal antibodies and vaccines type will lead the segment due to maximum usage of AI-based technologies and increasing investments in their research. While by applications, the market comprises research and discovery, clinical development, manufacturing & supply chain and other subsegments. At last End-User subsegment contains biopharmaceutical manufacturers, contract manufacturing organizations or contract research organizations (CRO), and academic & government research institutes. CROs and CMOs are expected to lead the market due to increased demand for their services regarding therapeutic developments.
By region, Asia Pacific is likely to account for the majority shares in the coming forecasting period 2024-2031 because of the growing software and services industry and funding for their development.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 991.0 Million |
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Revenue Forecast In 2034 |
USD 13,518.8 Million |
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Growth Rate CAGR |
CAGR of 30.0% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$Mn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, By Application, By End-User |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico ;The UK; France; Italy; Spain; China; Japan; India; South Korea; South East Asia; South Korea; South East Asia |
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Competitive Landscape |
IBM Watson Health, Google (Alphabet Inc.), Concreto HealthAI, Nvidia Corporation, PathAI, Atomwise, Inc., Deep Genomics, Cloud Pharmaceuticals, Inc., Ai-biopharma S.A.S., and Microsoft Corporation, others |
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Customization Scope |
Free customization report with the procurement of the report, Modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI in Biopharmaceutical Development Market Snapshot
Chapter 4. Global AI in Biopharmaceutical Development Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis of AI Industry
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. COVID-19 Impact on Metaverse Industry
Chapter 5. Market Segmentation 1: By Type Estimates & Trend Analysis
5.1. By Type & Market Share, 2024 & 2034
5.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following By Type:
5.2.1. Monoclonal antibodies
5.2.2. Vaccines
5.2.3. Recombinant proteins
Chapter 6. Market Segmentation 2: By Application Estimates & Trend Analysis
6.1. By Application & Market Share, 2024 & 2034
6.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following Technology Type:
6.2.1. Research and discovery
6.2.2. Clinical development
6.2.3. Manufacturing & supply chain and other subsegments
Chapter 7. Market Segmentation 3: By End-User Estimates & Trend Analysis
7.1. By End-user & Market Share, 2024 & 2034
7.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 to 2034 for the following End-user:
7.2.1. Biopharmaceutical manufacturers
7.2.2. Contract manufacturing organizations (CMOs) or contract research organizations (CROs)
7.2.3. Academic & government research institutes
Chapter 8. AI in Biopharmaceutical Development Market Segmentation 4 : Regional Estimates & Trend Analysis
8.1. North America
8.1.1. North America AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by Type, 2021-2034
8.1.2. North America AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by Application, 2021-2034
8.1.3. North America AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by End-user, 2021-2034
8.1.4. North America AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by country, 2021-2034
8.1.4.1. U.S.
8.1.4.2. Canada
8.2. Europe
8.2.1. Europe AI in Biopharmaceutical Development Market revenue (US$ Million) by by Type, 2021-2034
8.2.2. Europe AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by Application, 2021-2034
8.2.3. Europe AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by End-user, 2021-2034
8.2.4. Europe AI in Biopharmaceutical Development Market revenue (US$ Million) by country, 2021-2034
8.2.4.1. Germany
8.2.4.2. Poland
8.2.4.3. France
8.2.4.4. Italy
8.2.4.5. Spain
8.2.4.6. UK
8.2.4.7. Rest of Europe
8.3. Asia Pacific
8.3.1. Asia Pacific AI in Biopharmaceutical Development Market revenue (US$ Million) by by Type, 2021-2034
8.3.2. Asia Pacific AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by Application, 2021-2034
8.3.3. Asia Pacific AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by End-user, 2021-2034
8.3.4. Asia Pacific AI in Biopharmaceutical Development Market revenue (US$ Million) by country, 2021-2034
8.3.4.1. China
8.3.4.2. India
8.3.4.3. Japan
8.3.4.4. Australia
8.3.4.5. Rest of Asia Pacific
8.4. Latin America
8.4.1. Latin America AI in Biopharmaceutical Development Market revenue (US$ Million) by Type, 2021-2034
8.4.2. Latin America AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by Application, 2021-2034
8.4.3. Latin America AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by End-user, 2021-2034
8.4.4. Latin America AI in Biopharmaceutical Development Market revenue (US$ Million) by country, (US$ Million) 2021-2034
8.4.4.1. Brazil
8.4.4.2. Rest of Latin America
8.5. Middle East & Africa
8.5.1. Middle East & Africa AI in Biopharmaceutical Development Market revenue (US$ Million) by Type, 2021-2034
8.5.2. Middle East & Africa AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by Application, 2021-2034
8.5.3. Middle East & Africa AI in Biopharmaceutical Development Market revenue (US$ Million) estimates and forecasts by End-user, 2021-2034
8.5.4. Middle East & Africa AI in Biopharmaceutical Development Market revenue (US$ Million) by country, (US$ Million) 2021-2034
8.5.4.1. South Africa
8.5.4.2. GCC Countries
8.5.4.3. Rest of MEA
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. IBM Watson Health
9.2.2. Google (Alphabet Inc.)
9.2.3. Concreto HealthAI
9.2.4. Nvidia Corporation
9.2.5. PathAI
9.2.6. Atomwise, Inc.
9.2.7. Deep Genomics
9.2.8. Cloud Pharmaceuticals, Inc.
9.2.9. Ai-biopharma S.A.S.
9.2.10. Microsoft Corporation
9.2.11. Other Prominent Players
Global AI in Biopharmaceutical Development Market, by Type
Global AI in Biopharmaceutical Development Market, by Application
Global AI in Biopharmaceutical Development Market, by End-User
Global AI in Biopharmaceutical Development Market, by Region
North America AI in Biopharmaceutical Development Market , by Country
Europe AI in Biopharmaceutical Development Market , by Country
Asia Pacific AI in Biopharmaceutical Development Market , by Country
Latin America AI in Biopharmaceutical Development Market , by Country
Middle East & Africa AI in Biopharmaceutical Development Market , by Country
Competitive Landscape
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
To know more about the research methodology used for this study, kindly contact us/click here.
