Global AI in Drug Repurposing Market 2026 – 2035

The global market size of AI in drug repurposing is USD 1.28 billion in 2025 and the market size is forecast to grow to USD 1.51 billion in 2026 and USD 10.56 billion by 2035 at an annual CAGR of 23.4% from 2026 to 2035. The market is driven by the increasing need to minimize costs and shorten the time involved in drug development processes due to lengthy and cost-consuming traditional processes.

Various AI based platforms are facilitating finding novel uses for established drugs quickly and efficiently by analysis of extensive biological and clinical data. Additionally, the increasing prevalence of chronic and rare diseases, increased availability of real-world data, and advancements in machine learning and computational biology technologies are fueling the adoption of Ai in drug repurposing.

Market Highlight

• North America was leading the market share in Ai in drug repurposing with 54% of the share in the year 2025.

• Asia Pacific will grow with a CAGR of 25% in the years 2026-35, owing to the chronic disease monitoring trend in the North America region.

• The Software & Platforms segment has the leading market share with 65% in the year 2025, By Component, leading across the globe.

• By Component, services will grow with the highest CAGR of 26.5% in the years 2026-35.

• The Technology, Machine Learning/Deep Learning segments had the highest market share of 46% in 2025 while Generative Ai & Large language models(LLM) is anticipated to grow with the highest CAGR of 24% over the projected period 2026-35.

• Oncology segment acquired 38% of the market share in 2025 by end use.

Significant Growth Factors

• Increased Demand for Shortening Drug Development Time and Reducing Development Costs: Traditional drug development is extremely slow and costly. It typically takes more than 10 to 12 years with billions of dollars invested. The AI based drug repurposing, on the other hand, is dramatically reducing the need to invest huge money and time to develop new drug candidates and is identifying new therapeutic uses of known or failed drug candidates. Leveraging the machine learning algorithms, prediction models can process the biological data, clinical trial data, molecule interactions, etc., rapidly and quickly in order to identify effective drug candidates with reduced cost and risk. As the pressure of increasing R&D efficiency on the pharmaceutical companies grows higher and higher, the AI based repurposing is expected to witness widespread adoption in the industry due to its effectiveness and affordability.

• The Rising Availability of Real-World and Multi-Omics Data: Availability of a huge volume of healthcare-related data, including but not limited to electronic health records, genomics data, proteomic data, and clinical trial data, is helping the adoption of AI in drug repurposing. These varied types of data are analyzed and integrated by AI platform and hence, correlation between diseases and the drug response can be identified. Real world evidence will help in off-label use discovery and long-term outcome prediction. Furthermore, the sharing of medical data across companies is increasing, and it will enable a higher rate of AI success.

• Advancements in Artificial Intelligence and Computational Biology: Rapid improvements of Artificial Intelligence, including deep learning, natural language processing (NLP), and network-based modeling are revolutionizing drug repurposing. These technologies provide the researchers with a means to analyze the intricate biological pathway, protein-protein interactions, and disease progression process with an increased depth and scale than ever. The AI technology can help extract data from various unstructured resources such as scientific journals and clinical notes. Enhancements in computational power and the widespread usage of cloud based platforms, on the other hand, enable higher processing speed and scale of these advanced solutions. With constant improvement and development of AI models for prediction of drug efficacy and safety, the drug repurposing market is expanding at a rapid pace.

• Increasing Prevalence of Chronic and Rare Diseases: Chronic diseases like cancer, heart diseases, and Alzheimer's are prevalent among increasing populations, and it will drive a huge demand for rapid development of new therapeutics. Rare diseases often lack specific therapies due to their rarity, which poses an insufficient incentive for drug development by commercial organizations. In this case, the AI based drug repurposing will become a solution to speed up development and ensure faster patient access. It is therefore expected that the AI driven repurposing will witness a high adoption by healthcare systems given its ability to bring up existing therapies to address unmet needs in chronic diseases and rare diseases.

• Growing Strategic Collaborations and Investments in AI Healthcare: A growing number of collaborations between pharmaceutical and biopharmaceutical companies with AI companies will be increasingly observed in the future. These collaborations combine medical expertise with innovative AI approaches to enhance and expedite the drug discovery and repurposing process. Furthermore, increasing venture capital investments and government funding initiatives in AI driven healthcare technology will continue to stimulate growth of the drug repurposing market. Startups as well as well-established corporations have begun to actively invest in and integrate AI solutions to improve the drug development pipelines and accelerate market entry of the repurposed drugs.

• Growing Regulatory Support and Focus on Accelerated Drug Approvals: Increased interest of the regulatory authorities towards effective drug repurposing approaches has led to increased programs for expedited approvals of these drugs. Orphan drug designation, fast-track review, and emergency use authorization are some of the regulatory measures adopted to accelerate the review and approval process of drugs that are identified through repurposing. Furthermore, utilization of AI based insights for drug submission to regulatory agencies will improve the efficiency of the approval process. The government's funding and regulatory policies promoting digital health technology are expected to continue encouraging pharma companies to take up AI based repurposing.

What are the Major Advances Changing the Ai in drug repurposing Market Today?

• Improvement of the AI algorithms and predictive modeling: Continuous advancement in the field of artificial intelligence, including deep learning, machine learning, and network-based models, is revolutionizing the drug repurposing domain. These sophisticated algorithms are able to precisely identify implicit relationships between drugs, targets, and diseases by analyzing voluminous datasets. AI models have already achieved the ability to predict drug efficacy, toxicity, and possible off-target effects at an earlier stage of development. Reducing the probability of failure in clinical trials and accelerating decision-making are key benefits of the advanced algorithms. Furthermore, improvement in the interpretability of AI (explainable AI) provides the researchers a better understanding about the outcomes. These technological breakthroughs are strengthening the reliability and usage of AI based drug repurposing across the pharmaceutical and biotech industries.

• Integration of Multi-Omics and real-world data: Integration of data from Genomics, proteomics, transcriptomics, and real world clinical data is major progress in the market. AI platforms integrate multiple databases to derive an in-depth understanding of disease mechanisms and drug-target interactions. In addition to genomics data, the utility of real-world evidence from electronic health records (EHRs) and patient registries to identify new applications for existing drugs is on the rise. This data-driven approach leads to higher accuracy and enables personalized treatment. With the increasing availability of the data through the digital health initiatives and large databases, the efficacy and scalability of AI based drug repurposing have been substantially augmented.

• Natural language processing for unstructured data analysis: Significant amount of biomedical information is present in unstructured formats like publications, clinical trial results, and medical notes. With the increasing advancement in natural language processing, AI can derive valuable insights from these sources efficiently. NLP-powered platforms can analyze research papers and clinical notes of patients at an extremely high speed to identify drug-disease relationships and thus minimize the literature review process to enable better hypotheses generation, increasing innovation in this area.

• Cloud computing and scalable AI infrastructure: Deployment of cloud based platforms allows the drug repurposing process to utilize AI solutions at a reduced cost and on a large scale. High performance computation facilities available at cloud platforms facilitate high scale processing of biological datasets and execution of large-scale simulations. These platforms enable collaboration between institutions by allowing easy sharing of the data and remote access, making the technology usable at almost all scales irrespective of the organization’s size.

Category Wise Insights

By Component

Why Software & Platforms Take Over the Market??

AI in repurposing software and platforms constitutes a significant part of the AI in drug repurposing market due to the fact that these are underlying technologies. This platform analyzes data like genomic, clinical trial outcome, and real world data to discover novel indications of old drugs by using AI algorithms. Pharmaceutical companies are increasingly employing such platforms to accelerate the process and reduce R&D costs. Automation and scalability of the software and platforms, together with the ease of integration with the existing IT systems, attract pharmaceutical and biotech companies. Advancement in cloud computing and AI frameworks further strengthens the capabilities of such platforms.

Services represent the fastest-growing segment due to increasing demand for AI consultation, implementation, and data analysis support. A lot of pharmaceutical and biotechnology companies lack in-house AI expertise, therefore requiring additional support from specialized service providers. Services involve model development, data curation, validation, and regulatory support. As the application of AI grows, the need for end-to-end solutions grows to enhance drug repurposing strategy. Increased complexity of AI systems and the requirement for customization have significantly pushed the demand for service-based offerings.

By Technology

Why Machine Learning/Deep Learning Dominates the Market?

Machine learning and deep learning are dominant in AI in the drug repurposing market due to the ability to analyze large datasets and to identify complex biological relations. Machine learning and deep learning algorithms are typically used to accurately predict the drug combinations' effectiveness, disease pathways, treatment outcomes, etc. In particular, deep learning models show higher capabilities of dealing with high-dimensional data such as genomics and molecular structure data. The effectiveness and widespread use of machine learning and deep learning models have made these algorithms the preferred technology for pharmaceutical researchers, and they are integrated with high performance computing systems and cloud computing platforms for enhanced operation.

Generative AI & Large Language Models (LLMs) are the fastest growing segment due to their potential to simulate molecule structure, generate hypotheses, and analyze unstructured biomedical data. They can rapidly screen drug combinations and predict potential therapeutic effects. LLMs can also help researchers analyze literature by mining data from a vast number of scientific articles. Generative AI has a rapid growth in the market and is continuing to innovate.

By Application

Why Oncology Dominates the Market?

Oncology accounts for the largest application segment in AI in the drug repurposing market because it is a complex field of significant unmet medical need. Since the complexity and variability of cancer, a lot of innovation is always required. AI driven drug repurposing can enable the discovery of appropriate drugs for new indications in oncology, leading to a shorter development time. The large amount of oncology data that is readily available (genomic, clinical, etc.) also supports AI based analysis. Both the high investment in R&D and the demand for cost-effective therapies push the market. Thus, there is a urgent demand to find quick and effective cancer treatments.

Rare & Orphan Diseases is the fastest growing segment of AI in the drug repurposing market due to their low medical needs. Because of limited patient populations, traditional methods to develop drugs for rare & orphan diseases are not financially viable for pharmaceutical companies. This makes the AI driven drug repurposing a viable solution where drugs are repurposed for orphan conditions. Regulations like the orphan drug designation and accelerated approval processes for orphan drugs contribute to market growth. An increased focus and research are growing for rare and orphan diseases.

Report Scope

Regional Analysis

Why was Asia Pacific Dominating the Market in 2025?

Asia Pacific is likely to dominate the AI in drug repurposing market in 2025 primarily due to its rapidly expanding pharmaceutical ecosystem, massive patient population, and increasing adoption of AI technologies in healthcare. The region holds a significant share of global clinical data, thus providing a solid base for AI driven drug discovery and repurposing. Countries such as China, India, and Japan are seeing an increase in the prevalence of chronic diseases such as cancer, cardiovascular diseases, neurological conditions, etc. Which is creating strong demand for rapid and cost-effective treatment options. Furthermore, governments in the region are pouring funds into digital health, AI research, and biotech innovations, which is driving the growth. The presence of numerous CROs (contract research organizations), an increasing number of clinical trials, and low operational costs also favor AI driven drug repurposing application adoption, and this makes the Asia Pacific the dominant region.

China Market Trends

China has one of the largest AI in drug repurposing markets due to its aggressive government support, sheer amount of health care data available, and blooming biotechnology sector. The country is pushing for AI in drug development with the help of national initiatives and funds allocated by the government. China has an abundant population with over 1.4 billion people suffering from various types of chronic and infectious diseases, creating the need for swift and effective drug development processes. The country's firms are increasingly tying up with global pharma companies and AI startups to spur innovation. EMR (electronic medical records), genomic databases, and the clinical trial infrastructure are helping train sophisticated AI models. The market is also getting a boost from reforms that accelerate drug approval processes in the country.

What is the reason North America is enjoying consistent growth?

The AI in the drug repurposing market is steady in North America due to its advanced technological infrastructure, high research and development capabilities, and early adoption of artificial intelligence in healthcare. The region, which mostly comprises the U.S., has some of the best pharmaceutical companies, AI startups, and research institutes engaged in the field of AI in drug repurposing. Large volume of high-quality datasets, such as genomic and clinical data, ensures efficient AI models. A considerable flow of funding from VCs (venture capitalists) and government institutions supports innovation. Favorable regulatory environments and a welcoming approach by industry towards AI enabled methods are also increasing the demand. Collaboration between academia, biotech companies, and tech firms will also favor market expansion.

What is the Size of the U.S. Market?

The Ai in drug repurposing U.S. Market size is estimated to reach USD 0.61 Billion in 2025 and it is projected to grow at a robust rate of 23.1% during the forecast period from 2026 to 2035.

U.S. Market Trends

The U.S. Market sees a large rate of adoption of advanced AI technologies and a strong integration of data driven drug development processes. The pharma companies are using AI platforms for identifying new applications for existing drugs, hence considerably reducing the timelines and development costs. High density of well-established AI companies and innovative biotech firms is driving the advancement in the domain. The regulatory bodies are also becoming increasingly supportive of AI based approaches, which fuels market growth. Real world evidence, EMR and genomic data are further complementing the development in AI models. A string of strategic alliances and M&A's are also happening among tech and pharma giants in the U.S., boosting the market.

Why is Europe Dwelling on Efficiency and Clinical Standardization?

Europe is an attractive market mainly due to its focus on regulatory compliance, clinical standardization and a cost efficient healthcare delivery system. Well established health care infrastructure and increasing population of elderly citizens in the region are driving the burden of chronic diseases. Emphasis is on evidence based medicine that pushes the region to adopt AI based tools to deliver dependable and reproducible results in AI in drug repurposing. A high standard of safety and efficacy is ensured by rigorous regulatory systems, which instills confidence in health care providers. Collaborative research between academic institutions and pharmaceutical companies, coupled with government initiatives, is also acting as a major growth driver. The need for optimizing healthcare expenditures further fuels market adoption by presenting a cheap alternative in drug repurposing.

Germany Market Trends

Germany is one of the key markets in Europe that contributes to AI in the drug repurposing market due to its well-established research infrastructure, sophisticated healthcare system, and pioneering research in pharmaceuticals. High research institutions, universities, and biotech companies are driving AI in drug development innovation through a network across the country. A robust health care infrastructure and high spending also contribute positively to the market. High density of quality clinical and genomic data is contributing positively to building efficient AI models for market growth. The adoption is further spurred by governmental support for the market through research initiatives focusing on digital health. Research organizations and the industry are seen to be working closely on various initiatives in the country to achieve growth.

Why then is the Middle East & Africa Region Growing?

The MEA market for AI in drug repurposing is gaining pace due to rising investments in health care digitalization and increased adoption of modern technologies. Investments are flowing into the Middle Eastern regions of the UAE and Saudi Arabia, focusing on biotechnology and AI as part of economic diversification. EMR and digital platforms are enabling AI based data analysis. In Africa, gradually improving infrastructure and growing contributions to international clinical research are creating opportunities for growth of this market. Increasing prevalence of infectious diseases and chronic disorders in the continent will provide a push to the market with a cheaper alternative in drug repurposing.

Top Players in the Market and Their Offerings

• BostonGene Corporation

• BenevolentAI

• Innophore

• Delta4.ai

• BioXcel Therapeutics Inc.

• BullFrog AI Holdings Inc.

• Graphwise

• Owkin Inc

• Insilico Medicine

• Healx.

• Others

Key Developments

The market is booming and has been tremendously evolving, and players in the industry intend to enhance both capabilities and product portfolios.

• In August 2025, Fifty1 Labs announced collaboration with BioSpark AI for the conversion of more than 10,000 clinical case reports from unstructured into a structured and queryable database composed of over 2,000 real-world treatment-outcome pathways per patient. This AI-powered initiative provides greater data access and helps the rapid identification of potential therapeutic drug candidates for chronic fatigue, neuroinflammation, and sleep disorders and is intended to make structured and organized data easier to query while facilitating the acceleration of the drug repurposing and functional medicine innovation.

• In May 2025, Tom Livne, former Verbit CEO announced the formation of Grace, an AI-based start-up aiming to repurpose dormant or otherwise under-utilized drug candidates and intending to raise USD 10-20M to accelerate its platform development by leveraging AI technologies to identify suitable therapeutic opportunities while increasing success rates during the clinical phase. By leveraging artificial intelligence, this approach to drug discovery addresses the inefficiency in drug development and facilitates a speedier process.

Such a strategy allows companies to consolidate their market share, grow their product portfolios, enhance their technological capacity, and tap into growth opportunities in the prevailing market.

The AI in Drug Repurposing Market is segmented as follows:

By Component

• Software & Platforms

• Services

By Technology

• Machine Learning/Deep Learning

• Natural Language Processing (NLP)

• Knowledge Graphs & Network-Based AI

• Generative AI & Large Language Models (LLMs)

• Computer Vision

By Application

• Oncology   

• Neurology

• Cardiology

• Infectious Diseases

• Rare & Orphan Diseases       

• Other

Regional Coverage:

North America

• U.S.

• Canada

• Mexico

• Rest of North America 

Europe

• Germany

• France

• U.K.

• Russia

• Italy

• Spain

• Netherlands

• Rest of Europe

Asia Pacific

• China

• Japan

• India

• New Zealand

• Australia

• South Korea

• Taiwan

• Rest of Asia Pacific 

The Middle East & Africa

• Saudi Arabia

• UAE

• Egypt

• Kuwait

• South Africa

• Rest of the Middle East & Africa

Latin America

• Brazil

• Argentina

• Rest of Latin America