AI in Pharmaceutical R & D - Market Share, Industry Trends, Statistics, and Growth Forecasts (2026-2031) | Generative AI and Services Drive 32.25% CAGR to USD 17.66 Billion
Dublin, Nov. 13, 2026 (GLOBE NEWSWIRE) -- "AI In Pharmaceutical R & D - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026-2031)" has been added to ResearchAndMarkets.com's offering.
AI in Pharmaceutical R&D Market to Reach USD 17.66 Billion by 2031
The global AI in pharmaceutical R&D market was valued at USD 3.30 billion in 2025 and is projected to increase from USD 4.36 billion in 2026 to USD 17.66 billion by 2031. The market is expected to register a CAGR of 32.25% during the 2026-2031 forecast period. Growth is being fueled by rising drug development costs, expanding biomedical data volumes, increasing Pharma-AI collaborations, regulatory support, and demand for faster, more efficient research processes.
Rising Biopharma R&D Costs Accelerate AI Adoption
High research costs and lengthy development timelines continue to place pressure on pharmaceutical and biotechnology companies. JAMA estimates that the average cost of bringing a drug to market is USD 1.31 billion after accounting for capital costs and program attrition. Smaller biopharma companies face a 37.6% cost premium compared with the top 20 companies, intensifying the need for technologies that improve productivity and reduce development risk.
AI platforms are helping companies enhance phase-transition success rates, optimize compound selection, and shorten patient trial durations. Insilico Medicine, for example, identified a preclinical candidate in eight months, compared with the traditional 2.5 to 4-year timeframe. Results such as these are strengthening confidence in AI-enabled pharmaceutical R&D and encouraging partnership agreements that include milestones linked to accelerated development timelines.
Biomedical Data Growth Expands Market Opportunities
The rapid expansion of genomics, proteomics, imaging, electronic health records, and molecular data is supporting more advanced AI models. Recursion has gained exclusive access to 20 petabytes of oncology data, increasing its total data holdings to approximately 50 petabytes. IBM Research trains models using more than a billion small molecules and protein sequences, while Xaira's X-Cell uses 4.9 billion parameters derived from 25.6 million single-cell transcriptomes.
Access to larger and more diverse data sets can improve model generalization and support predictions involving previously untested targets and pathways. This trend is also increasing investment in cloud computing, high-performance infrastructure, and specialized data platforms for AI-driven pharmaceutical R&D.
Data Quality and Talent Gaps Remain Key Challenges
Inconsistent annotations, missing metadata, and non-standard identifiers can reduce the reliability and generalizability of AI models. Regulatory authorities increasingly require traceable documentation for data-processing activities and clear disclosure of model limitations. Federated research initiatives are addressing these issues by standardizing ontologies and improving data collaboration across participating organizations.
National programs are also supporting data quality improvements. China's digital-intelligent transformation plan for 2025 includes unified data standards and regional data hubs. However, outdated assays, underrepresented populations, and limited access to AI-skilled life-science professionals remain barriers to broader adoption.
Software Leads the AI in Pharmaceutical R&D Market
Software accounted for 57.34% of the market in 2025. Integrated platforms such as RecursionOS manage workflows extending from target discovery to clinical-trial simulations. The platform processes millions of phenomics images each week and stores 36 petabytes of proprietary data. Exscientia integrates primary human-tissue assays into design workflows to improve the clinical relevance of its research. Strong platform adoption is supporting recurring license revenue and long-term software demand.
Services represent a smaller share but are projected to grow at a CAGR of 32.55% through 2031. Contract Research Organizations are expanding their use of AI-driven patient-recruitment tools and adaptive-trial designs. Demand for outsourced AI capabilities is expected to rise as pharmaceutical sponsors seek specialized expertise and prefer variable operating costs over fixed investments.
Generative Learning Emerges as the Fastest-Growing Technology
Machine learning generated 45.45% of technology revenue in 2025, supporting ADME-Tox prediction, biomarker discovery, and patient stratification. Supervised algorithms remain widely used when high-quality labeled data is available.
Generative learning is forecast to record a CAGR of 32.79% through 2031. AlphaFold 3's diffusion method has improved protein-ligand prediction accuracy by 50%, while Exscientia's sixth AI-generated molecule entered clinical trials in 2023. Transformer and diffusion networks are increasingly being used to design antibodies, RNA therapeutics, and PROTACs, positioning generative technologies as a major growth engine for the AI in pharmaceutical R&D market.
Regional Market Outlook
North America held 48.45% of global revenue in 2025, supported by major biotechnology clusters, early FDA guidance on AI credibility, and substantial private investment. Venture investors directed nearly USD 10 billion into AI drug-discovery deals in 2024, including five transactions valued above USD 1 billion. Recursion's BioHive-2 supercomputer, featuring 504 NVIDIA H100 GPUs, further demonstrates the region's advanced computational capacity.
Europe continues to benefit from regulatory engagement, public-private partnerships, and Horizon Europe funding. Initiatives such as MELLODDY support federated learning while addressing GDPR requirements, and the EMA's reflection paper provides guidance across the pharmaceutical product life cycle.
Asia-Pacific is projected to be the fastest-growing regional market, with a CAGR of 34.00% through 2031. China plans to establish 100 pilot digital drug factories and 10 large-model innovation hubs as part of its digital-intelligent transformation strategy. India provides cost-efficient data-science talent for Contract Research Organizations, while Japan is using precision-medicine grants to modernize clinical infrastructure. Pilot projects in Brazil and the UAE also indicate gradual adoption across South America and the Middle East & Africa.
Key Topics Covered:
1 Introduction
1.1 Study Assumptions & Market Definition
1.2 Scope of the Study
2 Research Methodology
3 Executive Summary
4 Market Landscape
4.1 Market Overview
4.2 Market Drivers
4.2.1 Escalating Biopharma R&D Costs
4.2.2 Expanding Biomedical Data Volumes
4.2.3 Pharma-AI Partnerships & Funding Surge
4.2.4 Regulatory Openness to AI in R&D
4.2.5 Emergence of Foundation Bio-Models
4.2.6 Growth of Federated?Learning Data Corridors
4.3 Market Restraints
4.3.1 Data Quality & Standardization Gaps
4.3.2 Shortage of AI-Skilled Life-Science Talent
4.3.3 IP Ambiguity for AI-Generated Molecules
4.3.4 Rising GPU / Cloud Compute Costs
4.4 Value / Supply-Chain Analysis
4.5 Regulatory Landscape
4.6 Technological Outlook
4.7 Porter's Five Forces Analysis
4.7.1 Threat of New Entrants
4.7.2 Bargaining Power of Suppliers
4.7.3 Bargaining Power of Buyers
4.7.4 Threat of Substitute Products
4.7.5 Competitive Rivalry
5 Market Size & Growth Forecasts (Value, USD)
5.1 By Component
5.1.1 Software
5.1.2 Services
5.1.3 Hardware
5.2 By Technology
5.2.1 Machine Learning
5.2.2 Natural Language Processing
5.2.3 Deep / Generative Learning
5.3 By Application
5.3.1 Target Identification & Validation
5.3.2 Hit Generation & Lead Optimisation
5.3.3 Preclinical / Clinical Testing
5.3.4 Drug Optimisation & Repurposing
5.4 By End User
5.4.1 Pharmaceutical & Biotechnology Companies
5.4.2 Contract Research Organisations
5.4.3 Academic & Research Institutes
5.4.4 Others
5.5 By Geography
5.5.1 North America
5.5.1.1 United States
5.5.1.2 Canada
5.5.1.3 Mexico
5.5.2 Europe
5.5.2.1 Germany
5.5.2.2 United Kingdom
5.5.2.3 France
5.5.2.4 Italy
5.5.2.5 Spain
5.5.2.6 Rest of Europe
5.5.3 Asia-Pacific
5.5.3.1 China
5.5.3.2 Japan
5.5.3.3 India
5.5.3.4 Australia
5.5.3.5 South Korea
5.5.3.6 Rest of Asia-Pacific
5.5.4 Middle East & Africa
5.5.4.1 GCC
5.5.4.2 South Africa
5.5.4.3 Rest of Middle East & Africa
5.5.5 South America
5.5.5.1 Brazil
5.5.5.2 Argentina
5.5.5.3 Rest of South America
6 Competitive Landscape
6.1 Market Concentration
6.2 Market Share Analysis
6.3 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products & Services, and Recent Developments)
6.3.1 Absci Corporation
6.3.2 Alphabet Inc. (DeepMind / Isomorphic Labs)
6.3.3 Atomwise
6.3.4 BenevolentAI S.A.
6.3.5 Berg Health (BPGbio)
6.3.6 BioSymetrics
6.3.7 Cloud Pharmaceuticals, Inc.
6.3.8 Deep Genomics Inc.
6.3.9 Exscientia plc
6.3.10 GNS Healthcare (Aitia)
6.3.11 Healx
6.3.12 Iktos
6.3.13 Insilico Medicine IP Limited
6.3.14 insitro
6.3.15 International Business Machines Corporation
6.3.16 NVIDIA
6.3.17 Owkin
6.3.18 Recursion Pharmaceuticals
6.3.19 Relay Therapeutics
6.3.20 Schrodinger
6.3.21 Standigm
6.3.22 Valo Health
6.3.23 Verge Genomics
6.3.24 XtalPi
7 Market Opportunities & Future Outlook
7.1 White-space & Unmet-Need Assessment
For more information about this report visit https://www.researchandmarkets.com/r/7r4q0e
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