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市场调查报告书
商品编码
1712504

全球药物研发人工智慧市场—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个工作天内

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简介目录

2024 年全球药物研发人工智慧市场规模达 62.4 亿美元，预计到 2032 年将达到 340.5 亿美元，2025-2033 年预测期内复合年增长率为 18.5%。

药物发现和开发中的人工智慧使用机器学习、深度学习、自然语言处理和资料分析等技术来加快发现、设计和开发新药的过程。透过分析来自基因组学、蛋白质组学和临床试验的大量数据集，人工智慧可以帮助识别潜在目标、预测分子相互作用、优化化合物选择并比传统方法更有效地预测结果，从而改变製药行业并使治疗发现更快、更精确。

市场动态：

驾驶员和约束装置

加大人工智慧应用力度，加速药物研发

全球药物研发和开发市场的人工智慧因其分析复杂生物资料、识别药物标靶以及预测化合物功效和毒性的能力而正在获得发展动力。该技术减少了传统药物开发的时间和成本。製药公司和研究机构正在使用机器学习演算法和深度学习工具来简化候选药物筛选、优化临床试验并增强决策能力，从而加快新药的上市时间。

此外，人工智慧还可以透过预测结果、设计试验和实现药物重新定位来提高临床试验的效率。然而，挑战包括强大的数据共享机制和演算法的全面智慧财产权保护。人工智慧驱动的製药公司必须有效地整合生物科学和演算法，确保干湿实验室实验的成功整合。

人工智慧整合的监管挑战

由于监管环境不断变化，全球药物研发和开发市场的人工智慧面临挑战，FDA 和 EMA 等机构正在製定人工智慧驱动工具的指南。缺乏资料处理、模型验证和演算法透明度的标准化协议会造成额外的合规负担。对资料隐私、道德考量以及人工智慧决策的可解释性的担忧也增加了这些不确定性。这些不确定性可能会延迟产品发布，并阻碍小型企业采用人工智慧技术，从而限制市场成长。

目录

第一章：市场介绍和范围

  • 报告目标
  • 报告范围和定义
  • 报告范围

第二章：高阶主管见解与关键要点

  • 市场亮点和战略要点
  • 主要趋势和未来预测
  • 技术片段
  • 按应用程式截取的程式码片段
  • 按地区分类的片段

第三章：动态

  • 影响因素
    • 驱动程式
      • 加大人工智慧应用力度，加速药物研发
      • 科技进步的兴起
    • 限制
      • 监管挑战
      • 人工智慧整合成本高昂
    • 机会
      • 新兴市场的扩张
    • 影响分析

第四章：战略洞察与产业展望

  • 监管分析
  • 市场领导者和先驱者
    • 新兴先锋和杰出参与者
    • 拥有最大销售品牌的既定领导者
    • 拥有成熟产品的市场领导者
  • CXO 观点
  • 最新进展与突破
  • 监管和报销情况
    • 北美洲
    • 欧洲
    • 亚太地区
    • 南美洲
    • 中东和非洲
  • 波特五力分析
  • 供应链分析
  • 专利分析
  • SWOT分析
  • 未满足的需求和差距
  • 市场进入和扩张的推荐策略
  • 情境分析：最佳情况、基本情况和最坏情况预测
  • 定价分析和价格动态
  • 关键意见领袖

第五章：药物研发市场中的人工智慧（按技术）

  • 机器学习
  • 自然语言处理
  • 生成式人工智慧
  • 其他的

第六章：人工智慧在药物研发市场的应用

  • 目标发现与验证
  • 热门药物发现与虚拟筛选
  • 先导化合物
  • 线索优化
  • 临床前测试
  • 临床试验
  • 其他的

第七章：人工智慧在药物研发市场的应用（按地区）

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 西班牙
    • 义大利
    • 欧洲其他地区
  • 南美洲
    • 巴西
    • 阿根廷
    • 南美洲其他地区
  • 亚太
    • 中国
    • 印度
    • 日本
    • 韩国
    • 亚太其他地区
  • 中东和非洲

第八章：竞争格局与市场定位

  • 竞争概况和主要市场参与者
  • 市占率分析与定位矩阵
  • 策略伙伴关係、併购
  • 产品组合和创新的关键发展
  • 公司基准化分析

第九章：公司简介

  • Alphabet (Google DeepMind)
    • 公司概况
    • 产品组合
      • 产品描述
      • 产品关键绩效指标 (KPI)
      • 历史和预测产品销售
      • 产品销售量
    • 财务概览
      • 公司收入
      • 地区收入份额
      • 收入预测
    • 关键进展
      • 併购
      • 关键产品开发活动
      • 监管部门批准等
    • SWOT分析
  • Atomwise Inc.
  • BenevolentAI
  • BioMap
  • BioSymetrics
  • DEEP GENOMICS.
  • Euretos.
  • Exscientia
  • IBM
  • Iktos.

第 10 章：附录

简介目录
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