AI in Drug Repurposing Market Size, Share, and Industry Analysis, By Component (Platforms and Services), By Deployment (Cloud-Based, On Premise, and Hybrid), By Technology (Natural Language Processing (NLP), Machine Learning & Deep Learning, and Others), By Type (Drug Repurposing, Drug Repositioning, Drug Rescue, and Combination Therapies), By End User (Pharmaceutical & Biotechnology Companies, Academic & Research Institutes, Contract Research Organizations (CROs), and Others), and Regional Forecast till 2034

The AI in drug repurposing market size was valued at USD 1.36 billion in 2025. The market is projected to grow from USD 1.72 billion in 2026 to USD 10.24 billion by 2034, exhibiting a CAGR of 24.1% during the forecast period.

The global AI in drug repurposing market is expected to expand steadily over the forecast period. The market is driven by the need to reduce R&D timelines and costs while improving the probability of clinical success. As biomedical literature, multi-omics datasets, and real-world patient data continue to scale, the demand for AI platforms that can rapidly mine evidence, map drug target–disease relationships, and prioritize repurposing candidates with stronger clinical rationale increases. Also, increasing collaborations between AI providers and healthcare innovators further reinforces market momentum by converting algorithms into repurposing pipelines.

• For instance, in September 2025, Predictive Oncology and Every Cure announced a strategic collaboration to use Predictive Oncology’s AI platform to identify and prioritize drugs for repurposing, highlighting the rising commercial adoption of AI-driven repurposing workflows, which helped drive market growth.

Furthermore, expanding pipelines, technological advancements, and key mergers and partnerships by major companies strengthen their market position and support the overall market growth.

AI in Drug Repurposing Market Driver

Increasing Collaboration Among AI and Biopharmaceutical Companies to Support Market Growth

Increasing AI and biopharmaceutical collaborations to reduce drug development timelines is one of the key factors driving market growth. They convert AI repurposing into validated, decision-ready programs. Various pharmaceutical and biotechnology companies have compound libraries, biological samples, and clinical context, while AI vendors bring scalable algorithms for hypothesis generation, ranking, and biomarker-led prioritization. A strategic partnership among these entities reduces the time and cost required to move from computational prediction to wet-lab confirmation and early clinical proof-of-concept. These collaborations also overcome the adoption barrier. As a result, partnerships accelerate real deployment, expand the pool of repurposable assets, and create repeatable pipelines that strengthen commercial demand for platforms and services.

• For instance, in February 2026, Merck collaborated with Mayo Clinic to apply artificial intelligence (AI), advanced analytics, and multimodal clinical data to support drug discovery and development. Such developments are driving market growth.

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For instance, in October 2025, the International Journal of Advanced Research in Science, Communication and Technology published an article titled ‘Comparative Analysis of AI-Driven Drug.

Discovery and Drug Repurposing.’ When using AI, the drug development timelines are reduced considerably.

AI in Drug Repurposing Market Restraint

Risk Associated with Cybersecurity and Ransomware to Hamper Market Growth

Data availability and governance issues are key restraints for AI in the drug repurposing market. Repurposed models are highly dependent on data availability and are only as strong as the clinical, molecular, and literature evidence they can learn from and validate. fragmented datasets across geographies and systems, limited permissions to use patient-level data, inconsistent coding and ontology standards, and strict security requirements hamper their efficiency. This slows down model training and validation and reduces confidence in outputs, ultimately delaying adoption for platforms.

• For instance, in February 2025, TriNetX warned in a survey that while RWD and AI are priorities, data compatibility, regulatory hurdles, and security concerns are looming barriers that can stall momentum—directly reflecting the governance and interoperability constraints that are holding back adoption. Such factors hamper growth.

AI in Drug Repurposing Market Opportunity

Expansion into Real-Time Applications by Pharmaceutical Companies Creates a Major Growth Opportunity

Market growth opportunities in AI in drug repurposing are expanding as the industry shifts from one-off academic exercises to repeatable, scalable platforms that can continuously mine new evidence and convert it into prioritized, validation-ready candidates. As pharma and biotech companies look to improve pipeline productivity, there is strong whitespace for solutions that (i) industrialize portfolio-wide repurposing across approved and shelved assets, (ii) integrate real-world and multimodal data to strengthen clinical relevance, and (iii) support faster proof-of-concept execution through cohort discovery, biomarker strategy, and trial design enablement. This creates a clear cause-and-effect pathway: better data integration and scalable platforms increase decision confidence, which drives broader adoption, larger enterprise contracts, and more partnership-led repurposing programs.

• For instance, in September 2025, Eli Lilly launched Lilly TuneLab, an AI/ML platform giving biotechnology companies access to drug discovery models trained on Lilly’s research data.

Segmentation

Key Insights

The report covers the following key insights:

• Technological Advancements in AI in Drug Repurposing
• Key Industry Developments (Strategic Partnerships, Collaborations, Acquisitions, and Mergers)
• New Product Launches, By Key Players
• Key Start Ups, By Key Regions

Analysis by Component

Based on the component, the market is segmented into platforms and services.

The platform segment is anticipated to hold a leading market share. The high share is expected due to the repeatable, scalable applications of these platforms for repurposing across many assets. These platforms consolidate data ingestion, hypothesis generation, scoring, and evidence packaging into a single workflow, thereby reducing cycle time and standardizing decisions across therapeutic areas. As pharmaceutical and research groups push for productivity through strategic collaborations, the segment is anticipated ot grow.  

• For instance, in April 2025, BioPhy partnered with Every Cure to unlock the potential of existing medicines to treat every disease and every patient possible. The partnership focused on the simulation and optimization of clinical trials and their endpoints, aiming to maximize trial success rates and efficient use of capital. Such developments are expected to lead to segmental growth.

Analysis by Deployment

Based on deployment, the market is segmented into cloud-based, on-premise, and hybrid.

The cloud-based segment is anticipated to hold a leading market share. The high share is attributed to the segment as AI repurposing workloads require elastic compute and fast iteration across large, frequently updated datasets. Cloud delivery also improves cross-site collaboration, speeds model updates, and lowers upfront infrastructure burden. These factors make it easier for organizations to scale from pilots to enterprise programs. Key companies launch new products to expand their offerings in the segment. 

• For instance, in January 2023, Revvity, Inc., launched the Signals Research Suite. This unified, cloud-native SaaS platform drives scientific collaboration across R&D disciplines from drug discovery to specialty chemical material development. Such developments are expected to lead to segmental growth.

Analysis by Technology

By technology, the market is divided into natural language processing (NLP), machine learning & deep learning, and others.

The machine learning & deep learning segment is anticipated to dominate the market as repurposing needs pattern recognition across heterogeneous signals such as text, structured biology, networks, and patient-level data. These models can improve ranking precision as new data arrives, and support continuous learning loops as validation results feed back into the system. The technology prioritizes candidates and predicts outcomes. Many key companies are focusing on strategic partnerships to capitalize on market growth potential.

• For instance, in May 2024, Sanofi, Formation Bio, and OpenAI collaborated to build AI-powered software to accelerate drug development and bring new medicines to patients more efficiently. Such developments are expected to drive the segmental growth.

Analysis by Type

By type, the market is divided into drug repurposing, drug repositioning, drug rescue, and combination therapies.

Drug repurposing is estimated to dominate as it is most commonly used commercially. Finding new indications for existing drugs with clearer ROI and faster proof-of-concept potential. Many end users start here to quickly demonstrate value, as approved/known compounds reduce early uncertainty, and positive signals can translate into faster clinical validation and partnering conversations.

• For instance, in August 2024, SAS partnered with Rejuvenate Biomed to build a drug repurposing discovery tool on SAS Viya, designed to help researchers systematically uncover drug–disease insights. Such developments are expected to drive the segmental growth.

Analysis by End User

By end user, the market is divided into pharmaceutical & biotechnology companies, academic & research institutes, contract research organizations (CROs), and others.

The pharmaceutical and biotechnology companies segment is estimated to dominate the market. The dominance of the segment is attributed to high spend as they control the largest asset portfolios with the strongest commercial incentive. They also have the validation infrastructure and clinical development operations that move AI outputs into experiments and trials. Such factors directly increase their investments and reinforce market growth.   

• For instance, in September 2023, Merck collaborated with BenevolentAI and Exscientia to drive accelerated drug discovery with a higher probability of success. The partnership aimed to generate novel development candidates in oncology, neurology, and immunology. Such developments are expected to drive the segmental growth.

Regional Analysis

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By region, the market is categorized into Europe, North America, Asia Pacific, Latin America, and the Middle East & Africa.

North America accounted for approximately 45.0% of the AI market for drug repurposing in 2025. The region is anticipated to grow due to large budgets and expanding pipelines. Large biopharmaceutical budgets with deep availability of computational talent make it easier to operationalize AI. The mature partnering ecosystem between AI vendors and biopharmaceutical companies supports the market's growth.

• For instance, in September 2025, Predictive Oncology Inc. collaborated with Every Cure to identify and prioritize drugs for repurposing and improve patient outcomes. Such developments drive the market growth in the region.

Europe is expected to grow at a significant CAGR during the forecast period. The region's growth is supported by strong public–private research networks and collaborative models that help overcome fragmented datasets and cross-border adoption barriers. Pan-European consortia and structured programs also help standardize evidence-generation and validation pathways, increasing confidence in repurposing decisions and driving platform uptake. Also, increasing investment in the region and growing government budgets reinforce the region’s growth potential.

• For example, in April 2025, LifeArc collaborated with the EU-funded REMEDi4ALL project to accelerate and transform the repurposing of medicines in Europe. This agreement reflected the growing interest in drug repositioning and repurposing as a faster, more cost-effective strategy to complement de novo drug development. Such developments support regional growth.

Asia Pacific is expected to grow at a stable CAGR during the forecast period. Increasingly, key companies are using AI to accelerate discovery and maximize output from growing pipelines, driving regional growth. Cross-border deals linking Asia Pacific AI capabilities with global development and commercialization further amplify growth.

Strategic collaborations among key companies to advance their research capabilities support regional growth.

• For instance, in June 2025, AstraZeneca collaborated with Shijiazhuang City-based CSPC Pharmaceuticals Group Limited. The collaboration aimed to advance the discovery and development of novel oral candidates, with the potential to treat diseases across multiple indications. Such developments are driving the region's growth.

Key Players covered

The AI in drug repurposing market is consolidated, with a few players capturing significant market share. The report includes the profiles of the following key players.

• BenevolentAI (U.K.)
• Recursion Pharmaceuticals, Inc. (U.S.)
• HEALX LIMITED(U.K.)
• Atomwise Inc. (U.S.)
• InSilico Medicine Canada Inc. (Canada)
• Certara, Inc. (U.S.)
• TriNetX, LLC (U.S.)
• Aetion (U.S.)
• Schrödinger, L.L.C. (U.S.)

Key Industry Developments

• April 2025: Sonrai Analytics collaborated with Paige to better enable biopharma researchers, pathologists, and clinicians to harness cutting-edge AI capabilities for precision medicine research and development. The collaboration helped to bring a set of solutions to accelerate drug discovery, biomarker identification, targeted therapeutics, and precision medicine applications.
• January 2025: Every Cure collaborated with Google Cloud to advance AI-driven drug repurposing by leveraging Google Cloud's AI tools, including the Gemini 2.0 large language model, to power the company’s MATRIX platform. The collaboration aimed to accelerate the discovery, validation, and global dissemination of treatments for diseases lacking effective therapies.