The AI in drug discovery market was valued at USD 2.29 billion in 2025 and is expected to reach USD 2.85 billion in 2026 and USD 16.77 billion by 2034, expanding at a CAGR of 24.78% over the forecast period 2026–2034. Leading companies include Recursion Pharmaceuticals, Atomwise Inc., Insilico Medicine, Isomorphic Labs, Schrödinger, Owkin, XtalPi, insitro, BenevolentAI, Aitia, BioSymetrics, and BPGbio. Rising pharmaceutical R&D costs, pipeline attrition, and growing collaboration between pharma companies and AI providers are the main demand drivers.

Oncology Leads Therapeutic Demand

By therapeutic area, oncology held the largest share of 29.0% in 2025, reflecting the high prevalence of cancer and the need for new medicines. AI technologies are used in oncology to find therapeutic targets, perform screening and biological data analysis, and create targeted therapies. Other therapeutic areas covered include neurodegenerative diseases, cardiovascular disease, metabolic diseases, and infectious disease. Chronic disease burden supports demand, with the report citing CDC data showing more than 40 million people in the U.S. have diabetes.

Preclinical Testing Accelerates

The preclinical testing segment is projected to grow at a CAGR of 26.6% during 2026–2034. AI enables scientists to assess biological and chemical information, evaluate drug safety and efficacy, and choose promising candidates before clinical trials. Demand to reduce cost, time, and risk at the early research stage contributes to adoption in preclinical testing, while drug optimization and repurposing forms the other application segment tracked in the report.

Personalized Medicine and Biomarker Discovery

Integration of AI with genomics, proteomics, and other omics technologies is driving demand for personalized medicine. AI algorithms analyze complex omics data to identify biomarkers and customize treatments to individual genetic profiles, which enhances therapeutic efficacy. Biomarker discovery appears among emerging technology trends, where AI identifies disease biomarkers from genomic, proteomic, and clinical data to enable precision medicine and targeted therapies, supporting personalized medicine development.

Drug Repurposing and Emerging Use Cases

In drug repurposing, AI evaluates existing medicines to identify new therapeutic applications, reducing development time, cost, and clinical risk. Other use cases include rare disease treatment development and vaccine research. Emerging technology trends include generative AI for molecule creation, digital twins, quantum computing in pharmaceutical research, federated learning for healthcare data security, and automated robotic laboratories integrated with AI.

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End-User and Regional Dynamics in the AI in Drug Discovery Market

The pharmaceutical and biotechnology companies segment is expected to register the highest end-user CAGR of 25.60%, driven by rising R&D investment, and the report notes that Pfizer has embraced AI to accelerate clinical drug development. Growing adoption among small and mid-sized biotech firms is flagged as an opportunity, since AI suppliers can help them screen drugs without large in-house technology teams. North America held a 38.0% share in 2025, and Asia Pacific is projected to post a 27.40% CAGR during 2026–2034.

Competitive Landscape and Recent Deals

Recursion Pharmaceuticals is described as a biotech company that uses advanced technology to decode biology and industrialize drug discovery, with several drugs in clinical trials. Atomwise uses its AtomNet technology, based on deep learning, for target identification and drug repositioning. In March 2026, Eli Lilly extended its collaboration with Insilico Medicine to a deal worth up to USD 2.75 billion, and in July 2026 GSK announced a collaboration with Relation Therapeutics.

Outlook Through 2034

The report expects further advances to focus on personalized medicine, laboratory automation using AI, and quantum computing for complex molecular simulations. Collaboration among technology vendors, research institutes, and pharmaceutical firms is expected to rise as regulatory frameworks mature and quality healthcare data becomes available, positioning AI to develop safer, faster, and more effective therapies worldwide.

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