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Machine Learning for Drug Repurposing Market Research Report

Published: Nov 03, 2025
ID: 4394338
127 Pages
Machine Learning
for Drug Repurposing

Machine Learning for Drug Repurposing Market - Global Growth Opportunities 2020-2033

Global Machine Learning for Drug Repurposing Market is segmented by Application (Pharmaceutical R&D, Biotechnology, Healthcare, Clinical Research Organizations, Drug Development), Type (AI-Driven Drug Discovery, Machine Learning Algorithms, Drug Target Identification, High-Throughput Screening, Predictive Modeling), and Geography (North America, LATAM, West Europe, Central & Eastern Europe, Northern Europe, Southern Europe, East Asia, Southeast Asia, South Asia, Central Asia, Oceania, MEA)

Report ID:
HTF4394338
Published:
CAGR:
17.40%
Base Year:
2024
Market Size (2024):
$3.8 billion
Forecast (2033):
$8.2 billion

Pricing

INDUSTRY OVERVIEW


The Machine Learning for Drug Repurposing is Growing at 17.40% and is expected to reach 8.2 billion by 2033.  Below mentioned are some of the dynamics shaping the Machine Learning for Drug Repurposing.
Machine Learning for Drug Repurposing Market GROWTH TREND 2024

The machine learning for drug repurposing market leverages AI and ML technologies to identify existing drugs that can be repurposed for new medical conditions. These technologies significantly accelerate drug development by analyzing large datasets to predict potential new uses for approved drugs. The market is growing rapidly as pharmaceutical companies seek more cost-effective, faster solutions to address unmet medical needs.
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Market Drivers:
The key drivers in the market include technological advancements, increasing demand by consumers for innovative products, and government-friendly policies. Our research company combines industry reports with expert interviews and market analysis tools to identify and quantify drivers such as these. We review the current trends and gather data from leading industry publications and market research firms to decipher exactly how these and other factors are encouraging or dampening market growth.

  • Growth of AI and ML Technologies
  • Increased Demand for Faster Drug Discovery
  • Need for Cost-Effective Drug Development
  • Rising Focus on Precision Medicine
  • Technological Advancements in Data Science
Market Restraints:
Some of the restraints to market growth may include regulatory challenges, high production costs, and disruptions in the supply chain. Our sources for these limitations include the regulation filings, industry surveys, and direct contributions from active participants within this marketplace. Tracking policy updates and economic reports further helps us to determine what kind of effect these factors have on the industry.
  • Lack of Standardization in Algorithms
  • Data Privacy Concerns
  • Limited Data Availability
  • Regulatory Challenges
  • Complex Computational Models
Trends in the Market:
Among the trending ones are sustainability, digital transformation, and increasing importance of data analytics. Our research company is tracking these trends through the use of trend analysis tools, social media sentiment analysis, and industry benchmarking studies. Insights in emerging market preferences and technological advancements also come from surveys and focus groups.
  • Use of AI in Drug Discovery
  • Expansion of ML in Drug Repurposing
  • Rise of Virtual Screening
  • Growth of Big Data in Healthcare
  • Increasing Use of Genomic Data
Market Opportunities:
These include emerging markets, innovation in product development, and strategic partnerships. We identify these opportunities by performing market segmentation analysis, competitive landscape assessment, and investment trend evaluation. The data is collected based on industry reports, financial performance analysis for major players, and forecasting models for identifying future growth areas.
  • Expansion in AI-Based Drug Repurposing Platforms
  • Growth in Virtual Screening for Drug Discovery
  • Increased Investment in AI for Healthcare
  • Demand for Cost-Effective Drug Development
  • Adoption of AI in Personalized Healthcare
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Regulation Shaping the Healthcare Industry


The healthcare industry is significantly influenced by a complex framework of regulations designed to ensure patient safety, efficacy of treatments, and the overall quality of care. Key regulatory areas include drug approval processes, medical device standards, and healthcare data protection. These regulations aim to maintain high standards for clinical practices and safeguard public health.

Major Regulatory Bodies Worldwide


1. U.S. Food and Drug Administration (FDA): In the United States, the FDA is a pivotal regulatory authority overseeing the approval and monitoring of pharmaceuticals, medical devices, and biologics. The FDA sets stringent standards for product safety and efficacy, which significantly impacts market entry and ongoing compliance for healthcare companies.
2. European Medicines Agency (EMA): The EMA plays a crucial role in the European Union, evaluating and supervising medicinal products. It provides centralized approval for drugs and ensures that products meet rigorous safety and efficacy standards across member states.
3. Health Canada: This agency regulates pharmaceuticals and medical devices in Canada, ensuring that products are safe, effective, and of high quality. Health Canada's regulations are aligned with international standards but tailored to meet national health needs.
4. World Health Organization (WHO): While not a regulatory body in the traditional sense, the WHO sets international health standards and provides guidelines that influence national regulatory frameworks. It plays a key role in global health policy and emergency response.
5. National Medical Products Administration (NMPA): In China, the NMPA regulates the approval and supervision of drugs and medical devices, with an increasing focus on aligning with global standards and facilitating market access.
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SWOT Analysis in the Healthcare Industry


SWOT analysis in the healthcare industry involves a structured assessment of Strengths, Weaknesses, Opportunities, and Threats to identify strategic advantages and areas for improvement.
•    Strengths: Evaluates internal factors such as advanced technology, skilled personnel, and strong brand reputation. For example, a hospital with cutting-edge medical equipment and specialized staff is considered to have a strong competitive edge.
•    Weaknesses: Identifies internal limitations like outdated facilities, regulatory compliance issues, or high operational costs. Weaknesses could include inefficient processes or lack of innovation.
•    Opportunities: Assesses external factors that could drive growth, such as emerging medical technologies, expanding markets, or favorable government policies. Opportunities might involve partnerships or new service lines.
•    Threats: Examines external challenges such as increasing competition, changing regulations, or economic downturns. Threats might include new entrants with disruptive technologies or stricter regulatory requirements.

Market Segmentation


Segmentation by Type

  • AI-Driven Drug Discovery
  • Machine Learning Algorithms
  • Drug Target Identification
  • High-Throughput Screening
  • Predictive Modeling
Machine Learning for Drug Repurposing Market value by AI-Driven Drug Discovery, Machine Learning Algorithms, Drug Target Identification, High-Throughput Screening, Predictive Modeling

Segmentation by Application

  • Pharmaceutical R&D
  • Biotechnology
  • Healthcare
  • Clinical Research Organizations
  • Drug Development
Machine Learning for Drug Repurposing Market size by Pharmaceutical R&D, Biotechnology, Healthcare, Clinical Research Organizations, Drug Development

Regional Outlook


The North America currently holds a significant share of the market, primarily due to several key factors: increasing consumption rates, a burgeoning population, and robust economic momentum. These elements collectively drive demand, positioning this region as a leader in the market. On the other hand, Europe is rapidly emerging as the fastest-growing area within the industry. This remarkable growth can be attributed to swift infrastructure development, the expansion of various industrial sectors, and a marked increase in consumer demand. These dynamics make this region a crucial player in shaping future market growth. In our report, we cover a comprehensive analysis of the regions and countries, including 
  • North America
  • LATAM
  • West Europe
  • Central & Eastern Europe
  • Northern Europe
  • Southern Europe
  • East Asia
  • Southeast Asia
  • South Asia
  • Central Asia
  • Oceania
  • MEA
Europe
North America
Fastest Growing Region
Dominating Region

The company consistently allocates significant resources to expand its research capabilities, develop new medical technologies, and enhance its pharmaceutical portfolio. Johnson & Johnson's investments in R&D, coupled with strategic acquisitions and partnerships, reinforce its position as a major contributor to advancements in healthcare. This focus on innovation and market expansion underscores the critical importance of the North American region in the global healthcare landscape.
  • IBM (US)
  • Google (US)
  • BenevolentAI (UK)
  • Insilico Medicine (US)
  • BioXcel Therapeutics (US)
  • Exscientia (UK)
  • Numerate (US)
  • Atomwise (US)
  • Owkin (France)
  • BERG Health (US)
  • Recursion Pharmaceuticals (US)
  • Healx (UK)
  • C4X Discovery (UK)
  • Exscientia (UK)
  • AIBrain (US)
Machine Learning for Drug Repurposing Competition Analysis of IBM (US), Google (US), BenevolentAI (UK), Insilico Medicine (US), BioXcel Therapeutics (US), Exscientia (UK), Numerate (US), Atomwise (US), Owkin (France), BERG Health (US), Recursion Pharmaceuticals (US), Healx (UK), C4X Discovery (UK), Exscientia (UK), AIBrain (US)

 




Regulatory Landscape



Primary and Secondary Research


Primary research involves the collection of original data directly from sources in the healthcare industry. Approaches include the survey of health professionals, interviews with patients, focus groups, and clinical trials. This gives an overview of the current practice, the needs of the patient, and the interest in emerging trends. Firsthand information on the efficacy of new treatments, an assessment of market demand, and insight into changes in regulation can be sought only with primary research.
Secondary Research: This is the investigation of existing information from a variety of sources, which may include industry reports, academic journals, government publications, and market research studies. Alfred secondary research empowers them to understand trends within industries, historical data, and competitive landscapes. It gives a wide view of the market dynamics and validates findings obtained from primary research. By combining both primary and secondary together, health organizations will be empowered to develop comprehensive strategies and make informed decisions based on a strong foundation built on data.

Report Infographics

Report Features

Details

Base Year

2024

Based Year Market Size (2023)

3.8 billion

Historical Period

2020 to 2024

CAGR (2024 to 2033)

17.40%

Forecast Period

2024 to 2033

Forecasted Period Market Size (2033)

8.2 billion

Scope of the Report

Segmentation by Type

AI-Driven Drug Discovery, Machine Learning Algorithms, Drug Target Identification, High-Throughput Screening, Predictive Modeling,

Segmentation by Application

Pharmaceutical R&D, Biotechnology, Healthcare, Clinical Research Organizations, Drug Development, Sales Channel

Regions Covered

North America, LATAM, West Europe, Central & Eastern Europe, Northern Europe, Southern Europe, East Asia, Southeast Asia, South Asia, Central Asia, Oceania, MEA

Companies Covered

IBM (US), Google (US), BenevolentAI (UK), Insilico Medicine (US), BioXcel Therapeutics (US), Exscientia (UK), Numerate (US), Atomwise (US), Owkin (France), BERG Health (US), Recursion Pharmaceuticals (US), Healx (UK), C4X Discovery (UK), Exscientia (UK), AIBrain (US)

Customization Scope

15% Free Customization (For EG)

Delivery Format

PDF and Excel through Email

Machine Learning for Drug Repurposing - Table of Contents

Chapter 1: Market Preface
1.1 Global Machine Learning for Drug Repurposing Market Landscape
1.2 Scope of the Study
1.3 Relevant Findings & Stakeholder Advantages
Chapter 2: Strategic Overview
2.1 Global Machine Learning for Drug Repurposing Market Outlook
2.2 Total Addressable Market versus Serviceable Market
2.3 Market Rivalry Projection
Chapter 3: Global Machine Learning for Drug Repurposing Market Business Environment & Changing Dynamics
3.1 Growth Drivers
3.1.1 Growth of AI and ML Technologies
3.1.2 Increased Demand for Faster Drug Discovery
3.1.3 Need for Cost-Effective Drug Development
3.1.4 Rising Focus on Precision Medicine
3.1.5 Technological Advancements in Data Science
3.2 Available Opportunities
3.2.1 Expansion in AI-Based Drug Repurposing Platforms
3.2.2 Growth in Virtual Screening for Drug Discovery
3.2.3 Increased Investment in AI for Healthcare
3.2.4 Demand for Cost-Effective Drug Development
3.2.5 Adoption of AI in Personalized Healthcare
3.3 Influencing Trends
3.3.1 Use of AI in Drug Discovery
3.3.2 Expansion of ML in Drug Repurposing
3.3.3 Rise of Virtual Screening
3.3.4 Growth of Big Data in Healthcare
3.3.5 Increasing Use of Genomic Data
3.4 Challenges
3.4.1 Lack of Standardization in Algorithms
3.4.2 Data Privacy Concerns
3.4.3 Limited Data Availability
3.4.4 Regulatory Challenges
3.4.5 Complex Computational Models
3.5 Regional Dynamics
Chapter 4: Global Machine Learning for Drug Repurposing Industry Factors Assessment
4.1 Current Scenario
4.2 PEST Analysis
4.3 Business Environment - PORTER 5-Forces Analysis
4.3.1 Supplier Leverage
4.3.2 Bargaining Power of Buyers
4.3.3 Threat of Substitutes
4.3.4 Threat from New Entrant
4.3.5 Market Competition Level
4.4 Roadmap of Machine Learning for Drug Repurposing Market
4.5 Impact of Macro-Economic Factors
4.6 Market Entry Strategies
4.7 Political and Regulatory Landscape
4.8 Supply Chain Analysis
4.9 Impact of Tariff War
Chapter 5: Machine Learning for Drug Repurposing : Competition Benchmarking & Performance Evaluation
5.1 Global Machine Learning for Drug Repurposing Market Concentration Ratio
5.1.1 CR4
5.1.2 CR8 and HH Index
5.1.2 % Market Share - Top 3
5.1.3 Market Holding by Top 5
5.2 Market Position of Manufacturers by Machine Learning for Drug Repurposing Revenue 2024
5.3 Global Machine Learning for Drug Repurposing Sales Volume by Manufacturers (2024)
5.4 BCG Matrix
5.4 Market Entropy
5.5 Technology Adoption Rates
5.6 Competitive Positioning Analysis
5.7 Market Share Dynamics
5.8 Price Competition Analysis
5.9 Product Portfolio Comparison
Chapter 6: Global Machine Learning for Drug Repurposing Market: Company Profiles
6.1 IBM (US)
6.1.1 IBM (US) Company Overview
6.1.2 IBM (US) Product/Service Portfolio & Specifications
6.1.3 IBM (US) Key Financial Metrics
6.1.4 IBM (US) SWOT Analysis
6.1.5 IBM (US) Development Activities
6.2 Google (US)
6.3 Benevolent AI (UK)
6.4 Insilico Medicine (US)
6.5 Bio Xcel Therapeutics (US)
6.6 Exscientia (UK)
6.7 Numerate (US)
6.8 Atomwise (US)
6.9 Owkin (France)
6.10 BERG Health (US)
6.11 Recursion Pharmaceuticals (US)
6.12 Healx (UK)
6.13 C4X Discovery (UK)
6.14 Exscientia (UK)
6.15 AIBrain (US)
Chapter 7: Global Machine Learning for Drug Repurposing by Type & Application (2020-2033)
7.1 Global Machine Learning for Drug Repurposing Market Revenue Analysis (USD Million) by Type (2020-2024)
7.1.1 AI-Driven Drug Discovery
7.1.2 Machine Learning Algorithms
7.1.3 Drug Target Identification
7.1.4 High-Throughput Screening
7.1.5 Predictive Modeling
7.2 Global Machine Learning for Drug Repurposing Market Revenue Analysis (USD Million) by Application (2020-2024)
7.2.1 Pharmaceutical R&D
7.2.2 Biotechnology
7.2.3 Healthcare
7.2.4 Clinical Research Organizations
7.2.5 Drug Development
7.3 Global Machine Learning for Drug Repurposing Market Revenue Analysis (USD Million) by Type (2024-2033)
7.4 Global Machine Learning for Drug Repurposing Market Revenue Analysis (USD Million) by Application (2024-2033)
Chapter 8: North America Machine Learning for Drug Repurposing Market Breakdown by Country, Type & Application
8.1 North America Machine Learning for Drug Repurposing Market by Country (USD Million) & Sales Volume (Units) [2020-2024]
8.1.1 United States
8.1.2 Canada
8.1.3 Mexico
8.2 North America Machine Learning for Drug Repurposing Market by Type (USD Million) & Sales Volume (Units) [2020-2024]
8.2.1 AI-Driven Drug Discovery
8.2.2 Machine Learning Algorithms
8.2.3 Drug Target Identification
8.2.4 High-Throughput Screening
8.2.5 Predictive Modeling
8.3 North America Machine Learning for Drug Repurposing Market by Application (USD Million) & Sales Volume (Units) [2020-2024]
8.3.1 Pharmaceutical R&D
8.3.2 Biotechnology
8.3.3 Healthcare
8.3.4 Clinical Research Organizations
8.3.5 Drug Development
8.4 North America Machine Learning for Drug Repurposing Market by Country (USD Million) & Sales Volume (Units) [2025-2033]
8.5 North America Machine Learning for Drug Repurposing Market by Type (USD Million) & Sales Volume (Units) [2025-2033]
8.6 North America Machine Learning for Drug Repurposing Market by Application (USD Million) & Sales Volume (Units) [2025-2033]
Chapter 9: Europe Machine Learning for Drug Repurposing Market Breakdown by Country, Type & Application
9.1 Europe Machine Learning for Drug Repurposing Market by Country (USD Million) & Sales Volume (Units) [2020-2024]
9.1.1 Germany
9.1.2 UK
9.1.3 France
9.1.4 Italy
9.1.5 Spain
9.1.6 Russia
9.1.7 Rest of Europe
9.2 Europe Machine Learning for Drug Repurposing Market by Type (USD Million) & Sales Volume (Units) [2020-2024]
9.2.1 AI-Driven Drug Discovery
9.2.2 Machine Learning Algorithms
9.2.3 Drug Target Identification
9.2.4 High-Throughput Screening
9.2.5 Predictive Modeling
9.3 Europe Machine Learning for Drug Repurposing Market by Application (USD Million) & Sales Volume (Units) [2020-2024]
9.3.1 Pharmaceutical R&D
9.3.2 Biotechnology
9.3.3 Healthcare
9.3.4 Clinical Research Organizations
9.3.5 Drug Development
9.4 Europe Machine Learning for Drug Repurposing Market by Country (USD Million) & Sales Volume (Units) [2025-2033]
9.5 Europe Machine Learning for Drug Repurposing Market by Type (USD Million) & Sales Volume (Units) [2025-2033]
9.6 Europe Machine Learning for Drug Repurposing Market by Application (USD Million) & Sales Volume (Units) [2025-2033]
Chapter 10: Asia Pacific Machine Learning for Drug Repurposing Market Breakdown by Country, Type & Application
10.1 Asia Pacific Machine Learning for Drug Repurposing Market by Country (USD Million) & Sales Volume (Units) [2020-2024]
10.1.1 China
10.1.2 Japan
10.1.3 India
10.1.4 South Korea
10.1.5 Australia
10.1.6 Southeast Asia
10.1.7 Rest of Asia Pacific
10.2 Asia Pacific Machine Learning for Drug Repurposing Market by Type (USD Million) & Sales Volume (Units) [2020-2024]
10.2.1 AI-Driven Drug Discovery
10.2.2 Machine Learning Algorithms
10.2.3 Drug Target Identification
10.2.4 High-Throughput Screening
10.2.5 Predictive Modeling
10.3 Asia Pacific Machine Learning for Drug Repurposing Market by Application (USD Million) & Sales Volume (Units) [2020-2024]
10.3.1 Pharmaceutical R&D
10.3.2 Biotechnology
10.3.3 Healthcare
10.3.4 Clinical Research Organizations
10.3.5 Drug Development
10.4 Asia Pacific Machine Learning for Drug Repurposing Market by Country (USD Million) & Sales Volume (Units) [2025-2033]
10.5 Asia Pacific Machine Learning for Drug Repurposing Market by Type (USD Million) & Sales Volume (Units) [2025-2033]
10.6 Asia Pacific Machine Learning for Drug Repurposing Market by Application (USD Million) & Sales Volume (Units) [2025-2033]
Chapter 11: Latin America Machine Learning for Drug Repurposing Market Breakdown by Country, Type & Application
11.1 Latin America Machine Learning for Drug Repurposing Market by Country (USD Million) & Sales Volume (Units) [2020-2024]
11.1.1 Brazil
11.1.2 Argentina
11.1.3 Chile
11.1.4 Rest of Latin America
11.2 Latin America Machine Learning for Drug Repurposing Market by Type (USD Million) & Sales Volume (Units) [2020-2024]
11.2.1 AI-Driven Drug Discovery
11.2.2 Machine Learning Algorithms
11.2.3 Drug Target Identification
11.2.4 High-Throughput Screening
11.2.5 Predictive Modeling
11.3 Latin America Machine Learning for Drug Repurposing Market by Application (USD Million) & Sales Volume (Units) [2020-2024]
11.3.1 Pharmaceutical R&D
11.3.2 Biotechnology
11.3.3 Healthcare
11.3.4 Clinical Research Organizations
11.3.5 Drug Development
11.4 Latin America Machine Learning for Drug Repurposing Market by Country (USD Million) & Sales Volume (Units) [2025-2033]
11.5 Latin America Machine Learning for Drug Repurposing Market by Type (USD Million) & Sales Volume (Units) [2025-2033]
11.6 Latin America Machine Learning for Drug Repurposing Market by Application (USD Million) & Sales Volume (Units) [2025-2033]
Chapter 12: Middle East & Africa Machine Learning for Drug Repurposing Market Breakdown by Country, Type & Application
12.1 Middle East & Africa Machine Learning for Drug Repurposing Market by Country (USD Million) & Sales Volume (Units) [2020-2024]
12.1.1 Saudi Arabia
12.1.2 UAE
12.1.3 South Africa
12.1.4 Egypt
12.1.5 Rest of Middle East & Africa
12.2 Middle East & Africa Machine Learning for Drug Repurposing Market by Type (USD Million) & Sales Volume (Units) [2020-2024]
12.2.1 AI-Driven Drug Discovery
12.2.2 Machine Learning Algorithms
12.2.3 Drug Target Identification
12.2.4 High-Throughput Screening
12.2.5 Predictive Modeling
12.3 Middle East & Africa Machine Learning for Drug Repurposing Market by Application (USD Million) & Sales Volume (Units) [2020-2024]
12.3.1 Pharmaceutical R&D
12.3.2 Biotechnology
12.3.3 Healthcare
12.3.4 Clinical Research Organizations
12.3.5 Drug Development
12.4 Middle East & Africa Machine Learning for Drug Repurposing Market by Country (USD Million) & Sales Volume (Units) [2025-2033]
12.5 Middle East & Africa Machine Learning for Drug Repurposing Market by Type (USD Million) & Sales Volume (Units) [2025-2033]
12.6 Middle East & Africa Machine Learning for Drug Repurposing Market by Application (USD Million) & Sales Volume (Units) [2025-2033]
Chapter 13: Research Finding and Conclusion
13.1 Research Finding
13.2 Conclusion
13.3 Analyst Recommendation

Frequently Asked Questions (FAQ):

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