The global AI in drug discovery market size was valued at USD 19.89 billion in 2025 and is predicted to hit around USD 160.49 billion by 2035, rising at a 23.22% CAGR, a study published by Towards Healthcare a sister firm of Precedence Research.
Ottawa, April 22, 2026 (GLOBE NEWSWIRE) — The global AI in drug discovery market size is calculated at USD 24.51 billion in 2026 and is expected to reach around USD 160.49 billion by 2035, growing at a CAGR of 23.22% for the forecasted period.
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AI revolutionizes pharmaceutical processes through process automation, predictive maintenance, and quality control. AI is used in supply chain management to predict demand, optimize logistics, and manage inventory effectively. Researchers focus on improving the results of patients by using AI, which further helps to analyze the genetic and clinical data and tailor treatments to patients. It results in increased efficacy and reduced adverse effects. AI has a great influence on the entire pharmaceutical industry, including core areas of expertise such as R&D, manufacturing, clinical trials, quality control, quality assurance, regulatory affairs, and marketing.
The AI in Drug Discovery Market: Highlights
- The AI in drug discovery market will likely exceed USD 24.51 billion by 2026.
- Valuation is projected to hit USD 160.49 billion by 2035.
- Estimated to grow at a CAGR of 23.22% starting from 2026 to 2035.
- North America held a major revenue share by 56.94% of the market in 2025.
- Asia-Pacific is expected to witness the fastest growth during the predicted timeframe.
- By type, the preclinical and clinical testing segment held a dominant position in the market by 46% in 2025.
- By type, the de novo drug design and drug optimization segment is expected to grow with the highest CAGR in the market during the studied years.
- By application, the oncology segment held the largest revenue share by 48% of the market in 2025.
- By technology, the artificial intelligence segment led the market share by 60% in 2025, and is expected to expand rapidly in the market in the coming years.
- By end-user, the pharmaceutical and biotechnology companies segment accounted for the highest revenue share by 45% of the market in 2025.
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Market Overview
AI Becoming an Element of BioPharmaceutical R&D
The AI in drug discovery market is transforming through various stages, right from proof of concept project, data integration and quality, workflow automation, scaling models, regulatory alignment driven by FDA and EMA guidelines for AI, operational maturity, system reliability and auditability, platform strategies, and long-term partnerships. AI is emerging as a pivotal technology contributing to all fields through experimentation, pilots, integration, compliance, and scalable infrastructure. AI-driven innovations in pharmaceuticals are optimizing drug discovery and industry operations.
However, AI in personalized medicine delivers tailored treatments, AI in pharmacovigilance impacts real-time detection of antibiotic drug resistance and safety monitoring, and AI in drug discovery boosts discovery and predicts drug interactions. Moreover, AI in clinical trials optimizes recruitment, design, and monitoring, while AI in manufacturing and supply chain results in smart factory and inventory management. AI-based technologies like machine learning, deep learning, and natural language processing are applied to address the complex challenges in pharmaceuticals.
AI Applications in Drug Discovery
The various AI models or methods used in drug discovery are machine learning, deep learning, and molecular dynamics. Machine learning has wide applications in screening chemical compounds, deep learning lies in predicting drug-target interactions, and molecular dynamics contributes to simulating drug efficacy in silico. Machine learning accelerated candidate identification, deep learning contributed to accurate protein structure predictions, and molecular dynamics led to faster identification of drug candidates.
Similarly, AI models are used for the prediction of drug-target interactions, for which deep learning models like generative adversarial networks (GANs) and neural networks are gaining popularity. They are used to learn large chemical and biological databases to further identify potential drug candidates. Furthermore, AI has wide applications in molecular dynamics simulations, chemical screening, and structure-activity relationship modelling. AI contributes to patient recruitment, trial design, optimization, and real-time monitoring. AI tools or methods used for this purpose are predictive analytics, electronic health record (EHR) analysis, in silico simulation, machine learning models, natural language processing, and image recognition.
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AI in Chemical Screening and Structure–Activity Relationship Modelling
Machine learning algorithms are used to quickly sieve through vast libraries of chemicals and research for the most promising compounds. Some of the potential algorithms applied in the chemical screening process are SVM, Random Forest, and neural networks. The SVM algorithm potentially classifies molecules based on their structural features, which assists in predicting biological activity.
Consequently, AI is also widely used in structure–activity relationship modelling, where it automatically works by scanning patterns on already existing data that gives predictions on new compounds. AI models assist research scientists in identifying the best candidates for drug development, which saves time and other resources.
The AI in Drug Discovery Market: Regional Analysis
| Region | Shares 2025 (%) | |
| North America | 56.94 | % |
| Europe | 22.03 | % |
| Asia Pacific | 14.19 | % |
| Latin America | 4.88 | % |
| Middle East and Africa | 1.96 | % |
North America dominated the market share by 56.94% in 2025, owing to its highly advanced healthcare IT infrastructure, huge adoption of AI in drug discovery, and strategic pharmaceutical partnerships to adopt computational tools into their core R&D workflows. In March 2026, Conscience, a Canadian non-profit organization dedicated to enabling drug discovery through collaboration, announced the launch of an AI-driven medicines program to drive transformative research and support computational approaches in drug discovery and development.
U.S. Market Analysis
The AI in drug discovery market in the U.S. is revolutionizing due to regulatory and infrastructure advancements, high prevalence of cancer, and the increasing use of AI to identify patients with rare diseases and design precision therapeutics. In April 2026, Amazon launched an AI research tool to accelerate early-stage drug discovery. It will allow researchers to run complex computational workflows without writing code.
Asia Pacific is expected to grow at the fastest rate in the market during the forecast period due to national strategic initiatives, vast patient populations, increasing clinical trial activities, and expanding infrastructure. The integration of AI into the drug discovery process aims to reduce development timelines from years to months. The regional government programs aim to fund supercomputing hubs, establish national AI roadmaps, and create regulatory frameworks for AI-designed compounds.
India Market Trends
The AI in drug discovery market in India is growing and expanding due to strategic government initiatives such as Biopharma SHAKTI, BioE3 policy, digital public infrastructure, and regulatory modernization. India is strengthening the use of AI to advance healthcare delivery and improve access, affordability, and quality of medical devices. The government data indicates success in telemedicine, disease detection, surveillance, and nutrition outcomes.
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How Different Organizations Are Embracing AI in Drug Discovery (2023)
The table shows that AI-first biotechs lead AI adoption in drug discovery, with all of them using AI either substantially or extensively. Academia and general industry show moderate adoption, but a significant share still uses little to no AI. Pharmaceutical and biotech companies remain the most cautious, with the highest percentage reporting no AI adoption. Overall, the data highlights a clear gap: organizations built around AI embrace it deeply, while traditional sectors continue to adopt it slowly.
The AI in Drug Discovery Market: Segmentation Analysis
Type Insights
| Segments By Type | 2025 Shares (%) | |
| Preclinical and Clinical Testing | 46 | % |
| Molecule Screening | 9 | % |
| Target Identification | 15 | % |
| De Novo Drug Design and Drug Optimization | 30 | % |
The preclinical and clinical testing segment dominated the market share by 46% in 2025, owing to a shift towards predictive toxicology, digital twins, and organ-on-chip. AI-enabled workflows reduce the development timeline for a preclinical candidate to 13 to 18 months from 3 to 5 years. Deep learning tools predict absorption, distribution, metabolism, and excretion properties.
The de novo drug design and drug optimization segment is expected to grow at the fastest rate in the AI in drug discovery market during the forecast period due to the importance of AI-driven de novo design in reducing the time to develop preclinical candidates from 3-4 years to 13-18 months. Optimization is shifting towards personalized dosing and precision medicine, and is increasingly performed in closed-loop autonomous laboratories. Generative AI models are widely used to identify novel therapeutic models.
Application Insights
| Segments by Application | 2025 Shares (%) | |
| Oncology | 48 | % |
| Infectious Disease | 27 | % |
| Neurology | 15 | % |
| Others | 10 | % |
The oncology segment led the AI in drug discovery market share by 48% in 2025, owing to the integration of AI into oncology drug discovery, which is redefining the traditional process by boosting drug discovery, optimizing drug efficacy, and reducing toxicity. AI has enabled revolutionary improvements in simulation techniques, molecular modelling, and the identification of novel compounds, including antibodies and anti-tumor components. AI-assisted clinical trial designs have reduced the time and cost of trials and have optimized the admissions of patients.
Technology Insights
| Segments By Technology | 2025 Shares (%) | |
| Machine Learning | 60 | % |
| Other Technologies | 40 | % |
The artificial intelligence/ Machine Learning segment dominated the AI in drug discovery market share by 60% in 2025, and the segment is expected to expand rapidly in the market in the coming years due to the increased efforts of researchers to develop medicines. AI has wide applications in the drug development pipeline to deliver pharmaceuticals of promising therapeutic effects. AI is used to boost and optimize drug discovery and improve anti-tumor agents.
End-User Insights
| Segments By Technology | 2025 Shares (%) | |
| Pharmaceutical and Biotechnology Companies | 45 | % |
| Contract Research and Organizations | 25 | % |
| Academics and Research | 30 | % |
The pharmaceutical and biotechnology companies segment dominated the AI in drug discovery market share by 45% in 2025, owing to the rapid shift of companies from isolated AI pilots to production-ready systems. Large pharma companies are prioritizing long-term platform approaches, which provide partners access to datasets and AI models. Startups are standardizing end-to-end services from target identification to clinical trial design.
The AI in Drug Discovery Market Companies
- BenevolentAI
- BioSymetrics, Inc.
- IBM
- Exscientia
- Insilico Medicine
- GNS Healthcare
- Atomwise Inc.
- insitro
- Berg Health
- CYCLICA
How Big Pharma Ranks in AI Readiness as of 2023?
The image highlights how prepared major pharmaceutical companies are to adopt and integrate artificial intelligence into their operations as of 2023. Roche leads the list with the highest AI readiness score of 77.5, showing that it has invested heavily in digital transformation and advanced analytics. Bayer and Johnson & Johnson follow closely, with scores of 70.2 and 67.4, indicating that these companies also prioritize AI-driven innovation in research, development, and operations.
Novartis, Sanofi, and AstraZeneca sit in the middle of the ranking with scores ranging from 58 to 61, reflecting strong AI adoption but still room for deeper integration. Pfizer also remains close to this cluster with a score of 57.7, showing consistent progress in modernizing its data and AI capabilities.
Companies like GSK, Moderna, and Bristol Myers Squibb fall into the 50-point range, meaning they have adopted AI but may still be building the necessary infrastructure, talent, or strategy to reach higher maturity levels.
Further down the list, Eli Lilly, Merck & Co., Novo Nordisk, and AbbVie show comparatively lower readiness, with scores between 36 and 48. These scores suggest that although they have started adopting AI, they may still face challenges such as slower digital transformation, limited data integration, or more cautious investment approaches.
Overall, the table shows a clear gap between the early leaders who aggressively pursue AI innovations and those who are still developing foundational capabilities. This spread also reflects how differently each company prioritizes AI as a strategic driver for competitiveness, efficiency, and scientific advancement.
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Segments Covered in the Report
By Type
- Preclinical and Clinical Testing
- Molecule Screening
- Target Identification
- De Novo Drug Design and Drug Optimization
By Application
- Neurology
- Infectious Disease
- Oncology
- Others
By Technology
- Machine Learning
- Other Technologies
By End-User
- Pharmaceutical and Biotechnology Companies
- Contract Research Organizations
- Academics and Research
By Region
- North America
- Asia Pacific
- Europe
- Latin America
- Middle East and Africa (MEA)
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Towards Healthcare is a leading global provider of technological solutions, clinical research services, and advanced analytics, with a strong emphasis on life science research. Dedicated to advancing innovation in the life sciences sector, we build strategic partnerships that generate actionable insights and transformative breakthroughs. As a global strategy consulting firm, we empower life science leaders to gain a competitive edge, drive research excellence, and accelerate sustainable growth.
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