AI in Drug Repurposing Market Size & Growth Forecast 2027–2036, By Segments (Component, End Use, Technology, Application), Regional Demand Trends (North America, Asia Pacific, Europe), Key Country Insights (U.S., Japan, South Korea, Germany, France, Italy), and Competitive Landscape

Component Segment Analysis: Software & Platforms (Largest & Fastest-Growing Segment)

Software & platforms accounted for the largest share of 63.97% of the AI in drug repurposing market in 2026 by component and are also expected to register the fastest growth, driven by the increasing need for scalable computational tools that can accelerate drug discovery and identify new therapeutic applications for existing compounds. These platforms integrate machine learning, data analytics, molecular databases, and predictive modeling capabilities to evaluate complex relationships among drugs, diseases, biological targets, and clinical data. Their ability to consolidate diverse datasets and automate computationally intensive screening processes supports faster hypothesis generation and more efficient prioritization of repurposing candidates. Growing adoption of cloud-based platforms, improvements in data interoperability, and the expansion of biomedical datasets are further strengthening the role of software solutions across research workflows. In addition, pharmaceutical organizations and research institutions are increasingly incorporating AI-enabled platforms into existing discovery infrastructure, supporting continued demand for solutions that can improve research productivity, reduce manual analysis, and facilitate evidence-based drug repurposing decisions.

End Use Segment Analysis: Pharmaceutical & Biotechnology Companies (Largest & Fastest-Growing Segment)

Pharmaceutical & biotechnology companies represented the largest share of 60.97% of the AI in drug repurposing market by end use in 2026 and are also expected to experience the fastest growth, supported by the sector's increasing focus on shortening drug development timelines and extracting greater value from existing therapeutic assets. These organizations are deploying AI-driven approaches to analyze large-scale clinical, molecular, genomic, and real-world datasets to uncover previously unrecognized relationships between approved or investigational drugs and disease indications. AI-based repurposing can complement conventional research by enabling systematic screening of established compounds and helping researchers prioritize candidates for further laboratory and clinical investigation. The growing availability of structured biomedical data, advances in computational biology, and increasing integration of AI into pharmaceutical R&D workflows are expanding the practical applications of these technologies. Biotechnology companies are also leveraging AI platforms to explore new therapeutic hypotheses with greater computational efficiency, while pharmaceutical companies are using these capabilities to support portfolio optimization and identify additional opportunities across existing drug pipelines.

Technology Segment Analysis: Machine Learning/Deep Learning (Largest Segment) vs Generative AI & Large Language Models (LLMs) (Fastest-Growing Segment)

Machine Learning/Deep learning accounted for the largest share of 48.52% of the AI in drug repurposing market by technology in 2026, reflecting their established application in analyzing complex biomedical datasets and identifying relationships relevant to therapeutic repositioning. These technologies can process high-dimensional molecular, genomic, clinical, and disease-related information to recognize patterns that may not be readily identifiable through conventional analytical approaches. Machine learning models support drug-target interaction prediction, disease association analysis, compound screening, and candidate prioritization, while deep learning can handle increasingly complex datasets and nonlinear relationships across multiple biological variables. Their established integration into AI-driven drug discovery workflows, combined with expanding computational resources and growing access to biomedical data, continues to reinforce their role in drug repurposing applications.

Generative AI & large language models (LLMs) are expected to represent the fastest-growing technology segment in the AI in drug repurposing market, driven by their ability to interpret and synthesize extensive volumes of unstructured biomedical information. These technologies can assist researchers in extracting insights from scientific literature, clinical documentation, research publications, and other text-heavy sources while supporting the generation of new therapeutic hypotheses. Their ability to connect information across disparate knowledge domains can help identify potential relationships among diseases, drugs, biological mechanisms, and treatment outcomes that warrant further investigation. Increasing integration of generative AI with specialized biomedical databases and analytical platforms is expanding its utility beyond conventional text processing toward research support and knowledge discovery. As organizations seek more interactive and efficient approaches to navigating complex scientific information, the adoption of generative AI and LLM-based tools is expected to accelerate across drug repurposing research workflows.