AI in Drug Repurposing Market Size, Share, Trends and Forecast, 2026-2034
REPORT DETAILS
What is the AI in drug repurposing market size?
The global AI In drug repurposing market size was valued at USD 1.21 billion in 2025, growing at a CAGR of 20.10% from 2026 to 2034. Growing prevalence of chronic and rare diseases along with advancements in machine learning enabling accurate drug-target predictions is propelling the market growth.
Market Statistics
Key Takeaways
• North America held the largest share of 38.0% in 2025, in the AI in Drug Repurposing market. The region’s growth was attributed to strong AI pharma collaborations and advanced research infrastructure.
• Asia Pacific is expected to grow at a CAGR of 22.6% during 2026–2034. Government support and increasing AI use in drug research are driving the market.
• South Korea accounted for 12.0% of the Asia Pacific market in 2025. The market growth is supported by government funding and AI based drug discovery initiatives.
• The small molecules segment accounted for a market share of 39.0% in 2025. The segment’s leading position is because of established approval pathways and strong clinical data.
• The oncology segment accounted for a market share of 27.0% in 2025. Its growth is driven by the increasing cancer research and rising use of AI in precision medicine.
• The neurology segment is expected to grow at a CAGR of 20.4% during 2026–2034. Higher research spending is helping the development of AI-based treatment for neurological diseases.
Note: Figures and projections outlined in this report are the result of Polaris Market Research’s proprietary analytical processes, grounded in the latest available datasets and market observations.
What is AI in Drug Repurposing Market?
AI in Drug Repurposing Market refers to the application of artificial intelligence to identify new therapeutic uses for existing drugs. AI analyzes clinical, biological, and molecular data to predict drug–disease relationships, helping pharmaceutical companies accelerate drug discovery, lower development costs, improve success rates, and support precision medicine.
How AI Works in Drug Repurposing
- Data Collection: AI gathers information from clinical databases, scientific literature, genomic datasets, and electronic health records.
- Data Analysis: AI algorithms analyze drug properties, disease biology, molecular pathways, and protein interactions to identify hidden patterns.
- Candidate Identification: Machine learning models predict new drug–disease relationships and shortlist promising repurposing candidates.
- Prioritization: AI ranks candidate drugs based on predicted efficacy, safety, and probability of clinical success.
- Experimental Validation: The top candidates undergo laboratory testing and preclinical or clinical validation to confirm therapeutic potential.
- Further Development: Successfully validated drugs advance to clinical development and regulatory evaluation for new therapeutic indications.
What includes AI in drug repurposing market?
The market for drug repurposing AI includes sophisticated software platforms and algorithms that helps to identify new indications for approved drugs. These applications are based on machine learning, deep learning, and predictive analytics to speed up the discovery process and minimize R&D expenses. The AI systems provide drug efficacy insights, better decision-making, and reducing the time to market for new treatments in various therapeutic areas by analyzing large-scale biomedical data, clinical trials, and molecular structures.
The applications of AI platforms to identify new uses for established drugs are expanding exponentially. The platforms search massive biomedical databases and clinical histories to uncover secondary indications, thus cutting down the development costs as well as regulatory barriers. In October 2025, GNQ Insilico launched a revolutionary AI-driven assessment platform with the ability to transform precision medicine and drug discovery. The platform integrates complex machine learning algorithms to rapidly analyze complex biological data, leading to accelerated discovery of potential drug candidates and improved treatment customization. Source: https://www.businesswire.com
Source: Polaris Market Research Analysis
Biotechnology companies and research institutes are making investments in drug repurposing based on AI to enhance current pipelines. Predictive modeling and bioinformatics technologies are enabling identification of high-impact therapeutic opportunities. Partnerships among AI developers and pharmaceutical firms are growing, facilitated by efforts like NIH's NCATS program, which promotes data-driven repositioning studies.
AI in Drug Repurposing Trends for 2026
AI in drug repurposing is moving toward developing more sophisticated models capable of processing large and complex biomedical data. Machine learning, deep learning and knowledge graphs are applied to the study of drug-disease relationships, drug targets and drug repurposing. Other research efforts are being conducted to improve interpretability of AI predictions and facilitate their validation.
Generative AI and large language models are also gaining attention in drug repurposing. These tools can process scientific literature and help researchers identify possible drug candidates and treatment links. At the same time, AI is being combined with multi-omics data to support more precise drug selection and better understanding of disease mechanisms.
Market Dynamics
Market Driver- Increasing Prevalence of Chronic and Rare Diseases Is Propelling Drug Repurposing
The rising prevalence of chronic and rare diseases is increasing the need for quicker and more affordable treatment options. With the current trends, NCDs are expected to account for around 86% of the estimated 90 million annual deaths by mid-century. The rising burden of disease is driving pharmaceutical companies to adopt AI for faster and more effective drug repurposing. Source: https://news.un.org
Market Driver- Improving Accurate Drug-Target Predictions Using Machine Learning
Researchers are utilizing machine learning and deep learning to improve the prediction of drug-targets and drug-diseases. Methods such as graph neural networks and natural language processing are able to analyze large datasets and find potential drug candidates. In August 2025, Fifty1 AI Labs partnered with ViRx@Stanford to accelerate AI-driven antiviral drug repurposing and rapid response to viral threats thru the “BE READI!” program. Source: https://www.nasdaq.com
Market Opportunity- Multi-Omics Integration Supporting Precision Drug Repurposing
The use of multi-omics data, including genomics, proteomics, and metabolomics, is creating new opportunities for AI-based drug repurposing. To find disease patterns, drug responses, and possible treatment targets AI can study these large datasets. This helps researchers choose suitable drugs and develop more personalized treatments. As access to multi-omics data grows, AI can support more targeted drug repurposing.
Market Restraint- High Implementation Costs Restraining AI Adoption
High implementation costs are one of the major challenges faced by the AI in drug repurposing market. Companies have to invest in AI platforms, computing systems, data management, and skilled professionals. For smaller pharmaceutical and biotechnology companies these costs can be difficult to manage. Limited budgets may be barriers of AI adoption and access to the latest drug repurposing technologies.
Future of The AI In Drug Repurposing Market
The AI in drug repurposing market is expected to grow steadily as pharmaceutical companies accelerate digital transformation and leverage expanding biomedical datasets. Advances in generative AI, machine learning, and predictive analytics, along with increasing adoption of personalized medicine and stronger collaborations between technology providers and pharmaceutical companies, will continue to drive innovation and market growth.
Technological Advancements in AI Drug Repurposing
| Technology | Role in AI Drug Repurposing |
| Generative AI for Drug Discovery | Predicts new therapeutic uses for existing drugs and generates novel treatment hypotheses. |
| AI-Powered Molecular Simulation | Simulates drug–target interactions to identify promising repurposing candidates more quickly. |
| Digital Twins in Healthcare Research | Creates virtual patient models to evaluate drug responses before clinical testing. |
| Automated Literature Analysis | Scans scientific publications, patents, and clinical studies to uncover hidden drug–disease relationships |
| AI-Based Clinical Trial Matching | Identifies suitable patients and optimizes trial design to accelerate validation of repurposed drugs. |
| Knowledge Graph Technology | Connects drugs, diseases, genes, and proteins to reveal new therapeutic opportunities through relationship mapping. |
Source: Polaris Market Research Analysis
Adoption by Pharmaceutical Companies
Pharmaceutical companies are increasingly adopting AI-based drug repurposing platforms to improve R&D productivity, reduce development costs, and accelerate the identification of new therapeutic uses for existing drugs. By leveraging AI-driven data analysis and predictive modeling, companies can shorten discovery timelines, enhance decision-making, and unlock new commercial opportunities from established drug portfolios.
Source: Polaris Market Research Analysis
Segmental Insights
By Drug Type
Based on drug type, the AI in drug repurposing market is segmented into small molecules, biologics, vaccines, peptides, and others. Small molecules held the largest market share in 2025, accounting for 39% of the market. This was due to established approval processes, lower costs, and the availability of extensive data from past clinical trials. The capability of AI to recognize new uses for already available small molecules has fueled up repurposing programs and minimized development times.
Biologics are expected to register robust growth in the forecast period as AI algorithms are increasingly used in the analysis of intricate molecular interactions and protein structures, facilitating researchers to discover new therapeutic indications for biologic drugs.
By Therapeutic Area
On the basis of therapeutic area, the market is classified into oncology, neurology, cardiovascular diseases, infectious diseases, immunology, metabolic disorders, rare diseases, and others. Oncology accounted for the largest market share in 2025, at 27%, driven by a large number of cancer-related studies and increased adoption of AI-based models for repositioning drugs in precision oncology.
Neurology is expected to grow at a 20.4% CAGR from 2026 to 2034. The growth is supported by higher R&D spending and the need to speed up treatments for Alzheimer’s disease, Parkinson’s disease, and other neurodegenerative conditions. This was driven by a high volume of cancer-related research and growing use of AI-based models for precision drug repositioning.
By Deployment Mode
Based on deployment mode, the market is divided into cloud-based and on-premises solutions. Cloud-based platforms held the top share in 2025, accounting for 68.4% of the market. Their leading position was supported by their flexibility, scalability, and ability to handle large biomedical datasets efficiently. Enhanced collaboration between pharmaceutical companies and technology vendors for cloud-based AI solutions has further consolidated this segment's dominance.
The on-premises segment is likely to experience fastest growth during the forecast period, as pharmaceutical firms and research institutions opt for localized control of data and better security of data for sensitive projects.
By End User
Based on end user, the market is segmented into pharmaceutical & biotechnology companies, contract research organizations (CROs), academic & research institutes, healthcare providers, and others. Pharmaceutical and biotechnology companies led the market in 2025, accounting for 44.7% of the market. Their leading position was driven by the growing use of AI-based platforms to speed up discovery-to-market timelines and make better use of R&D spending.
Contract research organizations (CROs) are projected to grow at a fast rate over the forecast period, as more life science firms outsource repurposing initiatives based on AI for computational screening, validation, and preclinical examination.
AI in Drug Repurposing Market by Component
On the basis of component, the market is classified into software/platform and services. Software/platform includes AI tools used to identify potential drug candidates, study drug-target interactions, and analyze large amounts of biomedical data. These platforms help researchers find new uses for existing drugs, speeding up the repurposing process.
Services include data management, AI model development, platform setup, system integration, and technical support. The segment is expected to grow as pharmaceutical and biotechnology companies seek support in using AI for drug repurposing. For better research outcomes these services also help companies manage complex data and improve AI models.
AI in Drug Repurposing Market By Technology
On the basis of technology, the market is classified into machine learning and deep learning, natural language processing (NLP), knowledge graphs, generative AI and large language models (LLMs), and other technologies. Machine learning and deep learning help researchers study large datasets and predict drug-target relationships. NLP helps extract useful information from research papers, clinical data, and medical records.
Knowledge graphs link information about drugs, diseases, genes and biological targets to identify potential drug relationships. While finding new uses for existing drugs Generative AI and LLMs can analyze scientific information, suggest potential drug candidates, and support research teams. Other technologies also support data analysis and improve the overall drug repurposing process.
Real-World Applications of AI in Drug Repurposing
| Use Case | How AI Supports Drug Repurposing |
| Cancer Treatment |
Identifies existing drugs with potential to treat different types of cancer by analyzing molecular and clinical data. |
| Alternative Uses for Antiviral Drugs | Discovers new therapeutic applications for approved antiviral medicines against emerging diseases. |
| Rare Disease Treatment | Identifies repurposing opportunities for rare diseases where traditional drug development is limited. |
| Pandemic Response | Rapidly screens approved drugs to identify potential treatment candidates during infectious disease outbreaks. |
| Repurposing Failed Drug Candidates | Finds new indications for drugs that failed in previous trials but may be effective for other conditions. |
| Personalized Medicine | Recommends repurposed drugs based on patient-specific genetic, clinical, and disease profiles. |
Source: Polaris Market Research Analysis
Source: Polaris Market Research Analysis
Regional Analysis
North America accounted for a major share of the global AI in Drug Repurposing market in 2025, at 38.0%. Rising collaborations among AI startups and premier research centers are driving new ways of discovering repurposed drugs. The region's robust digital ecosystem and access to high-quality biomedical data continue to enhance AI-driven innovation throughout the healthcare value chain.
The U.S. AI In Drug Repurposing Market Overview
The U.S. is the major North American market, driven by a robust pharmaceutical and biotechnology industry investing in AI in drug discovery platforms. For instance, in October 2025, Massachusetts-based Lila Sciences closed a USD 350 million Series A to expand its AI-powered autonomous labs, bringing its total funding to USD 550 million. (Source: https://www.lila.ai ) The capital is raised to grow Lila's "AI Science Factories" Boston, San Francisco, and London sites where AI models develop hypotheses, run experiments, learn from outcomes, and iterate end-to-end. The nation's strong financing environment, coupled with mature collaborations amongst academia and AI solution providers, continues to propel large-scale repurposing programs.
Asia Pacific AI In Drug Repurposing Market Insights
Asia Pacific is expected to record the fastest growth during the forecast period, with a 22.6% CAGR from 2026 to 2034. This is due to the growing clinical data warehouses and healthcare databases that improve the validity of AI-driven drug discovery models. Also, partnerships between international pharmaceutical companies and local AI start-ups are facilitating quicker identification of promising repurposing candidates. Encouraging government initiatives promoting digital health innovation are further fueling the market growth.
South Korea AI in Drug Repurposing Market Analysis
South Korea is witnessing strong growth in AI solutions across life sciences research, with the market expected to grow at 12.0% CAGR during the forecast period. AI-application programs backed by the government for using AI in drug discovery and health
care analytics are creating an innovation ecosystem. In 2025, the South Korean government continued to strengthen AI-enabled drug development through programs supporting digital technologies and innovation in the domestic biopharmaceutical sector. The Ministry of Health and Welfare has highlighted the use of big data and artificial intelligence to accelerate innovation and strengthen Korea’s biohealth industry. Higher investments in home-grown AI strength and partnership with overseas biotech companies are helping to make South Korea an important regional center of AI-driven drug repurposing R&D.
Europe AI In Drug Repurposing Market Assessment
Europe continued steady growth driven by regulatory support and European Commission funding initiatives for AI-powered healthcare solutions. REMEDi4ALL, a project sponsored by the EU and led by EATRIS, was launched in 2022 to accelerate drug repurposing in Europe. With EUR 23 million in Horizon Europe funding over five years, the project aims to develop a drug repurposing innovation platform and a global network for further partnerships and policy-making. Source: https://remedi4all.org. Research collaborations among pharmaceutical companies, AI developers, and universities are rising, helping to accelerate repurposing research across therapeutic classes.
Source: Polaris Market Research Analysis
Key Players & Competitive Analysis
The global market for AI Drug Repurposing is highly competitive and fast-moving and is driven by rapid technological advancements in computational biology, artificial intelligence, and data analytics. Companies are working to develop platforms that streamline the process of discovering novel therapeutic uses of established drugs, reduce R&D time frames, and increase clinical trial success rates. Strategic alliances among pharmaceutical companies, AI technology solutions, and academia are increasingly becoming the focus of innovation as the industry continues to push for maximizing drug discovery efficacy and cost containment.
The AI drug repurposing market includes companies across different areas. Healx, Ignota Labs, Delta4, BullFrog AI, and Every Cure focus on drug repurposing and drug rescue. Recursion Pharmaceuticals, BenevolentAI, Insilico Medicine, Atomwise, and Innophore offer AI-based drug discovery platforms with repurposing capabilities. BioXcel Therapeutics, BioAge Labs, and United Therapeutics are biopharma developers using AI and related technologies.
BostonGene and Ginkgo Bioworks provide data and biology capabilities that support drug research. Academic and nonprofit initiatives such as TxGNN and Every Cure also contribute to the ecosystem. Every Cure can be discussed separately when distinguishing commercial companies from nonprofit initiatives.
Healx Ltd. uses AI to identify new treatment opportunities by finding connections between existing drugs and diseases. Its platform focuses strongly on rare diseases and helps researchers identify drugs that could be redeveloped for new uses. The company combines AI with scientific expertise to advance potential treatments through development.
Ignota Labs focuses on giving failed or shelved drug candidates a second chance. Its AI technology helps identify safety problems in drug candidates and find ways to address them. This approach supports drug repurposing by helping researchers recover existing assets and develop them for potential new therapeutic uses.
Which are the major key players in AI in drug repurposing market?
Key players in the global AI in Drug Repurposing market include Atomwise, Inc., BenevolentAI Limited, BioAge Labs, Inc., BioXcel Therapeutics, Inc., BostonGene Corporation, Cyclica Inc., Exscientia plc, Ginkgo Bioworks Holdings, Inc., Healx Ltd., Ignota Labs Ltd., Insilico Medicine, Inc., Melior Discovery, Inc., Recursion Pharmaceuticals, Inc., TxGNN, Inc., and United Therapeutics Corporation.
Key Players
· Recursion Pharmaceuticals, Inc. (including Exscientia and Cyclica capabilities)
· BenevolentAI
· Healx Ltd.
· Ignota Labs
· Insilico Medicine, Inc.
· Atomwise, Inc.
· Every Cure
· Delta4
· BullFrog AI
· Innophore
· BioAge Labs, Inc.
· BioXcel Therapeutics, Inc.
· United Therapeutics Corporation
· BostonGene Corporation
- Ginkgo Bioworks, Inc.
Challenges in the AI in Drug Repurposing Market
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Limited High-Quality Data: Incomplete and inconsistent healthcare datasets can reduce the accuracy of AI predictions.
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Regulatory Uncertainty: Evolving regulatory frameworks create challenges for the approval of AI-assisted drug repurposing.
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Data Privacy and Security: Strict data protection regulations limit access to sensitive patient and clinical information.
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Clinical Validation Requirements: AI-generated drug candidates still require extensive laboratory and clinical testing before commercialization.
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Workflow Integration: Integrating AI platforms with existing pharmaceutical R&D processes can be complex and time-consuming.
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Lack of Standardized AI Models: Differences in AI algorithms, datasets, and validation methods affect consistency and reproducibility across studies.
Recent Developments
- June 2026: Anthropic introduced Claude Science, an AI platform designed to support scientific research workflows. The platform helps with reproducible literature analysis, computational biology, and cheminformatics workflows. These capabilities can support drug discovery and may also be used for drug repurposing research. (Source: www.anthropic.com)
- In August 2025, Fifty1 Labs and BioSpark AI collaborated to convert more than 10,000 unstructured clinical case reports into a structured, searchable database featuring over 2,000 real-world patient treatment-outcome pathways. This AI-powered initiative advances drug repurposing and functional medicine by supporting prioritized candidate identification for chronic fatigue, neuroinflammation, and sleep-related disorders. Source: https://www.nasdaq.com
- In May 2025, former Verbit CEO Tom Livne founded Grace, a new startup focused on repurposing shelved drugs through advanced AI. Grace aims to secure USD 10–20 million in funding to accelerate the revival of drug candidates and reduce attrition across clinical development phases. Source: https://www.calcalistech.com
AI In Drug Repurposing Market Segmentation
By Component Outlook (Revenue, USD Billion, 2021–2034)
· Software/Platform
· Services
By Technology Outlook (Revenue, USD Billion, 2021–2034)
· ML/Deep Learning
· NLP
· Knowledge Graphs
· Generative AI/LLMs
By Drug Type Outlook (Revenue, USD Billion, 2021–2034)
- Small Molecules
- Biologics
- Vaccines
- Peptides
- Others
By Therapeutic Area Outlook (Revenue, USD Billion, 2021–2034)
- Oncology
- Neurology
- Cardiovascular Diseases
- Infectious Diseases
- Immunology
- Metabolic Disorders
- Rare Diseases
- Others
By Deployment Mode Outlook (Revenue, USD Billion, 2021–2034)
- Cloud-based
- On-premises
By End User Outlook (Revenue, USD Billion, 2021–2034)
- Pharmaceutical & Biotechnology Companies
- Contract Research Organizations (CROs)
- Academic & Research Institutes
- Healthcare Providers
- Others
By Regional Outlook (Revenue, USD Billion, 2021–2034)
- North America
- U.S.
- Canada
- Europe
- Germany
- France
- UK
- Italy
- Spain
- Netherlands
- Russia
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- Malaysia
- South Korea
- Indonesia
- Australia
- Vietnam
- Rest of Asia Pacific
- Middle East & Africa
- Saudi Arabia
- UAE
- Israel
- South Africa
- Rest of Middle East & Africa
- Latin America
- Mexico
- Brazil
- Argentina
- Rest of Latin America
AI In Drug Repurposing Market Report Scope
| Report Attributes | Details |
| Market Size in 2025 | USD 1.21 Billion |
| Market Size in 2026 | USD 1.45 Billion |
| Revenue Forecast by 2034 | USD 6.30 Billion |
| CAGR | 20.10% from 2026 to 2034 |
| Base Year | 2025 |
| Historical Data | 2021–2024 |
| Forecast Period | 2026–2034 |
| Quantitative Units | Revenue in USD billion and CAGR from 2026 to 2034 |
| Report Coverage | Revenue Forecast, Competitive Landscape, Growth Factors, and Industry Trends |
| Segments Covered |
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| Regional Scope |
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| Competitive Landscape |
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| Report Format |
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| Customization | Report customization as per your requirements with respect to countries, regions, and segmentation. |
Source: Polaris Market Research Analysis
AI In Drug Repurposing Market FAQ's
The global market size was valued at USD 1.21 billion in 2025 and is projected to grow to USD 6.30 billion by 2034.
The global market is projected to register a CAGR of 20.10% during the forecast period.
North America dominated the global market share in the AI in Drug Repurposing Market in 2025, holding the largest share of 38.0%, driven by strong AI pharma collaborations and advanced research infrastructure.
A few of the key players in the market are Atomwise, Inc., BenevolentAI Limited, BioAge Labs, Inc., BioXcel Therapeutics, Inc., BostonGene Corporation, Cyclica Inc., Exscientia plc, Ginkgo Bioworks Holdings, Inc., Healx Ltd., Ignota Labs Ltd., Insilico Medicine, Inc., Melior Discovery, Inc., Recursion Pharmaceuticals, Inc., TxGNN, Inc., and United Therapeutics Corporation
By drug type, the small molecules segment dominated the market revenue share in 2025, accounting for a 39.0% share, due to established approval pathways and strong clinical data.
By therapeutic area, the neurology segment is expected to witness the fastest growth during the forecast period, growing at a 20.4% CAGR (2026–2034), driven by higher research spending supporting the development of AI-based treatments for neurological diseases.
AI in drug repurposing uses artificial intelligence to identify new therapeutic uses for existing drugs by analyzing biological, clinical, and molecular data.
AI detects hidden drug–disease relationships, predicts treatment effectiveness, and prioritizes promising candidates, reducing research time and development costs.
Key technologies include machine learning, generative AI, natural language processing, knowledge graphs, molecular simulation, and predictive analytics.
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