Artificial intelligence is transforming pharmaceutical research by enabling researchers to identify new therapeutic applications for existing medicines more efficiently. AI in Drug Discovery is helping pharmaceutical and biotechnology companies analyze large volumes of biological, chemical, genomic, and clinical information to identify potential drug-disease relationships. Drug Repurposing Using AI has become particularly important because it can help researchers evaluate existing drugs for new indications while leveraging previously available safety and clinical information. Advances in machine learning, natural language processing, knowledge graphs, and generative AI are supporting the development of advanced Drug Repurposing Software and accelerating the search for potential therapies.

As per Grand View Research, the global artificial intelligence in drug repurposing market size was valued at USD 1.3 billion in 2025 and is projected to grow from USD 1.7 billion in 2026 to USD 7.7 billion by 2033, at a CAGR of 24.5% from 2026 to 2033. North America dominated the market with a revenue share of 52.9% in 2025. The software and platform segment accounted for the largest share, while machine learning and deep learning represented the leading technology segment. Oncology was the largest application area, and pharmaceutical and biotechnology companies represented the largest end-use segment.

The growing demand for cost-effective drug development is one of the major factors supporting market expansion. Traditional drug discovery can require extensive time, investment, and clinical development resources. AI-driven repurposing can analyze existing compounds and datasets to identify potential new therapeutic uses, helping researchers prioritize promising candidates and reduce the time required for early-stage discovery.

AI in Drug Discovery

AI is becoming an important technology across the broader drug discovery process. Machine learning models can analyze molecular structures, biological targets, disease pathways, genomic profiles, and clinical information to identify relationships that may be difficult to discover through conventional approaches.

The drug optimization and repurposing segment accounted for more than half of the artificial intelligence in drug discovery market in 2025, according to Grand View Research. AI can support target identification, compound screening, toxicity prediction, and therapeutic response analysis. These capabilities are helping researchers improve the efficiency of early drug development while supporting precision medicine initiatives.

Drug Repurposing Using AI

Drug Repurposing Using AI is gaining momentum because existing drugs can provide valuable information about safety, pharmacology, dosage, and clinical behavior. AI systems can analyze this information alongside disease biology and patient data to identify potential new indications.

Machine learning and deep learning models can identify hidden relationships between drugs, proteins, diseases, and biological pathways. Knowledge graphs can connect information from multiple sources, allowing researchers to explore complex drug-disease associations. AI can also evaluate combinations of drugs and identify potential synergistic effects.

These capabilities are particularly valuable in areas such as oncology, rare diseases, infectious diseases, neurological disorders, and cardiovascular conditions. Grand View Research identifies oncology as the largest application segment, while infectious diseases are expected to experience strong growth during the forecast period.

Drug Repurposing Software

The increasing availability of Drug Repurposing Software is making AI-based analysis more accessible to pharmaceutical companies, research institutions, and biotechnology organizations. These platforms can integrate molecular databases, clinical trial information, biomedical publications, genomic datasets, and real-world patient information.

Software platforms can automate data processing, candidate prioritization, drug-target prediction, and disease association analysis. They can also provide researchers with visual networks and ranked candidate lists that support further laboratory and clinical validation.

The software and platform segment accounted for 66.6% of the AI in drug repurposing market in 2025, highlighting the growing importance of technology platforms in computational drug development.

AI Foundation Models for Drug Discovery

One of the latest trends is the development of AI Foundation Models for Drug Discovery. These models are trained using large and diverse datasets and can potentially support multiple pharmaceutical research tasks.

Foundation models can learn relationships between molecular structures, proteins, diseases, biological pathways, and clinical information. Researchers can then adapt these models for drug-target prediction, compound screening, biomarker discovery, and repurposing research.

The development of foundation models can reduce the need to create separate AI systems for every research application. This approach is expected to contribute to more scalable and flexible AI workflows across pharmaceutical research.

AI Analysis of Biomedical Literature and Clinical Data

Another important trend is AI Analysis of Biomedical Literature and Clinical Data. Pharmaceutical researchers need to evaluate enormous amounts of scientific literature, clinical trial records, electronic health information, and research publications. Manually reviewing these sources can be time-consuming.

Natural language processing and large language models can rapidly analyze scientific publications and clinical information to identify relationships between drugs, diseases, genes, proteins, and treatment outcomes. Generative AI and large language models are expected to be among the fastest-growing technology areas in AI-driven drug repurposing because they can accelerate hypothesis generation and information extraction.

The increasing pressure on pharmaceutical companies to control development costs is also making AI-based approaches more attractive. The broader AI in drug discovery market is projected to reach USD 13.8 billion by 2033, demonstrating the expanding role of AI across pharmaceutical research and development.

AI-Powered Drug-Target Prediction

AI-Powered Drug-Target Prediction is another major area of innovation. Identifying suitable biological targets is a critical stage of drug development. AI models can analyze molecular and biological datasets to predict interactions between existing drugs and potential targets.

By combining genomic, proteomic, transcriptomic, and other biological information, AI systems can identify previously overlooked drug-target relationships. These insights can help researchers discover new therapeutic opportunities and prioritize candidates for laboratory validation.

AI-driven target prediction can also support the identification of polypharmacology opportunities, where a single drug may influence multiple biological targets. This can provide new possibilities for treating complex diseases and developing combination therapies.

Impact of Pharmaceutical Market Changes

The pharmaceutical industry is also facing changing economic and supply-chain conditions, making efficient drug development increasingly important. Discussions around tariffs and pharmaceutical imports have highlighted concerns about drug costs, manufacturing dependencies, and global access to medicines. These pressures can encourage pharmaceutical companies to explore more efficient approaches to research, development, manufacturing, and therapeutic optimization. The Global Industry Herald article “U.S. Tariff Shockwaves: 100% Import Duty on Pharma Drugs Raises Global Concerns” examines these concerns and their potential implications for the pharmaceutical industry. U.S. Tariff Shockwaves: 100% Import Duty on Pharma Drugs Raises Global Concerns

North America currently leads the AI in drug repurposing market because of strong pharmaceutical and biotechnology industries, significant research investment, advanced healthcare data infrastructure, and growing adoption of AI technologies. Asia Pacific is expected to experience the fastest growth during the forecast period as pharmaceutical research, healthcare digitization, and AI investments continue to expand.

Looking ahead, AI in drug repurposing is expected to move toward increasingly integrated platforms that combine molecular data, biomedical literature, clinical information, multi-omics datasets, and real-world evidence. AI Foundation Models for Drug Discovery, generative AI, knowledge graphs, multimodal data analysis, and AI-Powered Drug-Target Prediction are likely to remain important areas of innovation.

Overall, the convergence of AI in Drug Discovery, Drug Repurposing Using AI, and advanced Drug Repurposing Software is creating new opportunities for pharmaceutical research. As AI models become more sophisticated and datasets become more interconnected, these technologies can help researchers identify promising therapeutic opportunities faster, prioritize candidates more effectively, and support the development of treatments for diseases with significant unmet medical needs.