Artificial Intelligence in Drug Discovery Market - Share, Trends, Growth, and Forecasts, 2026-2031 | USD 10.29 Billion Opportunity at 25.94% CAGR, Led by APAC and AI Services
Dublin, Oct. 06, 2026 (GLOBE NEWSWIRE) -- "Artificial Intelligence In Drug Discovery - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026-2031)" has been added to ResearchAndMarkets.com's offering.
The artificial intelligence in drug discovery market was valued at USD 2.58 billion in 2025 and is expected to increase from USD 3.25 billion in 2026 to USD 10.29 billion by 2031. The market is projected to register a CAGR of 25.94% during the 2026-2031 forecast period, driven by rising biopharmaceutical research and development costs, growing global disease burdens, expanding biomedical datasets and increased collaboration between pharmaceutical companies and artificial intelligence specialists.
Rising R&D Costs Accelerate AI Adoption
A 40% decline in R&D productivity between 2010 and 2024 encouraged pharmaceutical executives to invest in predictive algorithms capable of shortening lead-optimization cycles from 18 months to six months. In 2025, Exscientia demonstrated that AI-designed molecules could progress to first-in-human studies within a single year, highlighting potential financial and operational advantages over traditional medicinal-chemistry workflows.
Rare-disease programs may benefit significantly because limited patient populations can restrict commercial returns. Budget-conscious biotechnology companies are increasingly licensing turnkey AI services rather than building costly internal infrastructure. As a result, artificial intelligence is becoming a cost-avoidance and productivity tool across drug discovery instead of an incremental technology expense.
Chronic and Infectious Diseases Support Market Expansion
Chronic illnesses accounted for 74% of worldwide mortality in 2025, while only a limited proportion of new molecular entities addressed metabolic and cardiovascular pathways. AI platforms can analyze multi-omic datasets to identify polygenic targets that may be missed by conventional screening methods. During the 2024 mpox resurgence, a generative-chemistry engine identified drug-repurposing candidates within 48 hours, demonstrating the speed of AI-enabled research during emerging public health threats.
Oncology represented 38% of AI drug discovery projects, while neurodegeneration emerged as the fastest-expanding therapeutic area. Algorithms that combine brain imaging with proteomics are helping researchers identify and stratify patient groups. The continued shift toward chronic, infectious and multi-factorial diseases is expected to increase demand for AI-supported target identification, drug design and candidate screening.
AI Explainability and Regulation Remain Key Challenges
Regulatory and clinical adoption may be constrained by concerns surrounding AI explainability. The FDA's 2025 draft guidance requires sponsors to document model lineage and decision boundaries, although many neural networks remain difficult to interpret. A lack of clear mechanistic rationales can also make clinical investigators more cautious about patient enrollment, potentially slowing trial recruitment.
The European Medicines Agency established an AI committee in 2025, but globally harmonized regulations are still developing. Attention mechanisms can improve model interpretability, although they may reduce predictive performance. Pharmaceutical sponsors must therefore balance accuracy, transparency and compliance, leading to longer validation timelines and greater documentation requirements. Other market considerations include the availability of high-quality biomedical data, strategic pharmaceutical-AI partnerships and shortages of multidisciplinary talent.
Services Record Strong Growth as Companies Seek Turnkey Capabilities
Software held a 62.43% market share in 2025, while services are forecast to expand at a CAGR of 27.54% through 2031. The market size for AI drug discovery services reached USD 0.79 billion in 2026 as companies increased their use of turnkey target-identification pipelines. Contract research organizations are integrating active-learning modules that customers can purchase on a project basis, converting fixed technology costs into variable spending.
Software remains essential for pharmaceutical businesses seeking control over proprietary libraries and intellectual property. However, per-simulation pricing is increasingly blurring the distinction between software licensing and services. Seed-stage biotechnology companies are particularly attracted to managed services because they often lack the capital required for dedicated computing infrastructure. Regulatory demand for validated audit trails also benefits specialized vendors that provide documentation and compliance support as managed services.
Machine Learning Leads While Quantum Technologies Gain Momentum
Machine-learning frameworks accounted for 46.54% of spending in 2025. Quantum approaches, however, are projected to grow at a CAGR of 27.65% through 2031 and could exceed USD 1 billion by the end of the forecast period if hardware error-correction milestones remain on schedule. Current neural-quantum state algorithms can simulate fewer than 100 atoms, which continues to limit their practical scope.
Natural-language processing represented an 18% share and is being used to examine approximately 30 million biomedical publications and enrich knowledge graphs. Computer-vision technologies held a 12% share, supported by their ability to process image datasets from phenotypic screening programs. Companies are also fine-tuning open-source protein models such as ESM-2 instead of developing bespoke architectures, reducing data requirements by as much as 80%. Partnerships such as IBM and Moderna indicate growing interest in quantum readiness for complex biologic simulations.
Regional Outlook for the Artificial Intelligence in Drug Discovery Market
North America led the market with a 43.54% share in 2025. FDA guidance, strong venture capital activity and an established biotechnology ecosystem supported regional growth. Venture funding in the United States reached USD 4.1 billion in 2025, with 62% directed toward companies that had advanced at least one asset into Phase II. Canada's Vector Institute also partnered with eight pharmaceutical companies to develop protein-engineering foundation models. North America's share may decline to approximately 40% by 2031, but the region is expected to remain a major center for AI-enabled pharmaceutical innovation.
Europe is projected to record a 24% CAGR. London strengthened its position as an AI-biotechnology hub through a GBP 100 million investment in 15 start-ups. Germany's adoption remained more limited, with 35% of pharmaceutical companies operating AI pipelines in 2025. Academic clusters in Barcelona and Milan are using public funding to establish open-science data lakes and support biotechnology spinouts.
Asia-Pacific is forecast to be the fastest-growing region, registering a CAGR of 26.54% through 2031. China doubled its share of global AI drug discovery projects to 12% in 2025, supported by government incentives and sovereign-AI initiatives. Insilico Medicine advanced three molecules into clinical trials, while India's Syngene reduced lead-optimization cycles to three weeks through AI-enhanced retrosynthesis planning. Japan's Takeda also partnered with Preferred Networks to automate antibody design.
South Korea and Australia are supporting AI drug discovery through government-funded programs, although both markets have less-developed venture capital ecosystems. The Middle East and Africa accounted for 4% of the market, while South America represented 3%, with growth constrained by limited R&D infrastructure and shortages of specialized talent. Asia-Pacific's rapid expansion is expected to play a decisive role in shaping the global artificial intelligence in drug discovery market through 2031.
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 Rising Biopharmaceutical R&D Cost Pressures
4.2.2 Growing Global Disease Burden Across Chronic and Infectious Areas
4.2.3 Increasing Strategic Collaborations Between Pharmaceutical and AI Companies
4.2.4 Expansion of High-Quality Biomedical Data Assets
4.2.5 Advancing Cloud and High-Performance Computing Accessibility
4.2.6 Emergence of Next-Generation AI Drug Design Paradigms
4.3 Market Restraints
4.3.1 Regulatory and Clinical Adoption Challenges Related to AI Explainability
4.3.2 Limited Availability of Integrated Multidisciplinary Talent
4.3.3 Data Fragmentation and Lack Of Standardization Across Research Silos
4.3.4 Intellectual Property and Liability Uncertainties for AI-Generated Molecules
4.4 Value / Supply-Chain Analysis
4.5 Regulatory Outlook
4.6 Technological Outlook
4.7 Porter's Five Forces Analysis
4.7.1 Threat Of New Entrants
4.7.2 Bargaining Power Of Buyers
4.7.3 Bargaining Power Of Suppliers
4.7.4 Threat Of Substitutes
4.7.5 Competitive Rivalry
5 Market Size & Growth Forecasts (Value, USD)
5.1 By Component
5.1.1 Software
5.1.2 Service
5.2 By Technology
5.2.1 Machine Learning
5.2.2 Natural Language Processing
5.2.3 Computer Vision
5.2.4 Quantum Machine Learning
5.3 By Application
5.3.1 Target Identification & Validation
5.3.2 Hit Generation & Prioritization
5.3.3 Lead Optimization
5.3.4 Candidate Screening
5.3.5 Drug Repurposing
5.3.6 De Novo Drug Design
5.3.7 Pre-Clinical Safety & Toxicity Assessment
5.4 By Drug Type
5.4.1 Small Molecule
5.4.2 Biologics
5.4.3 Gene And Cell Therapy
5.5 By Deployment
5.5.1 Cloud-Based
5.5.2 On-Premise
5.6 By End User
5.6.1 Pharmaceutical & Biotechnological Companies
5.6.2 Academic & Research Institutes
5.6.3 Other End Users
5.7 By Geography
5.7.1 North America
5.7.1.1 United States
5.7.1.2 Canada
5.7.1.3 Mexico
5.7.2 Europe
5.7.2.1 Germany
5.7.2.2 United Kingdom
5.7.2.3 France
5.7.2.4 Italy
5.7.2.5 Spain
5.7.2.6 Rest Of Europe
5.7.3 APAC
5.7.3.1 China
5.7.3.2 Japan
5.7.3.3 India
5.7.3.4 South Korea
5.7.3.5 Australia
5.7.3.6 Rest Of APAC
5.7.4 Middle East And Africa
5.7.4.1 Middle East
5.7.4.1.1 GCC
5.7.4.1.2 Turkey
5.7.4.1.3 Rest Of Middle East
5.7.4.2 Africa
5.7.4.2.1 South Africa
5.7.4.2.2 Rest Of Africa
5.7.5 South America
5.7.5.1 Brazil
5.7.5.2 Argentina
5.7.5.3 Rest Of South America
6 Competitive Landscape
6.1 Market Concentration
6.2 Strategic Moves
6.3 Market Share Analysis
6.4 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)
6.4.1 Aitia
6.4.2 Ardigen
6.4.3 Atomwise Inc.
6.4.4 Auransa Inc.
6.4.5 BenevolentAI
6.4.6 BioXcel Therapeutics
6.4.7 Cloud Pharmaceuticals Inc.
6.4.8 Cyclica Inc.
6.4.9 Deep Genomics
6.4.10 Eagle Genomics
6.4.11 Evotec SE
6.4.12 Exscientia PLC
6.4.13 Genesis Therapeutics
6.4.14 Healx
6.4.15 IBM Corporation
6.4.16 Innoplexus AG
6.4.17 Insilico Medicine Inc.
6.4.18 Isomorphic Labs (Alphabet)
6.4.19 Iktos
6.4.20 Microsoft Corporation
6.4.21 NVIDIA Corporation
6.4.22 Peptilogics
6.4.23 PostEra
6.4.24 Recursion Pharmaceuticals Inc.
6.4.25 Schrodinger Inc.
6.4.26 Standigm
6.4.27 Turbine AI
6.4.28 Valo Health
6.4.29 Verge Genomics
7 Market Opportunities & Future Outlook
7.1 White-Space & Unmet-Need Assessment
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