PUBLISHER: AnalystView Market Insights | PRODUCT CODE: 1944411
PUBLISHER: AnalystView Market Insights | PRODUCT CODE: 1944411
Artificial Intelligence In Drug Repurposing Market size was valued at US$ 990.98 Million in 2024, expanding at a CAGR of 20.10% from 2025 to 2032.
Artificial Intelligence in Drug Repurposing refers to the application of intelligent computational systems that identify new therapeutic uses for existing drugs by analyzing large, complex biomedical datasets. It leverages artificial intelligence technologies, such as machine learning, deep learning, and natural language processing, to uncover hidden relationships among drugs, targets, and diseases. These AI-driven tools help accelerate drug discovery timelines, reduce development costs, and improve the success rate of identifying viable treatment candidates. By automating data analysis and hypothesis generation, they enhance decision-making and research efficiency within pharmaceutical and biotechnology organizations. Artificial intelligence in drug repurposing also supports faster responses to unmet medical needs and enables more accessible, data-driven, and cost-effective drug development strategies.
Artificial Intelligence In Drug Repurposing Market- Market Dynamics
Increasing adoption of AI and machine learning in pharmaceutical R&D
Increasing adoption of AI and machine learning in pharmaceutical R&D refers to the growing integration of advanced computational models within drug discovery and development processes to improve research efficiency and outcomes. It enables the analysis of vast and complex biomedical datasets, including genomic data, clinical trial results, and scientific literature, to identify novel drug disease relationships with greater accuracy. In artificial intelligence driven drug repurposing, this adoption supports predictive modeling, target identification, and rapid screening of existing drug libraries for new therapeutic applications. As a result, pharmaceutical companies are able to reduce development timelines, lower R&D costs, and improve decision-making in early-stage research. Moreover, AI and machine learning enhance collaboration across digital research platforms, enabling seamless data integration from multiple sources. They also support continuous model refinement and learning through real-world and experimental data. Overall, the increasing use of AI and machine learning strengthens the scalability, reliability, and effectiveness of drug repurposing strategies in modern pharmaceutical R&D ecosystems. For example, according to the U.S. Food and Drug Administration's Center for Drug Evaluation and Research (CDER), the number of drug application submissions incorporating artificial intelligence has risen markedly across the drug development lifecycle, with over 500 AI-related submissions recorded between 2016 and 2023. In addition, the FDA released draft guidance in 2025 providing recommendations on the responsible use of AI to support regulatory decision-making for drug and biological products. This development highlights the FDA's proactive approach to encouraging innovation while maintaining rigorous standards for safety and efficacy in AI-enabled drug development. This underscores solid regulatory and institutional backing for the integration of advanced AI and machine learning tools within pharmaceutical research and development. It also reflects wider industry trends where data-driven technologies are being increasingly adopted to accelerate drug repurposing, enhance analytical accuracy, and optimize R&D decision-making. Consequently, AI-enabled approaches are emerging as efficient and cost-effective strategies for identifying new therapeutic applications and addressing unmet medical needs.
The Global Artificial Intelligence in Drug Repurposing Market is segmented by Therapeutic Area, Drug Type, End User, Deployment Mode, and Region.
The market is divided into three categories based on Therapeutic Area Oncology, Neurology, and Cardiovascular Diseases. The Oncology segment represents a significant portion of the Artificial Intelligence in Drug Repurposing market due to its capacity to speed up the discovery of new therapeutic uses for existing cancer drugs. Accelerating drug discovery in oncology involves using advanced technologies to reduce the time required to identify effective cancer treatments by analyzing extensive datasets, clinical trial outcomes, and molecular interactions. This process leverages AI-powered algorithms, predictive modeling, and machine learning to optimize candidate selection and streamline research workflows. Shortening discovery timelines improves R&D efficiency, lowers development costs, and enables faster delivery of potentially life-saving therapies. In AI-driven drug repurposing, this acceleration is achieved through computational screening, target prediction, and data-driven insights, allowing researchers to rapidly identify, validate, and prioritize promising oncology drug candidates. For instance, according to the World Health Organization's 2024 report on the global cancer burden, an estimated 20 million new cancer cases and 9.7 million cancer-related deaths occurred worldwide in 2022, with approximately 53.5 million people living within five years of a cancer diagnosis. These figures underscore the rapidly increasing global cancer burden and highlight the urgent need for improved prevention, early detection, and treatment services across healthcare systems.
The market is divided into two categories based on Drug Type: Small Molecule Drugs and Biologics. The Small Molecule Drugs segment accounts for the largest share of the Artificial Intelligence in Drug Repurposing market, driven by its extensive applicability and well-documented safety profiles. Its dominance is supported by the availability of rich historical and clinical data, which allows AI algorithms to efficiently analyze molecular structures, predict target interactions, and identify promising repurposing opportunities. This segment enables faster and more cost-effective drug development compared with new drug discovery, supporting quicker clinical translation. Additionally, small molecules benefit from scalable AI-driven workflows, applicability across multiple therapeutic areas, and consistent outcomes in repurposing initiatives, making them a key focus in the market. For instance, according to the Big Data in Medicine is Driving Big Changes, the volume of biomedical and clinical data has grown exponentially, with over 23 million biomedical articles indexed in PubMed, and nearly one million new publications added that year alone, reflecting the rapidly increasing availability of research and clinical information. The article also highlights that more than 80 % of healthcare data is unstructured, including clinical notes and narrative records, underscoring the scale, diversity, and complexity of medical data now being generated and leveraged for research, analysis, and data-driven decision-making. This extensive adoption underscores the maturity of digital pharmaceutical and biomedical research infrastructure. It reflects broader trends in which AI-driven technologies are enabling scalable access to clinical and molecular data, improved interoperability across research platforms, and the expanded use of AI-based solutions for drug repurposing and discovery.
Artificial Intelligence In Drug Repurposing Market- Geographical Insights
The Artificial Intelligence in Drug Repurposing market demonstrates significant regional variation, influenced by differences in pharmaceutical R&D infrastructure, healthcare systems, and AI technology adoption. North America continues to exhibit strong activity, supported by robust biotech and pharmaceutical research networks, widespread use of AI-driven drug repurposing platforms, and high demand for innovative therapeutics, particularly in the U.S. and Canada. Europe is witnessing consistent expansion, backed by growing investments in pharmaceutical research, regulatory support for AI-based drug development, and integration of interoperable clinical and molecular data systems. In Asia Pacific, increasing pharmaceutical research initiatives, large patient populations, supportive government policies, and partnerships between biotech firms and academic institutions are contributing to notable market momentum in countries such as China, India, Japan, and South Korea. Meanwhile, Latin America, the Middle East, and Africa are gradually strengthening their market presence through enhanced clinical research infrastructure, improved access to molecular and clinical datasets, and growing awareness of AI-enabled drug repurposing solutions.
United States Healthcare Virtual Assistant Market - Country Insights
The United States Artificial Intelligence in Drug Repurposing market is advancing steadily, fueled by a strong pharmaceutical and biotech infrastructure and the growing adoption of AI-powered drug discovery platforms. Research institutions, biotech firms, and pharmaceutical companies are increasingly using AI to uncover new therapeutic applications for existing drugs, optimize R&D workflows, and accelerate clinical development timelines. Engagement from leading technology providers, along with regulatory support for AI-driven drug development, is further promoting market growth. Moreover, the emphasis on improving efficiency, reducing development costs, and bringing therapies to market faster is accelerating the uptake of AI-enabled drug repurposing solutions across the U.S. pharmaceutical sector.
According to a UK Government announcement on advancing AI-driven drug discovery, the UK aims to support the OpenBind consortium in generating over 500,000 experimental protein-ligand complex structures and affinity measurements over five years, marking a 20-fold increase compared with all public data produced over the past 50 years. This effort is backed by government funding and strategic collaborations to expand high-quality biomedical datasets and accelerate AI-enabled drug research and development across the country.
The Artificial Intelligence in Drug Repurposing market is moderately concentrated, with a number of leading pharmaceutical, biotech, and AI-focused technology companies driving competition. Prominent players include IBM Watson Health, Exscientia, Insilico Medicine, Healx, BenevolentAI, Recursion Pharmaceuticals, Atomwise, Evotec, BioAge Labs, Cyclica, Schrodinger, and In Silico Trials. These companies compete by providing AI-powered platforms that identify new therapeutic applications for existing drugs, streamline R&D processes, predict drug-target interactions, and accelerate clinical development timelines. They differentiate themselves through capabilities such as deep learning-driven drug discovery, large-scale biomedical data analytics, integration with research databases, and cloud-based computational platforms, enabling faster, more efficient, and cost-effective drug repurposing.For example, in 2023, Exscientia entered a multi-target collaboration with Merck KGaA, leveraging its AI-powered drug discovery platform to accelerate the design of novel therapeutics, with an upfront payment of USD 20 million and potential milestone payments exceeding USD 670 million. By 2024, Exscientia further expanded its platform capabilities through a partnership with Amazon Web Services (AWS), enabling cloud-based computational drug design and large-scale data analytics. The market is driven by rising demand for efficient, data-driven, and AI-enabled drug repurposing solutions, particularly in oncology and rare diseases. Additionally, companies are enhancing their platforms with predictive modeling, deep learning algorithms, and cloud-based deployments to meet the growing need for faster, cost-effective, and precise identification of new therapeutic applications for existing drugs.
In November 2024, Recursion Pharmaceuticals completed a business combination with Exscientia, creating a unified AI-powered drug discovery platform that integrates both companies' technologies and pipelines to advance drug discovery and development with enhanced computational capabilities. This combined entity features more than 10 clinical and preclinical programs and aims to reduce discovery timelines and costs through iterative AI-driven research loops.
In July 2024, Insilico Medicine announced a collaboration with Inimmune to leverage its proprietary AI platform, Chemistry42, to accelerate the discovery and development of future immunotherapeutics, marking a strategic expansion of its AI-based drug design capabilities into novel therapeutic areas and reinforcing the role of AI in identifying and optimizing drug candidates.