The global AI in drug repurposing market was valued at USD 1.01 billion in 2024 and is forecasted to grow at a CAGR of 20.1% through 2034, as artificial intelligence transforms every stage of drug discovery and repositioning. The market’s segmentation structure is multi-dimensional, spanning applications, end-users, and technology types that collectively define its growth trajectory. AI-enabled systems for target identification, molecular screening, and pharmacovigilance are driving the most significant growth, while disease-specific applications—particularly in oncology, neurodegenerative disorders, and cardiovascular diseases—are expanding rapidly. Hospitals, biopharmaceutical companies, and academic institutions remain the key end-users, leveraging AI-driven analytics to optimize clinical pipelines and repurpose existing molecules for unmet therapeutic needs.

AI algorithms are enabling product differentiation in a market historically constrained by high R&D costs and long regulatory timelines. Machine learning (ML) and deep learning (DL) platforms now allow predictive modeling of molecular behavior, facilitating faster repurposing decisions with higher clinical success probabilities. Natural language processing (NLP) systems are increasingly used to analyze unstructured biomedical literature, improving value chain optimization through knowledge extraction. From a technological standpoint, supervised learning models dominate the market, though reinforcement learning and graph neural networks are gaining traction due to their efficiency in mapping protein-ligand interactions.

Segment-wise performance reveals the dominance of oncology applications, accounting for the largest share of global revenue, as AI tools identify secondary uses for anti-tumor compounds. Neurological applications are expected to witness the highest growth, supported by AI’s capability to interpret multi-omic datasets for diseases like Alzheimer’s and Parkinson’s. Meanwhile, cardiovascular and metabolic disease segments are expanding due to a surge in public-private research collaborations. The biopharmaceutical sector continues to hold the highest adoption rate, integrating AI tools into both preclinical and post-market surveillance phases to enhance value chain visibility.

Pricing structures across segments are being redefined by AI-driven efficiencies. Traditionally, drug discovery costs exceeded USD 2 billion per molecule; AI-enabled repurposing now reduces this by up to 50%, according to data from the U.S. FDA and OECD. Such cost optimization enhances affordability and encourages reinvestment into experimental therapies. However, the market still faces challenges around algorithmic interpretability and integration with legacy clinical systems. The lack of harmonized data standards across institutions also restricts the full potential of AI across the value chain.

Opportunities are intensifying around the deployment of hybrid AI systems combining biological databases, clinical trial repositories, and real-world data. These systems facilitate cross-validation of repurposing hypotheses, improving the likelihood of regulatory success. A growing trend in open-access data collaboration—seen in initiatives like Open Targets and NIH’s AI-based translational research projects—is expanding the evidence base for machine learning algorithms. This trend supports more transparent and application-specific growth across industry verticals.

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As R&D models shift toward automation, cloud-based AI platforms are enabling greater scalability and global collaboration. Pharmaceutical firms increasingly partner with technology providers to build modular AI ecosystems that support continuous data integration, ensuring segment-wise performance remains measurable and adaptable. The convergence of AI and bioinformatics has also introduced a new level of customization in drug repurposing workflows, further optimizing product pipelines.

With a balance of rapid innovation, evolving regulations, and rising demand for cost-effective therapeutics, the AI in drug repurposing market is transitioning toward long-term sustainability. Segmentation-based growth will remain critical for investors and developers aiming to align strategic priorities with measurable clinical and commercial outcomes.

Key Players:
• BenevolentAI
• Insilico Medicine
• Exscientia
• Atomwise
• Recursion Pharmaceuticals

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