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市场调查报告书
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1980048

AI驱动的药物发现市场预测至2034年：按组件、治疗领域、技术、应用、最终用户和地区进行全球分析。

AI For Drug Discovery Market Forecasts to 2034- Global Analysis By Component (Hardware, Software and Services), Therapeutic Area, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的研究，全球人工智慧驱动的药物发现市场预计将在 2026 年达到 29.3 亿美元，在预测期内以 24.8% 的复合年增长率增长，到 2034 年达到 172.5 亿美元。

人工智慧驱动的药物发现是指应用机器学习、深度学习和自然语言处理等先进的人工智慧技术来简化和增强药物发现过程。透过分析涵盖分子结构、生物路径以及临床试验结果等大量资料集，人工智慧模型可以预测化合物的疗效、识别潜在的药物标靶、优化分子设计并预测安全性。这缩短了研究週期，降低了成本，并提高了新疗法上市的成功率。它使製药和生物技术领域的药物发现更加精准、高效和数据驱动。

机器学习和深度学习的进展

机器学习和深度学习技术的快速发展是人工智慧驱动药物发现市场的主要驱动力。这些进步使得分析庞大而复杂的生物医学数据集成为可能，使人工智慧模型能够准确预测化合物疗效、优化分子设计并识别新的药物标靶。透过减少传统实验所需的时间和资源，这些技术提高了研究效率，增强了临床前和临床试验的决策能力，并加速了整个药物开发生命週期，惠及製药和生物技术领域。

高昂的实施成本

高昂的实施成本仍是人工智慧在药物研发领域应用的主要障碍。建构强大的人工智慧基础设施需要对硬体、软体和专业人员进行大量投资。中小製药公司往往难以调配必要的资金和技术资源。此外，将人工智慧整合到现有的研发流程中需要耗费大量时间和专业知识，这也将减缓其应用速度。这些成本障碍会限制人工智慧的广泛应用，尤其是在预算限制和基础设施不足的新兴市场。

对个人化医疗的需求日益增长

个人化医疗需求的日益增长为人工智慧在药物研发领域带来了巨大的机会。患者越来越希望获得根据自身基因谱和健康状况量身定制的治疗方法。人工智慧技术能够分析基因组学、蛋白质组学和临床数据，从而识别患者特异性的药物标靶并优化治疗效果。这种能力有助于精准医疗的发展，减少副作用，并改善治疗效果。製药和生物技术公司正在利用人工智慧来满足这一需求，并在这个不断成长且高度专业化的市场中占据竞争优势。

资料隐私和安全问题

资料隐私和安全问题对人工智慧驱动的药物研发构成重大威胁。该领域高度依赖敏感的患者和临床数据，包括基因组资讯、电子健康记录和临床试验结果。未授权存取和资料外洩会损害病患隐私，导致监管处罚，并损害机构声誉。确保强大的网路安全、遵守资料保护条例以及建立安全的资料共用机制至关重要。未能解决这些问题可能会阻碍人工智慧技术的应用，延缓合作研究，并削弱相关人员的信任。

新冠疫情的影响：

新冠疫情凸显了人工智慧在加速药物发现和疫苗研发方面的巨大潜力。在疫情危机中，人工智慧模型被用于快速识别潜在疗法并优化临床试验设计。儘管由于传统研究流程的中断，研发进度最初有所延误，但疫情也凸显了人工智慧在应对紧急公共卫生危机方面的价值。这加速了研发领域的数位转型，加强了技术提供者与製药公司之间的合作，并再次强调了在药物发现过程中对数据驱动型快速回应能力的需求。

在预测期内，机器人流程自动化 (RPA) 细分市场预计将占据最大的市场份额。

在预测期内，机器人流程自动化 (RPA) 预计将占据最大的市场份额。这主要归功于其能够简化重复性且耗时的任务。 RPA 可自动从各种来源资料提取，使研究人员能够专注于关键决策和复杂分析。 RPA 的应用提高了临床前和临床阶段工作流程的效率，并提升了生产力。製药和生物技术公司正在扩大 RPA 的应用范围，以加速药物发现过程，并在其药物开发专案中取得持续、高品质的成果。

在预测期内，药品滥用产业预计将呈现最高的复合年增长率。

在预测期内，药物重定位领域预计将呈现最高的成长率。这是因为该领域涉及分析分子结构和临床结果，以挖掘现有药物的新治疗用途。与新药研发相比，这种方法显着缩短了研发时间并降低了成本。快速应对新兴疾病和未满足的医疗需求的能力正在推动其进一步普及。製药公司正在利用人工智慧进行药物重定位，以有效地扩展其研发管线，增强市场竞争力，并加快患者获得有效治疗方法的速度。

市占率最大的地区：

在整个预测期内，北美预计将凭藉其强大的医药和生物技术生态系统保持最大的市场份额。该地区受益于先进的技术基础设施和对人工智慧创新技术的早期应用。领先的人工智慧解决方案供应商、支援性的法规结构以及科技公司与研究机构之间的合作，都在巩固其市场领导地位。高昂的医疗保健支出和对高性价比药物研发的需求，使北美能够保持其主导地位，并推动行业标准的製定和全球创新。

复合年增长率最高的地区：

在预测期内，由于技术的快速普及和政府的支持，亚太地区预计将呈现最高的复合年增长率。新兴经济体正迅速采用人工智慧来克服传统的研发挑战、缩短研发週期并提高药物疗效。製药製造地的扩张、临床试验的增加以及与全球人工智慧解决方案供应商的合作，都推动了市场的加速发展。该地区大规模的患者群体和成本效益高的商业环境为人工智慧驱动的药物倡议提供了巨大的成长潜力。

免费客製化服务：

购买此报告的客户可以选择以下免费自订选项之一：

  • 企业概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 主要参与者（最多3家公司）的SWOT分析
  • 区域细分
    • 主要国家的市场估算和预测，以及根据客户需求量身定制的复合年增长率（註：需要进行可行性测试）。
  • 竞争性标竿分析
    • 根据主要参与者的产品系列、地理覆盖范围和策略联盟进行基准分析。

目录

第一章执行摘要

  • 市场概览及主要亮点
  • 驱动因素、挑战与机会
  • 竞争格局概述
  • 战略洞察与建议

第二章：研究框架

  • 研究目标和范围
  • 相关人员分析
  • 研究假设和限制
  • 调查方法

第三章 市场动态与趋势分析

  • 市场定义与结构
  • 主要市场驱动因素
  • 市场限制与挑战
  • 投资成长机会和重点领域
  • 产业威胁与风险评估
  • 技术与创新展望
  • 新兴市场/高成长市场
  • 监管和政策环境
  • 新冠疫情的影响及復苏前景

第四章：竞争环境与策略评估

  • 波特五力分析
    • 供应商的议价能力
    • 买方的议价能力
    • 替代品的威胁
    • 新进入者的威胁
    • 竞争公司之间的竞争
  • 主要企业市占率分析
  • 产品基准评效和效能比较

第五章：全球人工智慧驱动药物发现市场：按组成部分划分

  • 硬体
  • 软体
  • 服务

第六章：全球人工智慧驱动药物发现市场：按治疗领域划分

  • 肿瘤学
  • 循环系统
  • 神经病学
  • 免疫学
  • 感染疾病
  • 罕见疾病

第七章：全球人工智慧驱动药物发现市场：按技术划分

  • 机器学习
  • 深度学习
  • 自然语言处理（NLP）
  • 机器人流程自动化 (RPA)
  • 其他技术

第八章：全球人工智慧驱动药物发现市场：按应用领域划分

  • 药物标靶识别
  • 药物发现设计与开发
  • 药物转换
  • 临床试验的优化
  • 生物标记发现

第九章：全球人工智慧驱动药物发现市场：按最终用户划分

  • 製药和生物技术公司
  • 受託研究机构（CRO）
  • 学术研究机构
  • 其他最终用户

第十章：全球人工智慧驱动药物发现市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 比利时
    • 瑞典
    • 瑞士
    • 波兰
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 泰国
    • 马来西亚
    • 新加坡
    • 越南
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥伦比亚
    • 智利
    • 秘鲁
    • 其他南美国家
  • 世界其他地区（RoW）
    • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 以色列
      • 其他中东国家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲国家

第十一章 策略市场资讯

  • 工业价值网络和供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十二章 产业趋势与策略倡议

  • 併购
  • 伙伴关係、联盟和合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十三章：公司简介

  • Insilico Medicine
  • BenevolentAI
  • Exscientia
  • Recursion Pharmaceuticals
  • Atomwise
  • Deep Genomics
  • Schrodinger, Inc.
  • NVIDIA Corporation
  • XtalPi
  • Iktos
  • Cloud Pharmaceuticals
  • Standigm
  • Cyclica
  • Isomorphic Labs
  • Gero
Product Code: SMRC34212

According to Stratistics MRC, the Global AI For Drug Discovery Market is accounted for $2.93 billion in 2026 and is expected to reach $17.25 billion by 2034 growing at a CAGR of 24.8% during the forecast period. AI for Drug Discovery refers to the application of advanced artificial intelligence technologies, including machine learning, deep learning, and natural language processing, to streamline and enhance the drug development process. By analyzing vast datasets from molecular structures and biological pathways to clinical trial results AI models can predict compound efficacy, identify potential drug targets, optimize molecular designs, and forecast safety profiles. This accelerates research timelines, reduces costs, and improves success rates in bringing novel therapeutics to market, enabling more precise, efficient, and data driven drug discovery across pharmaceuticals and biotechnology sectors.

Market Dynamics:

Driver:

Advances in Machine Learning & Deep Learning

The rapid evolution of machine learning and deep learning technologies is a key driver for the AI for Drug Discovery market. These advancements enable the analysis of vast and complex biomedical datasets, allowing AI models to accurately predict compound efficacy, optimize molecular designs, and identify novel drug targets. By reducing the time and resources required for traditional experimentation, these technologies enhance research productivity, improve decision making in preclinical and clinical studies, and accelerate the overall drug development lifecycle across pharmaceutical and biotechnology sectors.

Restraint:

High Implementation Costs

High implementation costs remain a significant restraint for the adoption of AI in drug discovery. Establishing robust AI infrastructures requires substantial investment in hardware, software, and specialized talent. Small and mid-sized pharmaceutical companies often face challenges in allocating the necessary financial and technical resources. Additionally, integrating AI into existing R&D workflows demands considerable time and expertise, which can slows adoption. These cost barriers can limit widespread deployment, particularly in emerging markets where budget constraints and infrastructure limitations persist.

Opportunity:

Growing Demand for Personalized Medicine

The rising demand for personalized medicine presents a substantial opportunity for AI in drug discovery. Patients increasingly seek therapies tailored to their genetic profiles and individual health conditions. AI technologies can analyze genomic, proteomic, and clinical data to identify patient specific drug targets and optimize therapeutic efficacy. This capability supports the development of precision medicines, reduces adverse effects, and enhances treatment outcomes. Pharmaceutical and biotechnology companies are leveraging AI to address this demand, positioning themselves to capitalize on a growing and highly specialized market.

Threat:

Data Privacy & Security Concerns

Data privacy and security concerns pose a significant threat to AI-driven drug discovery. The field relies heavily on sensitive patient and clinical data, including genomic information, electronic health records, and trial results. Unauthorized access or breaches could compromise patient confidentiality, lead to regulatory penalties, and damage organizational reputation. Ensuring robust cybersecurity, compliance with data protection regulations, and secure data-sharing mechanisms is critical. Failure to address these concerns can hinder the adoption of AI technologies, slow collaboration, and reduce confidence among stakeholders.

Covid-19 Impact:

The COVID-19 pandemic highlighted the potential of AI in accelerating drug discovery and vaccine development. During the crisis, AI models were employed to rapidly identify therapeutic candidates and optimize clinical trial designs. While disruptions to traditional research workflows initially slowed development timelines, the pandemic emphasized the value of AI in responding to urgent health crises. It accelerated digital adoption in R&D, strengthened partnerships between technology providers and pharmaceutical companies, and reinforced the need for data driven, rapid-response capabilities in drug discovery pipelines.

The robotics process automation (RPA) segment is expected to be the largest during the forecast period

The robotics process automation (RPA) segment is expected to account for the largest market share during the forecast period, due to its ability to streamline repetitive and time consuming tasks. RPA automates data extraction and processing from diverse sources, enabling researchers to focus on critical decision-making and complex analyses. Its implementation improves workflow efficiency and enhances productivity across preclinical and clinical stages. Pharmaceutical and biotechnology companies increasingly adopt RPA to accelerate discovery processes and achieve consistent, high quality results in drug development programs.

The drug repurposing segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the drug repurposing segment is predicted to witness the highest growth rate, because it identifies existing drugs with potential new therapeutic applications by analyzing molecular structures and clinical outcomes. This approach significantly reduces development time and costs compared to de novo drug discovery. The ability to rapidly respond to emerging diseases and unmet medical needs further drives adoption. Pharmaceutical companies are leveraging AI for drug repurposing to expand pipelines efficiently, enhance market competitiveness, and deliver faster patient access to effective therapies.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to strong pharmaceutical and biotechnology ecosystem. The region benefits from advanced technological infrastructure and early adoption of AI innovations. Presence of leading AI solution providers, supportive regulatory frameworks, and collaborations between tech companies and research institutions strengthen market leadership. High healthcare expenditure, with demand for cost effective drug development, enables North America to maintain dominance, shaping industry standards and driving innovation globally.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, owing to rapid technological adoption and supportive government initiatives. Emerging economies are increasingly embracing AI to overcome traditional R&D challenges, reduce development timelines, and enhance drug efficacy. Expansion of pharmaceutical manufacturing hubs, rising clinical trials, and collaborations with global AI solution providers contribute to market acceleration. The region's large patient population and cost effective operational landscape offer immense growth potential for AI-driven drug discovery initiatives.

Key players in the market

Some of the key players in AI For Drug Discovery Market include Insilico Medicine, BenevolentAI, Exscientia, Recursion Pharmaceuticals, Atomwise, Deep Genomics, Schrodinger, Inc., NVIDIA Corporation, XtalPi, Iktos, Cloud Pharmaceuticals, Standigm, Cyclica, Isomorphic Labs and Gero.

Key Developments:

In January 2026, NVIDIA and CoreWeave have deepened their partnership to accelerate the build-out of over 5 gigawatts of AI factories by 2030, backed by NVIDIA's $2 billion investment and aligned infrastructure and software efforts to scale AI compute globally.

In September 2025, OpenAI and NVIDIA unveiled a landmark strategic partnership to build and deploy at least 10 gigawatts of NVIDIA AI systems millions of GPUs for next-gen AI data centers, backed by up to $100 billion in phased investment starting in 2026.

Components Covered:

  • Hardware
  • Software
  • Services

Therapeutic Areas Covered:

  • Oncology
  • Cardiovascular
  • Neurology
  • Immunology
  • Infectious Diseases
  • Rare Diseases

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Robotics Process Automation (RPA)
  • Other Technologies

Applications Covered:

  • Drug Target Identification
  • Drug Design & Development
  • Drug Repurposing
  • Clinical Trial Optimization
  • Biomarker Discovery

End Users Covered:

  • Pharmaceutical & Biotechnology Companies
  • Contract Research Organizations (CROs)
  • Academic & Research Institutes
  • Other End Users

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI For Drug Discovery Market, By Component

  • 5.1 Hardware
  • 5.2 Software
  • 5.3 Services

6 Global AI For Drug Discovery Market, By Therapeutic Area

  • 6.1 Oncology
  • 6.2 Cardiovascular
  • 6.3 Neurology
  • 6.4 Immunology
  • 6.5 Infectious Diseases
  • 6.6 Rare Diseases

7 Global AI For Drug Discovery Market, By Technology

  • 7.1 Machine Learning
  • 7.2 Deep Learning
  • 7.3 Natural Language Processing (NLP)
  • 7.4 Robotics Process Automation (RPA)
  • 7.5 Other Technologies

8 Global AI For Drug Discovery Market, By Application

  • 8.1 Drug Target Identification
  • 8.2 Drug Design & Development
  • 8.3 Drug Repurposing
  • 8.4 Clinical Trial Optimization
  • 8.5 Biomarker Discovery

9 Global AI For Drug Discovery Market, By End User

  • 9.1 Pharmaceutical & Biotechnology Companies
  • 9.2 Contract Research Organizations (CROs)
  • 9.3 Academic & Research Institutes
  • 9.4 Other End Users

10 Global AI For Drug Discovery Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 Insilico Medicine
  • 13.2 BenevolentAI
  • 13.3 Exscientia
  • 13.4 Recursion Pharmaceuticals
  • 13.5 Atomwise
  • 13.6 Deep Genomics
  • 13.7 Schrodinger, Inc.
  • 13.8 NVIDIA Corporation
  • 13.9 XtalPi
  • 13.10 Iktos
  • 13.11 Cloud Pharmaceuticals
  • 13.12 Standigm
  • 13.13 Cyclica
  • 13.14 Isomorphic Labs
  • 13.15 Gero

List of Tables

  • Table 1 Global AI For Drug Discovery Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI For Drug Discovery Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI For Drug Discovery Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global AI For Drug Discovery Market Outlook, By Software (2023-2034) ($MN)
  • Table 5 Global AI For Drug Discovery Market Outlook, By Services (2023-2034) ($MN)
  • Table 6 Global AI For Drug Discovery Market Outlook, By Therapeutic Area (2023-2034) ($MN)
  • Table 7 Global AI For Drug Discovery Market Outlook, By Oncology (2023-2034) ($MN)
  • Table 8 Global AI For Drug Discovery Market Outlook, By Cardiovascular (2023-2034) ($MN)
  • Table 9 Global AI For Drug Discovery Market Outlook, By Neurology (2023-2034) ($MN)
  • Table 10 Global AI For Drug Discovery Market Outlook, By Immunology (2023-2034) ($MN)
  • Table 11 Global AI For Drug Discovery Market Outlook, By Infectious Diseases (2023-2034) ($MN)
  • Table 12 Global AI For Drug Discovery Market Outlook, By Rare Diseases (2023-2034) ($MN)
  • Table 13 Global AI For Drug Discovery Market Outlook, By Technology (2023-2034) ($MN)
  • Table 14 Global AI For Drug Discovery Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 15 Global AI For Drug Discovery Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 16 Global AI For Drug Discovery Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 17 Global AI For Drug Discovery Market Outlook, By Robotics Process Automation (RPA) (2023-2034) ($MN)
  • Table 18 Global AI For Drug Discovery Market Outlook, By Other Technologies (2023-2034) ($MN)
  • Table 19 Global AI For Drug Discovery Market Outlook, By Application (2023-2034) ($MN)
  • Table 20 Global AI For Drug Discovery Market Outlook, By Drug Target Identification (2023-2034) ($MN)
  • Table 21 Global AI For Drug Discovery Market Outlook, By Drug Design & Development (2023-2034) ($MN)
  • Table 22 Global AI For Drug Discovery Market Outlook, By Drug Repurposing (2023-2034) ($MN)
  • Table 23 Global AI For Drug Discovery Market Outlook, By Clinical Trial Optimization (2023-2034) ($MN)
  • Table 24 Global AI For Drug Discovery Market Outlook, By Biomarker Discovery (2023-2034) ($MN)
  • Table 25 Global AI For Drug Discovery Market Outlook, By End User (2023-2034) ($MN)
  • Table 26 Global AI For Drug Discovery Market Outlook, By Pharmaceutical & Biotechnology Companies (2023-2034) ($MN)
  • Table 27 Global AI For Drug Discovery Market Outlook, By Contract Research Organizations (CROs) (2023-2034) ($MN)
  • Table 28 Global AI For Drug Discovery Market Outlook, By Academic & Research Institutes (2023-2034) ($MN)
  • Table 29 Global AI For Drug Discovery Market Outlook, By Other End Users (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.