Drug Discovery 분야 인공지능(AI) 시장 : 기술별, 치료 영역별, 용도별, 최종 사용자별, 도입 형태별 - 세계 시장 예측(2026-2032년)

The Artificial Intelligence in Drug Discovery Market is projected to grow by USD 6.71 billion at a CAGR of 18.12% by 2032.

Artificial intelligence in drug discovery has moved from experimental use cases to a core R&D capability across target identification, hit discovery, lead optimization, ADMET prediction, biomarker discovery, and clinical trial design. The shift is supported by validated scientific milestones, including the AlphaFold Protein Structure Database covering more than 200 million predicted protein structures, the U.S. FDA's ongoing work on AI and machine learning in drug development, and the growing use of high-performance cloud computing in life sciences.

For pharmaceutical companies, biotechnology firms, contract research organizations, and technology partners, AI is not a replacement for biology, chemistry, or clinical evidence. Its strongest value is in compressing hypothesis cycles, improving decision quality, and prioritizing compounds and patient populations before expensive wet-lab or clinical investments are made.

Transformative Shifts in the AI Drug Discovery Landscape

The drug discovery landscape is shifting from sequential, lab-intensive workflows to integrated computational-experimental models. Generative AI, graph neural networks, natural language processing, and multimodal foundation models are helping researchers analyze chemical libraries, omics datasets, protein structures, publications, patents, and real-world data at scale.

Regulatory and operating models are also changing. The U.S. FDA, EMA, and other agencies are increasing attention on model transparency, validation, data provenance, and risk management. At the same time, cloud infrastructure, secure data collaboration, and automation are enabling pharmaceutical R&D teams to evaluate more hypotheses while maintaining traceability and reproducibility.

Cumulative Impact of Artificial Intelligence on Drug R&D

The cumulative impact of artificial intelligence is most visible in earlier and more evidence-driven decision-making. AI-enabled platforms can help identify disease-relevant targets, screen virtual compound libraries, forecast toxicity, optimize molecular properties, and support patient stratification. These applications reduce avoidable experimentation and improve the quality of candidates moving into validation.

However, AI does not eliminate scientific uncertainty. Drug attrition remains driven by complex biology, safety, efficacy, manufacturability, and clinical execution. The organizations gaining the greatest advantage are those combining high-quality proprietary data, rigorous model governance, laboratory validation, and cross-functional teams spanning biology, chemistry, informatics, clinical development, and regulatory affairs.

Key Regional Insights: North America, Europe, Asia-Pacific, Latin America, Middle East, and Africa

North America leads AI-enabled drug discovery through a dense ecosystem of pharmaceutical companies, AI-native biotechnology firms, academic medical centers, cloud providers, venture capital, and regulatory engagement. The United States benefits from NIH-funded biomedical research, FDA digital health and AI initiatives, and a large clinical trial infrastructure, while Canada's long-standing AI research base and biomedical clusters strengthen translational innovation.

Europe is advancing through strong public research, biopharmaceutical manufacturing, and regulatory modernization, including the EU AI Act and European Health Data Space framework. Asia-Pacific is scaling rapidly, with China, Japan, India, South Korea, Singapore, and Australia investing in genomics, precision medicine, computational biology, and biomanufacturing. Latin America is emerging through Brazil and Mexico's clinical research capacity, public health datasets, and pharmaceutical manufacturing foundations, while the Middle East is investing in genomics, digital health, and precision medicine through national strategies in the UAE, Saudi Arabia, and other Gulf economies. Africa's opportunity is tied to genomic diversity, infectious disease research, local biobank development, and capacity-building programs that can improve global model generalizability and reduce data bias in AI drug discovery.

Key Group Insights: EU, G7, BRICS, ASEAN, GCC, and NATO

The European Union is becoming a reference market for trustworthy AI in life sciences, combining advanced research networks with strict rules on data protection, clinical evidence, and algorithmic accountability. G7 countries continue to shape global standards through pharmaceutical R&D intensity, academic excellence, intellectual property systems, regulatory science coordination, and cross-border collaboration on health security.

BRICS economies are strategically important because of their population scale, expanding clinical research networks, manufacturing capacity, and increasing investment in AI, genomics, and biotechnology. ASEAN is gaining relevance through Singapore-led biomedical innovation, regional digital health adoption, and growing clinical research activity across Southeast Asia, while the GCC is using national health transformation programs, population genomics initiatives, and advanced hospital infrastructure to accelerate precision medicine. NATO member countries also influence the sector indirectly through biosecurity, resilient supply chains, cybersecurity, trusted data infrastructure, and dual-use technology governance that affect AI-enabled biomedical innovation.

Key Country Insights Across Major AI Drug Discovery Markets

The United States remains the most influential country for AI in drug discovery due to its concentration of pharmaceutical R&D, venture-backed AI biotechnology companies, FDA engagement, NIH-supported biomedical research, and leading academic medical centers. Canada contributes world-class machine learning expertise and translational AI hubs, while Mexico and Brazil strengthen the Americas through clinical research access, pharmaceutical production capabilities, public health institutions, and growing digital health adoption.

In Europe, the United Kingdom combines biomedical research excellence, national health data assets, and a strong AI startup ecosystem; Germany leads in pharmaceutical manufacturing, engineering, applied research, and industrial automation; France supports AI and health innovation through national investment programs and biomedical research networks; Italy and Spain offer strong clinical trial networks, hospital systems, and translational medicine capabilities; and Russia retains scientific capacity in chemistry, mathematics, and computational research despite geopolitical and collaboration constraints. In Asia-Pacific, China's scale in data infrastructure, AI investment, genomics, and biopharma innovation is significant; India combines pharmaceutical manufacturing, digital public infrastructure, bioinformatics talent, and software engineering depth; Japan offers advanced precision medicine, robotics, and aging-related biomedical research capabilities; Australia has strong clinical research, genomics programs, and biomedical science institutions; and South Korea is expanding rapidly through government-backed biohealth, hospital digitalization, semiconductor strength, and AI initiatives.

Actionable Recommendations for Industry Leaders

Industry leaders should prioritize AI use cases with measurable scientific and operational value, such as target validation, virtual screening, toxicity prediction, protein structure analysis, biomarker discovery, and trial enrichment. Building a defensible advantage requires proprietary datasets, interoperable data architecture, model validation protocols, bias monitoring, cybersecurity controls, and clear documentation aligned with regulatory expectations.

Should also invest in human-AI operating models. The highest-performing organizations embed computational scientists with medicinal chemists, biologists, clinicians, and regulatory experts. Partnerships with AI biotechnology firms, cloud providers, universities, and CROs should be structured around data rights, reproducibility, auditability, model ownership, privacy protection, and milestone-based outcomes rather than technology adoption alone.

Research Methodology

This executive summary is based on a structured secondary research approach using verified public information from regulatory agencies, scientific institutions, government programs, peer-reviewed literature, public disclosures, and recognized life sciences databases. Key themes were triangulated across AI drug discovery workflows, regional innovation systems, regulatory developments, scientific adoption, and investment patterns.

The analysis emphasizes evidence-backed trends rather than speculative market claims. It evaluates how artificial intelligence is applied across discovery and development, where adoption is strongest, which regional ecosystems are gaining momentum, and what governance practices are needed to convert computational insights into validated therapeutic progress.

Conclusion: AI as a Strategic Engine for Modern Drug Discovery

Artificial intelligence is becoming a durable capability in drug discovery because it improves the speed, scale, and precision of scientific decision-making. Its greatest value is achieved when computational models are connected to reliable biological data, laboratory validation, clinical insight, and disciplined governance.

The landscape will be shaped by organizations that can combine AI innovation with regulatory credibility, proprietary data, and therapeutic domain expertise. As adoption expands across regions and industry groups, AI-enabled drug discovery is expected to remain one of the most important forces modernizing pharmaceutical R&D.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Definition
• 1.3. Market Segmentation & Coverage
• 1.4. Years Considered for the Study
• 1.5. Currency Considered for the Study
• 1.6. Language Considered for the Study
• 1.7. Key Stakeholders

2. Research Methodology

• 2.1. Introduction
• 2.2. Research Design
  • 2.2.1. Primary Research
  • 2.2.2. Secondary Research
• 2.3. Research Framework
  • 2.3.1. Qualitative Analysis
  • 2.3.2. Quantitative Analysis
• 2.4. Market Size Estimation
  • 2.4.1. Top-Down Approach
  • 2.4.2. Bottom-Up Approach
• 2.5. Data Triangulation
• 2.6. Research Outcomes
• 2.7. Research Assumptions
• 2.8. Research Limitations

3. Executive Summary

• 3.1. Introduction
• 3.2. CXO Perspective
• 3.3. Market Size & Growth Trends
• 3.4. Market Share Analysis, 2025
• 3.5. FPNV Positioning Matrix, 2025
• 3.6. New Revenue Opportunities
• 3.7. Next-Generation Business Models
• 3.8. Industry Roadmap

4. Market Overview

• 4.1. Introduction
• 4.2. Industry Ecosystem & Value Chain Analysis
  • 4.2.1. Supply-Side Analysis
  • 4.2.2. Demand-Side Analysis
  • 4.2.3. Stakeholder Analysis
• 4.3. Market Dynamics
  • 4.3.1. Key Drivers
  • 4.3.2. Key Restraints
  • 4.3.3. Key Opportunities
  • 4.3.4. Key Challenges
• 4.4. Porter's Five Forces Analysis
• 4.5. PESTLE Analysis
• 4.6. Market Outlook
  • 4.6.1. Near-Term Market Outlook (0-2 Years)
  • 4.6.2. Medium-Term Market Outlook (3-5 Years)
  • 4.6.3. Long-Term Market Outlook (5-10 Years)
• 4.7. Go-to-Market Strategy

5. Market Insights

• 5.1. Consumer Insights & End-User Perspective
• 5.2. Consumer Experience Benchmarking
• 5.3. Opportunity Mapping
• 5.4. Distribution Channel Analysis
• 5.5. Pricing Trend Analysis
• 5.6. Regulatory Compliance & Standards Framework
• 5.7. ESG & Sustainability Analysis
• 5.8. Disruption & Risk Scenarios
• 5.9. Return on Investment & Cost-Benefit Analysis

6. Cumulative Impact of Artificial Intelligence 2026

7. Artificial Intelligence in Drug Discovery Market, by Technology

• 7.1. Computer Vision
• 7.2. Deep Learning
• 7.3. Machine Learning
• 7.4. Natural Language Processing

8. Artificial Intelligence in Drug Discovery Market, by Therapeutic Area

• 8.1. Cardiovascular Diseases
• 8.2. Central Nervous System
• 8.3. Infectious Diseases
• 8.4. Oncology

9. Artificial Intelligence in Drug Discovery Market, by Application

• 9.1. ADMET & Toxicology Prediction
  • 9.1.1. Pharmacodynamics Prediction
  • 9.1.2. Pharmacokinetics Prediction
  • 9.1.3. Toxicity Prediction
• 9.2. Clinical Trial Optimization
  • 9.2.1. Patient Recruitment
  • 9.2.2. Trial Design Optimization
• 9.3. Hit Identification
  • 9.3.1. High Throughput Screening
  • 9.3.2. In Silico Target Validation
  • 9.3.3. Virtual Screening
• 9.4. Lead Optimization
  • 9.4.1. De Novo Drug Design
  • 9.4.2. Quantitative Structure Activity Relationship
  • 9.4.3. Structure Based Drug Design
• 9.5. Protein Structure Prediction
  • 9.5.1. Ab Initio Modeling
  • 9.5.2. Homology Modeling
  • 9.5.3. Molecular Dynamics Simulation

10. Artificial Intelligence in Drug Discovery Market, by End User

• 10.1. Academic & Research Institutes
• 10.2. Biotechnology Companies
• 10.3. Contract Research Organizations
• 10.4. Pharmaceutical Companies

11. Artificial Intelligence in Drug Discovery Market, by Deployment Mode

• 11.1. Cloud Based
• 11.2. Hybrid
• 11.3. On Premises

12. Artificial Intelligence in Drug Discovery Market, by Region

• 12.1. Asia-Pacific
• 12.2. North America
• 12.3. Latin America
• 12.4. Europe
• 12.5. Middle East
• 12.6. Africa

13. Artificial Intelligence in Drug Discovery Market, by Group

• 13.1. ASEAN
• 13.2. GCC
• 13.3. European Union
• 13.4. BRICS
• 13.5. G7
• 13.6. NATO

14. Artificial Intelligence in Drug Discovery Market, by Country

• 14.1. United States
• 14.2. Canada
• 14.3. Mexico
• 14.4. Brazil
• 14.5. United Kingdom
• 14.6. Germany
• 14.7. France
• 14.8. Russia
• 14.9. Italy
• 14.10. Spain
• 14.11. China
• 14.12. India
• 14.13. Japan
• 14.14. Australia
• 14.15. South Korea

15. Competitive Landscape

• 15.1. Market Concentration Analysis, 2025
  • 15.1.1. Concentration Ratio (CR)
  • 15.1.2. Herfindahl Hirschman Index (HHI)
• 15.2. Recent Developments & Impact Analysis, 2025
• 15.3. Product Portfolio Analysis, 2025
• 15.4. Benchmarking Analysis, 2025

16. Company Profiles

• 16.1. AbCellera
• 16.2. Atomwise Inc
• 16.3. BenchSci
• 16.4. BenevolentAI
• 16.5. BioAge Labs
• 16.6. Bpgbio Inc
• 16.7. Deep Genomics
• 16.8. Envisagenics
• 16.9. Evaxion Biotech A/S
• 16.10. Generate Biomedicines
• 16.11. Genesis Therapeutics
• 16.12. Iambic Therapeutics
• 16.13. Iktos
• 16.14. Insilico Medicine
• 16.15. Insitro
• 16.16. Nimbus Therapeutics
• 16.17. NuMedii Inc
• 16.18. Owkin Inc
• 16.19. Recursion Pharmaceuticals
• 16.20. Schrodinger Inc
• 16.21. Standigm Inc
• 16.22. Terray Therapeutics
• 16.23. Valo Health
• 16.24. Verge Genomics
• 16.25. XtalPi Inc