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市場調查報告書
商品編碼
1712504

全球藥物研發人工智慧市場—2025-2033

Global AI in Drug Discovery and Development Market - 2025-2033
出版日期: | 出版商: DataM Intelligence | 英文 180 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

2024 年全球藥物研發人工智慧市場規模達 62.4 億美元，預計到 2032 年將達到 340.5 億美元，2025-2033 年預測期內複合年成長率為 18.5%。

藥物發現和開發中的人工智慧使用機器學習、深度學習、自然語言處理和資料分析等技術來加快發現、設計和開發新藥的過程。透過分析來自基因組學、蛋白質組學和臨床試驗的大量資料集，人工智慧可以幫助識別潛在目標、預測分子相互作用、最佳化化合物選擇並比傳統方法更有效地預測結果，從而改變製藥行業並使治療發現更快、更精確。

市場動態：

駕駛員和約束裝置

加大人工智慧應用力度，加速藥物研發

全球藥物研發和開發市場的人工智慧因其分析複雜生物資料、識別藥物標靶以及預測化合物功效和毒性的能力而正在獲得發展動力。該技術減少了傳統藥物開發的時間和成本。製藥公司和研究機構正在使用機器學習演算法和深度學習工具來簡化候選藥物篩選、最佳化臨床試驗並增強決策能力，從而加快新藥的上市時間。

此外，人工智慧還可以透過預測結果、設計試驗和實現藥物重新定位來提高臨床試驗的效率。然而，挑戰包括強大的數據共享機制和演算法的全面智慧財產權保護。人工智慧驅動的製藥公司必須有效地整合生物科學和演算法，確保乾濕實驗室實驗的成功整合。

人工智慧整合的監管挑戰

由於監管環境不斷變化，全球藥物研發和開發市場的人工智慧面臨挑戰，FDA 和 EMA 等機構正在製定人工智慧驅動工具的指南。缺乏資料處理、模型驗證和演算法透明度的標準化協議會造成額外的合規負擔。對資料隱私、道德考量以及人工智慧決策的可解釋性的擔憂也增加了這些不確定性。這些不確定性可能會延遲產品發布，並阻礙小型企業採用人工智慧技術，從而限制市場成長。

目錄

第1章：市場介紹和範圍

  • 報告目標
  • 報告範圍和定義
  • 報告範圍

第2章：高階主管見解與關鍵要點

  • 市場亮點和戰略要點
  • 主要趨勢和未來預測
  • 技術片段
  • 按應用程式截取的程式碼片段
  • 按地區分類

第3章：動態

  • 影響因素
    • 驅動程式
      • 加大人工智慧應用力度，加速藥物研發
      • 科技進步的興起
    • 限制
      • 監管挑戰
      • 人工智慧整合成本高昂
    • 機會
      • 新興市場的擴張
    • 影響分析

第4章：戰略洞察與產業展望

  • 監管分析
  • 市場領導者和先驅者
    • 新興先鋒和傑出參與者
    • 擁有最大銷售品牌的既定領導者
    • 擁有成熟產品的市場領導者
  • CXO 觀點
  • 最新進展與突破
  • 監管和報銷情況
    • 北美洲
    • 歐洲
    • 亞太地區
    • 南美洲
    • 中東和非洲
  • 波特五力分析
  • 供應鏈分析
  • 專利分析
  • SWOT分析
  • 未滿足的需求和差距
  • 市場進入和擴張的推薦策略
  • 情境分析：最佳情況、基本情況和最壞情況預測
  • 定價分析和價格動態
  • 關鍵意見領袖

第5章：藥物研發市場中的人工智慧（按技術）

  • 機器學習
  • 自然語言處理
  • 生成式人工智慧
  • 其他

第6章：人工智慧在藥物研發市場的應用

  • 目標發現與驗證
  • 熱門藥物發現與虛擬篩選
  • 先導化合物
  • 線索最佳化
  • 臨床前測試
  • 臨床試驗
  • 其他

第7章：人工智慧在藥物研發市場的應用（按地區）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 西班牙
    • 義大利
    • 歐洲其他地區
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地區
  • 亞太
    • 中國
    • 印度
    • 日本
    • 韓國
    • 亞太其他地區
  • 中東和非洲

第8章：競爭格局與市場定位

  • 競爭概況和主要市場參與者
  • 市佔率分析與定位矩陣
  • 策略夥伴關係、併購
  • 產品組合和創新的關鍵發展
  • 公司基準化分析

第9章：公司簡介

  • Alphabet (Google DeepMind)
    • 公司概況
    • 產品組合
      • 產品描述
      • 產品關鍵績效指標 (KPI)
      • 歷史和預測產品銷售
      • 產品銷售量
    • 財務概覽
      • 公司收入
      • 地區收入佔有率
      • 收入預測
    • 關鍵進展
      • 併購
      • 關鍵產品開發活動
      • 監管部門批准等
    • SWOT分析
  • Atomwise Inc.
  • BenevolentAI
  • BioMap
  • BioSymetrics
  • DEEP GENOMICS.
  • Euretos.
  • Exscientia
  • IBM
  • Iktos.

第 10 章：附錄

簡介目錄
Product Code: HCIT9508

The global AI in drug discovery and development market reached US$ 6.24 billion in 2024 and is expected to reach US$ 34.05 billion by 2032, growing at a CAGR of 18.5% during the forecast period 2025-2033.

AI in drug discovery and development uses technologies like machine learning, deep learning, natural language processing, and data analytics to speed up the process of discovering, designing, and developing new drugs. By analyzing large datasets from genomics, proteomics, and clinical trials, AI helps identify potential targets, predict molecular interactions, optimize compound selection, and forecast outcomes more efficiently than traditional methods, transforming the pharmaceutical industry and making therapy discovery faster and more precise.

Market Dynamics: Drivers & Restraints

Increasing Adoption of Artificial Intelligence for Faster Drug Development

The global AI in drug discovery and development market is gaining momentum due to its ability to analyze complex biological data, identify drug targets, and predict compound efficacy and toxicity. This technology reduces time and cost in traditional drug development. Pharmaceutical companies and research institutions are using machine learning algorithms and deep learning tools to streamline candidate screening, optimize clinical trials, and enhance decision-making, leading to faster time-to-market for new drugs.

Additionally, AI improves clinical trial efficiency by predicting outcomes, designing trials, and enabling drug repositioning. However, challenges include robust data-sharing mechanisms and comprehensive intellectual property protections for algorithms. AI-driven pharmaceutical companies must integrate biological sciences and algorithms effectively, ensuring the successful fusion of wet and dry laboratory experiments.

Regulatory Challenges in AI Integration

The global AI in drug discovery and development market faces challenges due to the evolving regulatory landscape, with bodies like the FDA and EMA developing guidelines for AI-driven tools. The lack of standardized protocols for data handling, model validation, and algorithm transparency creates additional compliance burdens. Concerns around data privacy, ethical considerations, and explainability in AI decisions also add to these uncertainties. These uncertainties can delay product launches and discourage smaller players from adopting AI technologies, limiting market growth.

Segment Analysis

The global AI in drug discovery and development market is segmented based on technology, application, and region.

Technology

Machine learning in the technology segment is expected to grow with the highest CAGR in the forecast period.

Machine learning is a subfield of artificial intelligence that enables machines to imitate intelligent human behavior. AI systems are used to perform complex tasks similar to human problem-solving. The goal of AI is to create computer models that exhibit "intelligent behaviors" like humans, such as recognizing visual scenes, understanding natural language, or performing physical actions. Boris Katz, a principal research scientist at CSAIL, emphasizes this goal.

Machine learning is a key driver of global AI in the drug discovery and development market. It enables faster and more accurate analysis of complex biological data, reducing time and cost. It helps identify and validate drug targets by recognizing patterns in large datasets, aiding in early drug development stages. ML also streamlines compound screening and lead optimization by predicting drug efficacy and toxicity, improving success rates. It also supports efficient clinical trial design through patient stratification and real-time data analysis.

For instance, in April 2024, Aurigene Pharmaceutical Services Limited, a Dr. Reddy's Laboratories company, introduced Aurigene.AI, an AI and ML-assisted platform designed to expedite drug discovery projects from hit identification to candidate nomination.

Geographical Analysis

Asia-Pacific is expected to hold a significant position in the AI drug discovery and development market with the highest market share

The market growth in the Asia-Pacific region is contributed to by various factors such as rising pharmaceutical innovations, increasing investments by pharmaceutical and biopharmaceutical companies in drug discovery and development activities, etc.

For instance, in the Asia-Pacific region, Japan has the strongest pharmaceutical industry, supported by constant and advanced R&D activities, novel innovations, etc. Many pharmaceutical companies operate globally and stand among industry giants. These companies invest heavily in drug discovery and development activities and are forming strategic alliances with AI technology leaders. These collaborative initiatives are the major market drivers of the Japanese market.

For instance, in February 2024, Ono Pharmaceutical Co., Ltd. announced a research collaboration with InveniAI LLC to identify novel therapeutic targets by leveraging InveniAI's cutting-edge artificial intelligence (AI) and machine learning (ML) platforms AlphaMeld and ChatAlphaMeld.

Moreover, in February 2024, Atinary Technologies Inc. announced a partnership with Takeda, one of the largest pharmaceutical manufacturers in Japan and globally. Through this partnership, Takeda will leverage Atinary's leading AI Self-Driving Labs technology, combined with its expertise in R&D and drug discovery.

In addition, several leading technology leaders are establishing their footprint in the Asia-Pacific region, especially in Japan, which will create opportunities for Japanese manufacturers to leverage these advanced AI technologies in their drug discovery activities.

Competitive Landscape

The major global players in the AI in drug discovery market are Alphabet (Google DeepMind), Atomwise Inc., BenevolentAI, BioMap, BioSymetrics, DEEP Genomics, Euretos, Exscientia, IBM, and Iktos. Among others.

Key Developments

  • In January 2025, InveniAI LLC, has announced its recent key milestones as a part of its commitment to revolutionize the drug development process. InveniAI has launched its fully owned subsidiary AlphaMeld Corporation, which specializes in artificial intelligence (AI), generative AI, and machine learning technologies for the development of novel therapies.
  • In July 2024, Exscientia plc announced the expansion of its collaboration with Amazon Web Services (AWS) to utilize the cloud provider's artificial intelligence (AI) and machine learning (ML) services for powering Exscientia's platform for end-to-end drug discovery and automation. The platform uses generative AI models and AWS's scalability and robotic lab automation to design drug candidates quickly and at low cost.

Why Purchase the Report?

  • Pipeline & Innovations: Reviews ongoing clinical trials, product pipelines, and forecasts upcoming pharmaceutical advancements.
  • Technology Performance & Market Positioning: Analyzes product performance, market positioning, and growth potential to optimize strategies.
  • Real-World Evidence: Integrates patient feedback and data into product development for improved outcomes.
  • Physician Preferences & Health System Impact: Examines healthcare provider behaviors and the impact of health system mergers on adoption strategies.
  • Market Updates & Industry Changes: Covers recent regulatory changes, new policies, and emerging technologies.
  • Competitive Strategies: Analyzes competitor strategies, market share, and emerging players.
  • Pricing & Market Access: Reviews pricing models, reimbursement trends, and market access strategies.
  • Market Entry & Expansion: Identifies optimal strategies for entering new markets and partnerships.
  • Regional Growth & Investment: Highlights high-growth regions and investment opportunities.
  • Supply Chain Optimization: Assesses supply chain risks and distribution strategies for efficient Technology delivery.
  • Sustainability & Regulatory Impact: Focuses on eco-friendly practices and evolving regulations in healthcare.
  • Post-market Surveillance: Uses post-market data to enhance product safety and access.
  • Pharmacoeconomics & Value-Based Pricing: Analyzes the shift to value-based pricing and data-driven decision-making in R&D.

The global AI in drug discovery and development market report would provide approximately 54 tables, 47 figures, and 180 pages.

Technology Audience 2023

  • Manufacturers: Pharmaceutical, Biotech Companies, Contract Manufacturers, Distributors, Hospitals.
  • Regulatory & Policy: Compliance Officers, Government, Health Economists, Market Access Specialists.
  • Technology & Innovation: R&D Professionals, Clinical Trial Managers, Pharmacovigilance Experts.
  • Investors: Healthcare Investors, Venture Fund Investors, Pharma Marketing & Sales.
  • Consulting & Advisory: Healthcare Consultants, Industry Associations, Analysts.
  • Supply Chain: Distribution and Supply Chain Managers.
  • Consumers & Advocacy: Patients, Advocacy Groups, Insurance Companies.
  • Academic & Research: Academic Institutions.

Table of Contents

1. Market Introduction and Scope

  • 1.1. Objectives of the Report
  • 1.2. Report Coverage & Definitions
  • 1.3. Report Scope

2. Executive Insights and Key Takeaways

  • 2.1. Market Highlights and Strategic Takeaways
  • 2.2. Key Trends and Future Projections
  • 2.3. Snippet by Technology
  • 2.4. Snippet by Application
  • 2.5. Snippet by Region

3. Dynamics

  • 3.1. Impacting Factors
    • 3.1.1. Drivers
      • 3.1.1.1. Increasing Adoption of Artificial Intelligence for Faster Drug Development
      • 3.1.1.2. Rise in Technological Advancements
    • 3.1.2. Restraints
      • 3.1.2.1. Regulatory Challenges
      • 3.1.2.2. High Cost associated with AI integration
    • 3.1.3. Opportunities
      • 3.1.3.1. Expansion in Emerging Markets
    • 3.1.4. Impact Analysis

4. Strategic Insights and Industry Outlook

  • 4.1. Regulatory Analysis
  • 4.2. Market Leaders and Pioneers
    • 4.2.1. Emerging Pioneers and Prominent Players
    • 4.2.2. Established leaders with the largest-selling Brand
    • 4.2.3. Market leaders with established Product
  • 4.3. CXO Perspectives
  • 4.4. Latest Developments and Breakthroughs
  • 4.5. Regulatory and Reimbursement Landscape
    • 4.5.1. North America
    • 4.5.2. Europe
    • 4.5.3. Asia Pacific
    • 4.5.4. South America
    • 4.5.5. Middle East & Africa
  • 4.6. Porter's Five Forces Analysis
  • 4.7. Supply Chain Analysis
  • 4.8. Patent Analysis
  • 4.9. SWOT Analysis
  • 4.10. Unmet Needs and Gaps
  • 4.11. Recommended Strategies for Market Entry and Expansion
  • 4.12. Scenario Analysis: Best-Case, Base-Case, and Worst-Case Forecasts
  • 4.13. Pricing Analysis and Price Dynamics
  • 4.14. Key Opinion Leaders

5. AI in Drug Discovery and Development Market, By Technology

  • 5.1. Introduction
    • 5.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 5.1.2. Market Attractiveness Index, By Technology
  • 5.2. Machine Learning*
    • 5.2.1. Introduction
    • 5.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 5.3. Natural Language Processing
  • 5.4. Generative AI
  • 5.5. Others

6. AI in Drug Discovery and Development Market, By Application

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 6.1.2. Market Attractiveness Index, By Application
  • 6.2. Target Discovery & Validation*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Hit Discovery & Virtual Screening
  • 6.4. Hit-to-Lead
  • 6.5. Lead Optimization
  • 6.6. Pre-Clinical Testing
  • 6.7. Clinical Trials
  • 6.8. Others

7. AI in Drug Discovery and Development Market, By Region

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 7.1.2. Market Attractiveness Index, By Region
  • 7.2. North America
    • 7.2.1. Introduction
    • 7.2.2. Key Region-Specific Dynamics
    • 7.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 7.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 7.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 7.2.5.1. U.S.
      • 7.2.5.2. Canada
      • 7.2.5.3. Mexico
  • 7.3. Europe
    • 7.3.1. Introduction
    • 7.3.2. Key Region-Specific Dynamics
    • 7.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 7.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 7.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 7.3.5.1. Germany
      • 7.3.5.2. U.K.
      • 7.3.5.3. France
      • 7.3.5.4. Spain
      • 7.3.5.5. Italy
      • 7.3.5.6. Rest of Europe
  • 7.4. South America
    • 7.4.1. Introduction
    • 7.4.2. Key Region-Specific Dynamics
    • 7.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 7.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 7.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 7.4.5.1. Brazil
      • 7.4.5.2. Argentina
      • 7.4.5.3. Rest of South America
  • 7.5. Asia-Pacific
    • 7.5.1. Introduction
    • 7.5.2. Key Region-Specific Dynamics
    • 7.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 7.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 7.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 7.5.5.1. China
      • 7.5.5.2. India
      • 7.5.5.3. Japan
      • 7.5.5.4. South Korea
      • 7.5.5.5. Rest of Asia-Pacific
  • 7.6. Middle East and Africa
    • 7.6.1. Introduction
    • 7.6.2. Key Region-Specific Dynamics
    • 7.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 7.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application

8. Competitive Landscape and Market Positioning

  • 8.1. Competitive Overview and Key Market Players
  • 8.2. Market Share Analysis and Positioning Matrix
  • 8.3. Strategic Partnerships, Mergers & Acquisitions
  • 8.4. Key Developments in Product Portfolios and Innovations
  • 8.5. Company Benchmarking

9. Company Profiles

  • 9.1. Alphabet (Google DeepMind) *
    • 9.1.1. Company Overview
    • 9.1.2. Product Portfolio
      • 9.1.2.1. Product Description
      • 9.1.2.2. Product Key Performance Indicators (KPIs)
      • 9.1.2.3. Historic and Forecasted Product Sales
      • 9.1.2.4. Product Sales Volume
    • 9.1.3. Financial Overview
      • 9.1.3.1. Company Revenue
      • 9.1.3.2. Geographical Revenue Shares
      • 9.1.3.3. Revenue Forecasts
    • 9.1.4. Key Developments
      • 9.1.4.1. Mergers & Acquisitions
      • 9.1.4.2. Key Product Development Activities
      • 9.1.4.3. Regulatory Approvals, etc.
    • 9.1.5. SWOT Analysis
  • 9.2. Atomwise Inc.
  • 9.3. BenevolentAI
  • 9.4. BioMap
  • 9.5. BioSymetrics
  • 9.6. DEEP GENOMICS.
  • 9.7. Euretos.
  • 9.8. Exscientia
  • 9.9. IBM
  • 9.10. Iktos.

LIST NOT EXHAUSTIVE

10. Appendix

  • 10.1. About Us and Services
  • 10.2. Contact Us