제약용 인공지능(AI) : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the artificial intelligence in pharmaceutical market size is projected to be USD 4.35 billion in 2025, USD 6.16 billion in 2026, and reach USD 34.99 billion by 2031, growing at a CAGR of 41.52% from 2026 to 2031.

This report is Segmented by Technology (Machine Learning, and More), Offering (Software Platforms and Services), Application (Drug Discovery, Clinical-Trial Design, and More), Deployment Mode (Cloud-Based, On-Premise/Edge, and Hybrid), and Geography (North America, Europe, Asia-Pacific, and More). The Market Forecasts are Provided in Terms of Value (USD).

Global Artificial Intelligence (AI) In Pharmaceutical Market Trends and Insights

Proliferation of Cross-Industry Collaborations and Partnerships

Pharmaceutical incumbents increasingly fuse their regulatory expertise with startups' algorithmic speed. PostEra's USD 610 million expansion deal with Pfizer in 2024 targets synthesis-planning models that chart low-cost routes for oncology molecules. The 2024 all-stock merger of Recursion and Exscientia created a 10-asset clinical pipeline underpinned by a 23-trillion-observation data lake, demonstrating data-aggregation advantages at scale. Insilico Medicine's USD 120 million pact with Qilu Pharmaceutical shows how cross-border alliances secure patient access and manufacturing capacity in Asia. Such risk-sharing models link milestone payments to clinical outcomes, lowering Big Pharma's upfront R&D exposure while granting AI vendors upside participation. The FDA's January 2025 draft guidance explicitly endorses joint development agreements, clarifying data-sharing and liability rules that once hindered collaboration.

Escalating Pressure to Reduce Drug-Discovery Costs and Timelines

Average out-of-pocket R&D spend per approved asset reached USD 2.6 billion, with cycle times stretching 10-15 years, eroding enterprise returns. AI pipelines automate hit identification, lead optimization, and toxicity prediction, trimming both cost and duration by roughly one-third. Insilico Medicine's fibrosis candidate ISM001-055 progressed from target discovery to Phase IIa proof-of-concept in 30 months, underscoring efficiency gains. The FDA deployed an internal agentic AI in December 2025 that cut investigational new drug review times by 22% in early pilots. While a single generative-chemistry model can cost USD 5 million in compute, BCG estimates 40% lower per-program expenses and 30% shorter timelines once platforms reach scale.

Shortage of Skilled AI-Biopharmaceutical Talent

Global demand for specialists who combine machine learning, molecular biology, and regulatory science far exceeds supply. Fewer than 5,000 senior-level professionals fit this profile, and median salaries topped USD 250,000 in 2025, a 35% premium to software engineering roles. Attrition exceeds 20% as Meta, Google, and OpenAI lure scientists with equity and unlimited compute credits. Academia produces under 200 joint MD-PhD graduates in computational drug discovery annually, creating chronic pipeline gaps. Pharmaceutical HR teams report that 60% of AI requisitions stay open longer than six months, slowing platform deployments and prompting high-multiple "acqui-hires" whose main value lies in staffing rather than IP. Without talent availability, even well-funded AI strategies risk execution delays that erode time-to-market advantages.

Other drivers and restraints analyzed in the detailed report include:

1. Accelerated Adoption of AI-Driven Adaptive Clinical-Trial Designs
2. Maturation of Generative AI Foundation Models for Protein Folding
3. Fragmentation of Clinical and Genomic Data Sets

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

The technology segment posted USD - numbers at segment level not supplied; still generative AI platforms outperformed the overall artificial intelligence in pharmaceutical market with a 43.21% CAGR forecast, while machine learning held a 38.21% share of 2025 sales. This divergence stems from R&D executives favoring algorithms that create molecules over those that merely classify them. NVIDIA's BioNeMo democratized transformer and diffusion models, letting mid-size biotechs run protein language inference without building GPU farms. The artificial intelligence in pharmaceutical market size for generative systems is projected to expand sharply as models like Isomorphic Labs' diffusion stack generate 10,000 ligand ideas per target per day.

Computer vision and NLP remain indispensable but secondary. Convolutional neural networks exceed 95% diagnostic accuracy in histopathology image classification, whereas NLP modules harvest 40% more safety signals from FAERS narratives than rule-based engines. Reinforcement learning optimizes dosing regimens, yet performance brittleness confines it to narrow use cases. Symbolic AI drafts regulatory documents, but uptake is modest. Over 2026-2031 deep-learning image analytics will grow near the artificial intelligence in pharmaceutical industry average, ceding spotlight to protein-aware diffusion models that sustain compound novelty.

Services will grow at 43.78%, eclipsing platform software despite the latter's 45.32% 2025 revenue share. CIOs prefer variable pricing aligned with clinical milestones, a model exemplified by Recursion's per-candidate billing that shifted USD 180 million in risk in 2025. Hyperscalers combine GPU clusters, pre-trained models, and compliance tooling, capturing 35% of the services subsegment. The artificial intelligence in pharmaceutical market share for cloud-managed LLMOps reached the low-double digits, reinforcing vendor lock-in advantages for AWS, Azure, and Google Cloud.

Software platforms remain essential for data-rich pharmas. Schrodinger, Benchling, and Dotmatics each commanded enterprise ACVs above USD 500,000 in 2025. Yet growth cools as license-heavy models conflict with CFO mandates for cash conservation. Custom project engagements grew 22% year-over-year, especially for rare-disease pipelines demanding bespoke feature engineering. Over the forecast horizon, service providers that guarantee regulatory-grade outputs will consolidate share, while monolithic licenses migrate to subscription or milestone-linked contracts.

Complete Report Scope:

• By Technology
  • Machine Learning
    • Supervised Learning
    • Unsupervised & Self-Supervised Learning
  • Deep Learning
  • Natural Language Processing
  • Computer Vision
  • Generative AI (Diffusion / Transformer-Based)
  • Other Technologies
• By Offering
  • Software Platforms
  • Services (AI-As-A-Service, Custom Projects, Managed LLMOps)
• By Application
  • Drug Discovery & Pre-Clinical Development
  • Clinical-Trial Design & Patient Recruitment
  • Manufacturing & Quality Control
  • Pharmacovigilance & Safety Monitoring
  • Sales, Marketing & Commercial Analytics
  • Laboratory Automation / Self-Driving Labs
  • Other Applications
• By Deployment Mode
  • Cloud-Based
    • Public Cloud
    • Private VPC / Sovereign Cloud
  • On-Premise / Edge
  • Hybrid (Burst-To-Cloud)
• Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • Japan
    • India
    • Australia
    • South Korea
    • Rest of Asia-Pacific
  • Middle East & Africa
    • GCC
    • South Africa
    • Rest of Middle East & Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America held 41.52% of 2025 revenue, underpinned by a USD 4.2 billion venture influx and FDA sandbox programs that accelerate algorithm validation. California, Massachusetts, and New York dominated deal flow, while Canada contributed federated-learning frameworks to satisfy privacy laws yet captured just 4% of regional funding. Mexico's contract-manufacturing plants began piloting computer-vision QA, though adoption outside multinationals remains limited. The FDA's January 2026 good-AI guidelines further strengthen the region's first-mover advantage.

Asia-Pacific is forecast to post a 42.54% CAGR, the fastest of any region. China committed RMB 15 billion (USD 2.1 billion) in 2025 to AI-pharma consortia, elevating domestic champions such as XtalPi. Japan's sandbox program targets geriatric adverse-event prediction as one-third of its population exceeds 65 years[2]. India leveraged cost-effective clinical-trial infrastructure to attract USD 320 million in 2024 AI funding, mostly for generics optimization. South Korea and Australia together held under 5% share but formed national consortiums to reduce reliance on U.S. and Chinese stacks.

Europe accounted for 22% of global turnover in 2025. Germany's Fraunhofer Institute partnered with Bayer and Boehringer to develop explainable modules aligned with EMA auditing requirements[3]. The UK's AI Airlock Sandbox slashed adaptive-trial approval cycles by seven months. France houses a 67-million-record health data hub, yet GDPR consent constraints limit pharmaceutical access to 10 million records, impeding large-scale model training. Latin America, the Middle East, and Africa combined delivered 8% of 2025 revenue; Brazil and the UAE piloted AI-based pharmacovigilance but lack sufficient trial density to generate global-grade datasets.

1. AbSci Corp.
2. Alphabet
3. Atomwise Inc.
4. Benevolent AI
5. Cyclica Inc. (Numinus)
6. Deep Genomics
7. Evotec
8. Exscientia PLC
9. InveniAI
10. Insilico Medicine
11. NVIDIA Corp.
12. Owkin SA
13. PathAI
14. Recursion Pharmaceuticals
15. Valo Health
16. Verge Genomics
17. VeriSIM Life
18. XtalPi

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

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 Proliferation of Cross-Industry Collaborations and Partnerships
  • 4.2.2 Escalating Pressure to Reduce Drug-Discovery Costs and Timelines
  • 4.2.3 Accelerated Adoption of AI-Driven Adaptive Clinical-Trial Designs
  • 4.2.4 Maturation of Generative AI Foundation Models for Protein Folding
  • 4.2.5 Emergence of Quantum-Enhanced Computing for Molecular Simulation
  • 4.2.6 Expansion Of Regulatory AI Sandboxes Facilitating Algorithmic Trial Design
• 4.3 Market Restraints
  • 4.3.1 Shortage of Skilled AI-Biopharmaceutical Talent
  • 4.3.2 Fragmentation of Clinical and Genomic Data Sets
  • 4.3.3 Rising Cloud Compute Costs Relative to R&D Budgets
  • 4.3.4 Regulatory Concerns Over Algorithmic Bias And Transparency
• 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 Buyers / Consumers
  • 4.7.3 Bargaining Power Of Suppliers
  • 4.7.4 Threat Of Substitute Products
  • 4.7.5 Intensity Of Competitive Rivalry

5 Market Size & Growth Forecasts (Value, USD)

• 5.1 By Technology
  • 5.1.1 Machine Learning
    • 5.1.1.1 Supervised Learning
    • 5.1.1.2 Unsupervised & Self-Supervised Learning
  • 5.1.2 Deep Learning
  • 5.1.3 Natural Language Processing
  • 5.1.4 Computer Vision
  • 5.1.5 Generative AI (Diffusion / Transformer-Based)
  • 5.1.6 Other Technologies
• 5.2 By Offering
  • 5.2.1 Software Platforms
  • 5.2.2 Services (AI-As-A-Service, Custom Projects, Managed LLMOps)
• 5.3 By Application
  • 5.3.1 Drug Discovery & Pre-Clinical Development
  • 5.3.2 Clinical-Trial Design & Patient Recruitment
  • 5.3.3 Manufacturing & Quality Control
  • 5.3.4 Pharmacovigilance & Safety Monitoring
  • 5.3.5 Sales, Marketing & Commercial Analytics
  • 5.3.6 Laboratory Automation / Self-Driving Labs
  • 5.3.7 Other Applications
• 5.4 By Deployment Mode
  • 5.4.1 Cloud-Based
    • 5.4.1.1 Public Cloud
    • 5.4.1.2 Private VPC / Sovereign Cloud
  • 5.4.2 On-Premise / Edge
  • 5.4.3 Hybrid (Burst-To-Cloud)
• 5.5 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 Corp.
  • 6.3.2 Alphabet Inc. (Isomorphic Labs)
  • 6.3.3 Atomwise Inc.
  • 6.3.4 BenevolentAI
  • 6.3.5 Cyclica Inc. (Numinus)
  • 6.3.6 Deep Genomics
  • 6.3.7 Evotec SE
  • 6.3.8 Exscientia PLC
  • 6.3.9 InveniAI LLC
  • 6.3.10 Insilico Medicine
  • 6.3.11 NVIDIA Corp.
  • 6.3.12 Owkin SA
  • 6.3.13 PathAI
  • 6.3.14 Recursion Pharmaceuticals
  • 6.3.15 Valo Health
  • 6.3.16 Verge Genomics
  • 6.3.17 VeriSIM Life
  • 6.3.18 XtalPi Inc.

7 Market Opportunities & Future Outlook

• 7.1 White-Space & Unmet-Need Assessment