人工智慧赋能罕见疾病药物研发市场预测至2032年：全球药物类型、适应症、技术、应用、最终用户和区域分析

根据 Stratistics MRC 的一项研究，全球人工智慧赋能的罕见疾病药物开发市场预计到 2025 年将达到 59 亿美元，到 2032 年将达到 377 亿美元，预测期内复合年增长率为 30.1%。

人工智慧赋能的罕见疾病药物研发利用机器学习技术来辨识治疗标靶、预测化合物疗效，并加速罕见疾病和孤儿疾病的临床试验设计。透过分析基因组、蛋白质组和患者数据，人工智慧模型能够揭示隐藏的模式，并实现现有药物的再利用。这种方法降低了研发成本和时间，同时也提高了成功率。生技公司和研究机构正在利用这些工具来满足尚未满足的医疗需求，并改变小众疗法的研发方式，尤其是在商业性奖励有限的疾病领域。

据美国国立卫生研究院称，基于多组体学资料训练的人工智慧模型正在获得识别罕见遗传疾病新药物靶点的能力，这些疾病以前由于缺乏对其病理的了解而被认为是「无法治疗的」。

机器学习演算法的进步

机器学习的快速发展正在革新罕见疾病药物研发，实现更快、更精准的标靶辨识和化合物筛检。人工智慧模型能够分析复杂的基因组、蛋白质组和临床资料集，从而发现新的治疗途径。这些演算法缩短了研发週期，并提高了早期药物研发的成功率。随着计算生物学和深度学习技术的日益成熟，製药公司正积极采用人工智慧来应对治疗选择有限的罕见疾病，从而推动创新并扩大精准医疗的覆盖范围。

取得患者资料有困难

罕见疾病患者族群规模小，导致临床和基因组资料集有限，这本身就为研究带来了许多挑战。资料匮乏阻碍了人工智慧模型的训练、检验和泛化能力。不完整或零散的记录会降低演算法的准确性，并延缓药物研发进程。隐私法规和资料孤岛进一步限制了高品质资料集的取得。克服这些限制需要全球数据共用倡议、合成数据生成以及与患者权益倡导组织的合作。如果数据可用性不足，人工智慧在罕见疾病药物研发中的真正潜力将受到限制。

人工智慧公司与製药公司之间的合作

人工智慧技术提供者与製药公司之间的策略联盟正在为罕见疾病药物研发开闢新的可能性。这些合作将计算技术与临床和监管洞察相结合，从而加速产品管线的开发。合资企业能够共同取得专有资料集、化合物库和疾病模型。随着製药公司寻求降低研发风险并提高投资回报率，人工智慧公司提供了可扩展的平台，用于标靶预测、分子设计和临床试验优化。这些合作正在重塑药物发现流程，并扩大治疗的可能性。

伦理和资料隐私问题

人工智慧驱动的药物研发引发了伦理和隐私方面的担忧，尤其是在罕见疾病领域，患者资料具有高度可识别性。滥用敏感的健康资讯、缺乏知情同意以及不透明的演算法决策都可能损害公众信任。监管机构对资料管治、偏见缓解和可解释性的审查日益严格，要求企业实施健全的资料保护通讯协定、透明的人工智慧模型和伦理审查框架。未能应对这些风险可能导致声誉受损、法律诉讼以及相关人员信任的丧失。

新冠疫情的影响：

新冠疫情加速了人工智慧在药物研发领域的应用，包括罕见疾病领域。临床试验和实验室资源的受限促使研究人员转向In Silico模拟和虚拟筛检。人工智慧平台实现了远端协作、快速假设检验以及现有化合物的再利用。此次危机凸显了研发领域采用敏捷、数据驱动方法的重要性。疫情过后，人工智慧将继续在重建稳健的药物研发管线中发挥核心作用，从而推动对罕见疾病研究领域数位化创新的投资和监管支持不断增加。

预计在预测期内，小分子药物细分市场将占据最大的市场份额。

由于小分子药物拥有成熟的研发路径、扩充性以及与人工智慧驱动筛检的兼容性，预计在预测期内，小分子药物领域将占据最大的市场份额。这些化合物易于合成、修饰，并可透过计算模型进行测试。人工智慧能够加速先导化合物的发现、毒性预测和药物动力学优化。小分子药物仍然是靶向细胞内通路和罕见基因突变的首选药物。其成本效益和监管方面的便利性进一步推动了小分子药物在人工智慧辅助的罕见疾病药物研发中的广泛应用。

预计在预测期内，罕见癌症领域将实现最高的复合年增长率。

在预测期内，受未被满足的需求和基因组数据不断增长的推动，罕见癌症领域预计将呈现最高的成长率。人工智慧工具正被越来越多地用于识别生物标记、对患者进行分层以及设计针对罕见癌症的标靶治疗方案。多组体学整合和真实世界证据分析的进步正在促进个人化治疗。随着精准肿瘤学的扩展，人工智慧从有限的数据集中提取可操作见解的能力正在为罕见癌症研究做出贡献。该领域的紧迫性和创新性正在推动其快速成长。

占比最大的地区：

预计亚太地区将在预测期内占据最大的市场份额，这主要得益于医疗保健投资的成长、生物技术生态系统的扩展以及政府主导的人工智慧倡议。中国、日本和韩国等国家正在将人工智慧融入其国家药物研发计划和罕见疾病登记系统中。区域内的製药公司正与人工智慧Start-Ups合作，以加速产品管线的开发。该地区庞大的人口规模和不断提高的罕见疾病诊断率正在进一步推动市场需求。亚太地区对数位医疗的积极态度正推动该地区成为市场领导。

预计年复合成长率最高的地区：

在预测期内，北美预计将实现最高的复合年增长率，这主要得益于其先进的人工智慧基础设施、强大的製药产业实力以及有利的法规环境。美国透过学术研究、创业投资和FDA试验计画，在人工智慧药物研发领域处于领先地位。罕见疾病倡导组织和资料共用网路正在促进临床试验的招募和模型训练。科技巨头与製药公司之间的合作正在加速创新。随着精准医疗和孤儿药研发的蓬勃发展，北美正在推动市场的快速扩张。

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目录

第一章执行摘要

第二章 前言

• 概述
• 相关利益者
• 调查范围
• 调查方法
  • 资料探勘
  • 数据分析
  • 数据检验
  • 研究途径
• 研究材料
  • 原始研究资料
  • 次级研究资讯来源
  • 先决条件

第三章 市场趋势分析

• 介绍
• 司机
• 抑制因素
• 机会
• 威胁
• 技术分析
• 应用分析
• 终端用户分析
• 新兴市场
• 新冠疫情的影响

第四章 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
• 竞争对手之间的竞争

第五章 全球罕见疾病人工智慧药物开发市场（依药物类型划分）

• 介绍
• 小分子药物
• 生物製药
• 基因治疗
• 基于RNA的疗法

第六章 全球罕见疾病人工智慧药物开发市场（依适应症划分）

• 介绍
• 神经肌肉疾病
• 罕见癌症
• 代谢紊乱
• 遗传症候群
• 免疫疾病

7. 全球罕见疾病人工智慧药物研发市场（依技术划分）

• 介绍
• 机器学习
• 深度学习
• 自然语言处理与生物资讯学
• 计算化学
• 知识图谱建模

第八章 全球罕见疾病人工智慧药物开发市场（按应用领域划分）

• 介绍
• 目标识别
• 药物再利用
• 临床试验优化
• 生物标记发现

第九章 全球人工智慧赋能罕见疾病药物开发市场（依最终用户划分）

• 介绍
• 製药公司
• 生技Start-Ups
• 研究所
• 合约研究组织

第十章 全球罕见疾病人工智慧药物研发市场（按地区划分）

• 介绍
• 北美洲
  • 美国
  • 加拿大
  • 墨西哥
• 欧洲
  • 德国
  • 英国
  • 义大利
  • 法国
  • 西班牙
  • 其他欧洲
• 亚太地区
  • 日本
  • 中国
  • 印度
  • 澳洲
  • 纽西兰
  • 韩国
  • 亚太其他地区
• 南美洲
  • 阿根廷
  • 巴西
  • 智利
  • 南美洲其他地区
• 中东和非洲
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 卡达
  • 南非
  • 其他中东和非洲地区

第十一章 重大进展

• 协议、伙伴关係、合作和合资企业
• 收购与併购
• 新产品上市
• 业务拓展
• 其他关键策略

第十二章 企业概况

• NVIDIA
• Insilico Medicine
• Exscientia
• BenevolentAI
• Google
• Recursion Pharmaceuticals
• Atomwise
• Sanofi
• Roche
• Moderna
• Genentech
• Pfizer
• IBM
• AstraZeneca
• CytoReason
• BioNTech
• Takeda
• Novartis

According to Stratistics MRC, the Global AI-Driven Rare-Disease Drug-Discovery Market is accounted for $5.9 billion in 2025 and is expected to reach $37.7 billion by 2032 growing at a CAGR of 30.1% during the forecast period. AI-Driven Rare-Disease Drug Discovery uses machine learning to identify therapeutic targets, predict compound efficacy, and accelerate clinical trial design for rare and orphan diseases. By analyzing genomic, proteomic, and patient data, AI models uncover hidden patterns and repurpose existing drugs. This approach reduces R&D costs and timelines while improving success rates. Biotech firms and research institutions leverage these tools to address unmet medical needs, especially in conditions with limited commercial incentives, transforming how niche therapeutics are developed.

According to the National Institutes of Health, AI models trained on multi-omics data are now capable of identifying novel drug targets for rare genetic disorders that were previously considered "undruggable" due to a lack of understanding of their underlying pathology.

Market Dynamics:

Driver:

Advancements in machine learning algorithms

Rapid progress in machine learning is revolutionizing rare-disease drug discovery by enabling faster, more accurate target identification and compound screening. AI models can analyze complex genomic, proteomic, and clinical datasets to uncover novel therapeutic pathways. These algorithms reduce R&D timelines and improve success rates in early-stage drug development. As computational biology and deep learning techniques mature, pharmaceutical companies are increasingly integrating AI to address rare diseases with limited treatment options, driving innovation and expanding the scope of precision medicine.

Restraint:

Limited availability of patient datasets

Rare diseases inherently suffer from small patient populations, resulting in limited clinical and genomic datasets. This data scarcity hampers AI model training, validation, and generalizability. Incomplete or fragmented records reduce algorithmic accuracy and slow drug development. Privacy regulations and data silos further restrict access to high-quality datasets. Overcoming this restraint requires global data-sharing initiatives, synthetic data generation, and partnerships with patient advocacy groups. Without expanded data availability, AI's full potential in rare-disease drug discovery remains constrained.

Opportunity:

Collaborations between AI firms and pharma

Strategic partnerships between AI technology providers and pharmaceutical companies are unlocking new opportunities in rare-disease drug discovery. These collaborations combine computational expertise with clinical and regulatory know-how, accelerating pipeline development. Joint ventures enable shared access to proprietary datasets, compound libraries, and disease models. As pharma seeks to de-risk R&D and improve ROI, AI firms offer scalable platforms for target prediction, molecule design, and trial optimization. Such alliances are reshaping drug discovery workflows and expanding therapeutic possibilities.

Threat:

Ethical and data privacy concerns

AI-driven drug discovery raises ethical and privacy concerns, especially in rare diseases where patient data is highly identifiable. Misuse of sensitive health information, lack of informed consent, and opaque algorithmic decisions can erode trust. Regulatory scrutiny around data governance, bias mitigation, and explainability is intensifying. Companies must implement robust data protection protocols, transparent AI models, and ethical review frameworks. Failure to address these risks may lead to reputational damage, legal challenges, and reduced stakeholder confidence.

Covid-19 Impact:

The COVID-19 pandemic accelerated adoption of AI in drug discovery, including rare diseases. Disruptions in clinical trials and lab access prompted a shift toward in silico modeling and virtual screening. AI platforms enabled remote collaboration, rapid hypothesis testing, and repurposing of existing compounds. The crisis highlighted the need for agile, data-driven R&D approaches. Post-pandemic, AI continues to play a central role in rebuilding resilient drug pipelines, with increased investment and regulatory support for digital innovation in rare-disease research.

The small molecule drugs segment is expected to be the largest during the forecast period

The small molecule drugs segment is expected to account for the largest market share during the forecast period, due to its established development pathways, scalability, and compatibility with AI-driven screening. These compounds are easier to synthesize, modify, and test using computational models. AI accelerates lead identification, toxicity prediction, and optimization of pharmacokinetics. Small molecules remain the preferred modality for targeting intracellular pathways and rare genetic mutations. Their cost-effectiveness and regulatory familiarity further support widespread adoption in AI-assisted rare-disease drug development.

The rare cancers segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the rare cancers segment is predicted to witness the highest growth rate, driven by unmet clinical needs and growing genomic data availability. AI tools are increasingly used to identify biomarkers, stratify patients, and design targeted therapies for rare oncology indications. Advances in multi-omics integration and real-world evidence analysis enhance treatment personalization. As precision oncology expands, rare cancer research benefits from AI's ability to uncover actionable insights from limited datasets. This segment's urgency and innovation potential fuel rapid growth.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, supported by rising healthcare investments, expanding biotech ecosystems, and government-led AI initiatives. Countries like China, Japan, and South Korea are integrating AI into national drug discovery programs and rare-disease registries. Regional pharma companies are partnering with AI startups to accelerate pipeline development. The region's large population base and increasing rare-disease diagnosis rates further drive demand. Asia Pacific's proactive stance on digital health positions it as a market leader.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR due to its advanced AI infrastructure, strong pharmaceutical presence, and supportive regulatory environment. The U.S. leads in AI-driven drug discovery through academic research, venture capital funding, and FDA pilot programs. Rare-disease advocacy groups and data-sharing networks enhance clinical trial recruitment and model training. Collaborations between tech giants and pharma firms are accelerating innovation. As precision medicine and orphan drug development gain momentum, North America drives rapid market expansion.

Key players in the market

Some of the key players in AI-Driven Rare-Disease Drug-Discovery Market include NVIDIA, Insilico Medicine, Exscientia, BenevolentAI, Google, Recursion Pharmaceuticals, Atomwise, Sanofi, Roche, Moderna, Genentech, Pfizer, IBM, AstraZeneca, CytoReason, BioNTech, Takeda and Novartis.

Key Developments:

In October 2025, Insilico Medicine announced the first AI-discovered novel target for a rare fibrosis disease has entered Phase I trials, potentially cutting years from the traditional discovery timeline.

In September 2025, NVIDIA and Recursion Pharmaceuticals expanded their collaboration, launching a new AI supercomputer platform to map the cellular biology of hundreds of poorly understood rare genetic disorders.

In August 2025, a consortium led by AstraZeneca and BenevolentAI initiated a $250 million project to apply their AI knowledge graphs to de-risk and accelerate the development of rare neurological disease therapies.

Drug Types Covered:

• Small Molecule Drugs
• Biologics
• Gene Therapies
• RNA-Based Therapeutics

Indications Covered:

• Neuromuscular Disorders
• Rare Cancers
• Metabolic Disorders
• Genetic Syndromes
• Immunological Disorders

Technologies Covered:

• Machine Learning
• Deep Learning
• NLP & Bioinformatics
• Computational Chemistry
• Knowledge Graph Modeling

Applications Covered:

• Target Identification
• Drug Repurposing
• Clinical Trial Optimization
• Biomarker Discovery

End Users Covered:

• Pharmaceutical Companies
• Biotechnology Startups
• Research Institutions
• Contract Research Organizations

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & 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 2024, 2025, 2026, 2028, and 2032
• 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

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Technology Analysis
• 3.7 Application Analysis
• 3.8 End User Analysis
• 3.9 Emerging Markets
• 3.10 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global AI-Driven Rare-Disease Drug-Discovery Market, By Drug Type

• 5.1 Introduction
• 5.2 Small Molecule Drugs
• 5.3 Biologics
• 5.4 Gene Therapies
• 5.5 RNA-Based Therapeutics

6 Global AI-Driven Rare-Disease Drug-Discovery Market, By Indication

• 6.1 Introduction
• 6.2 Neuromuscular Disorders
• 6.3 Rare Cancers
• 6.4 Metabolic Disorders
• 6.5 Genetic Syndromes
• 6.6 Immunological Disorders

7 Global AI-Driven Rare-Disease Drug-Discovery Market, By Technology

• 7.1 Introduction
• 7.2 Machine Learning
• 7.3 Deep Learning
• 7.4 NLP & Bioinformatics
• 7.5 Computational Chemistry
• 7.6 Knowledge Graph Modeling

8 Global AI-Driven Rare-Disease Drug-Discovery Market, By Application

• 8.1 Introduction
• 8.2 Target Identification
• 8.3 Drug Repurposing
• 8.4 Clinical Trial Optimization
• 8.5 Biomarker Discovery

9 Global AI-Driven Rare-Disease Drug-Discovery Market, By End User

• 9.1 Introduction
• 9.2 Pharmaceutical Companies
• 9.3 Biotechnology Startups
• 9.4 Research Institutions
• 9.5 Contract Research Organizations

10 Global AI-Driven Rare-Disease Drug-Discovery Market, By Geography

• 10.1 Introduction
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
  • 10.2.3 Mexico
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 Italy
  • 10.3.4 France
  • 10.3.5 Spain
  • 10.3.6 Rest of Europe
• 10.4 Asia Pacific
  • 10.4.1 Japan
  • 10.4.2 China
  • 10.4.3 India
  • 10.4.4 Australia
  • 10.4.5 New Zealand
  • 10.4.6 South Korea
  • 10.4.7 Rest of Asia Pacific
• 10.5 South America
  • 10.5.1 Argentina
  • 10.5.2 Brazil
  • 10.5.3 Chile
  • 10.5.4 Rest of South America
• 10.6 Middle East & Africa
  • 10.6.1 Saudi Arabia
  • 10.6.2 UAE
  • 10.6.3 Qatar
  • 10.6.4 South Africa
  • 10.6.5 Rest of Middle East & Africa

11 Key Developments

• 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 11.2 Acquisitions & Mergers
• 11.3 New Product Launch
• 11.4 Expansions
• 11.5 Other Key Strategies

12 Company Profiling

• 12.1 NVIDIA
• 12.2 Insilico Medicine
• 12.3 Exscientia
• 12.4 BenevolentAI
• 12.5 Google
• 12.6 Recursion Pharmaceuticals
• 12.7 Atomwise
• 12.8 Sanofi
• 12.9 Roche
• 12.10 Moderna
• 12.11 Genentech
• 12.12 Pfizer
• 12.13 IBM
• 12.14 AstraZeneca
• 12.15 CytoReason
• 12.16 BioNTech
• 12.17 Takeda
• 12.18 Novartis

List of Tables

• Table 1 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Region (2024-2032) ($MN)
• Table 2 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Drug Type (2024-2032) ($MN)
• Table 3 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Small Molecule Drugs (2024-2032) ($MN)
• Table 4 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Biologics (2024-2032) ($MN)
• Table 5 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Gene Therapies (2024-2032) ($MN)
• Table 6 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By RNA-Based Therapeutics (2024-2032) ($MN)
• Table 7 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Indication (2024-2032) ($MN)
• Table 8 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Neuromuscular Disorders (2024-2032) ($MN)
• Table 9 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Rare Cancers (2024-2032) ($MN)
• Table 10 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Metabolic Disorders (2024-2032) ($MN)
• Table 11 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Genetic Syndromes (2024-2032) ($MN)
• Table 12 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Immunological Disorders (2024-2032) ($MN)
• Table 13 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Technology (2024-2032) ($MN)
• Table 14 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Machine Learning (2024-2032) ($MN)
• Table 15 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Deep Learning (2024-2032) ($MN)
• Table 16 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By NLP & Bioinformatics (2024-2032) ($MN)
• Table 17 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Computational Chemistry (2024-2032) ($MN)
• Table 18 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Knowledge Graph Modeling (2024-2032) ($MN)
• Table 19 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Application (2024-2032) ($MN)
• Table 20 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Target Identification (2024-2032) ($MN)
• Table 21 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Drug Repurposing (2024-2032) ($MN)
• Table 22 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Clinical Trial Optimization (2024-2032) ($MN)
• Table 23 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Biomarker Discovery (2024-2032) ($MN)
• Table 24 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By End User (2024-2032) ($MN)
• Table 25 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Pharmaceutical Companies (2024-2032) ($MN)
• Table 26 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Biotechnology Startups (2024-2032) ($MN)
• Table 27 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Research Institutions (2024-2032) ($MN)
• Table 28 Global AI-Driven Rare-Disease Drug-Discovery Market Outlook, By Contract Research Organizations (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.