人工智慧賦能罕見疾病藥物研發市場預測至2032年：全球藥物類型、適應症、技術、應用、最終用戶和區域分析

根據 Stratistics MRC 的一項研究，全球人工智慧賦能的罕見疾病藥物開發市場預計到 2025 年將達到 59 億美元，到 2032 年將達到 377 億美元，預測期內複合年成長率為 30.1%。

人工智慧賦能的罕見疾病藥物研發利用機器學習技術來辨識治療標靶、預測化合物療效，並加速罕見疾病和孤兒疾病的臨床試驗設計。透過分析基因組、蛋白質組和患者數據，人工智慧模型能夠揭示隱藏的模式，並實現現有藥物的再利用。這種方法降低了研發成本和時間，同時也提高了成功率。生技公司和研究機構正在利用這些工具來滿足尚未滿足的醫療需求，並改變小眾療法的研發方式，尤其是在商業性獎勵有限的疾病領域。

據美國國立衛生研究院稱，基於多組體學資料訓練的人工智慧模型正在獲得識別罕見遺傳疾病新藥物靶點的能力，這些疾病以前由於缺乏對其病理的了解而被認為是「無法治療的」。

機器學習演算法的進步

機器學習的快速發展正在革新罕見疾病藥物研發，實現更快、更精準的標靶辨識和化合物篩檢。人工智慧模型能夠分析複雜的基因組、蛋白質組和臨床資料集，從而發現新的治療途徑。這些演算法縮短了研發週期，並提高了早期藥物研發的成功率。隨著計算生物學和深度學習技術的日益成熟，製藥公司正積極採用人工智慧來應對治療選擇有限的罕見疾病，從而推動創新並擴大精準醫療的覆蓋範圍。

取得患者資料有困難

罕見疾病患者族群規模小，導致臨床和基因組資料集有限，這本身就為研究帶來了許多挑戰。資料匱乏阻礙了人工智慧模型的訓練、檢驗和泛化能力。不完整或零散的記錄會降低演算法的準確性，並延緩藥物研發進程。隱私法規和資料孤島進一步限制了高品質資料集的取得。克服這些限制需要全球數據共用舉措、合成數據生成以及與患者權益倡導組織的合作。如果數據可用性不足，人工智慧在罕見疾病藥物研發中的真正潛力將受到限制。

人工智慧公司與製藥公司之間的合作

人工智慧技術提供者與製藥公司之間的策略聯盟正在為罕見疾病藥物研發開闢新的可能性。這些合作將計算技術與臨床和監管洞察相結合，從而加速產品管線的開發。合資企業能夠共同取得專有資料集、化合物庫和疾病模型。隨著製藥公司尋求降低研發風險並提高投資回報率，人工智慧公司提供了可擴展的平台，用於標靶預測、分子設計和臨床試驗最佳化。這些合作正在重塑藥物發現流程，並擴大治療的可能性。

倫理和資料隱私問題

人工智慧驅動的藥物研發引發了倫理和隱私方面的擔憂，尤其是在罕見疾病領域，患者資料具有高度可識別性。濫用敏感的健康資訊、缺乏知情同意以及不透明的演算法決策都可能損害公眾信任。監管機構對資料管治、偏見緩解和可解釋性的審查日益嚴格，要求企業實施健全的資料保護通訊協定、透明的人工智慧模型和倫理審查框架。未能應對這些風險可能導致聲譽受損、法律訴訟以及相關人員信任的喪失。

新冠疫情的影響：

新冠疫情加速了人工智慧在藥物研發領域的應用，包括罕見疾病領域。臨床試驗和實驗室資源的受限促使研究人員轉向In Silico模擬和虛擬篩檢。人工智慧平台實現了遠端協作、快速假設檢驗以及現有化合物的再利用。此次危機凸顯了研發領域採用敏捷、數據驅動方法的重要性。疫情過後，人工智慧將繼續在重建穩健的藥物研發管線中發揮核心作用，從而推動對罕見疾病研究領域數位化創新的投資和監管支持不斷增加。

預計在預測期內，小分子藥物細分市場將佔據最大的市場佔有率。

由於小分子藥物擁有成熟的研發路徑、擴充性以及與人工智慧驅動篩檢的兼容性，預計在預測期內，小分子藥物領域將佔據最大的市場佔有率。這些化合物易於合成、修飾，並可透過計算模型進行測試。人工智慧能夠加速先導化合物的發現、毒性預測和藥物動力學最佳化。小分子藥物仍然是靶向細胞內通路和罕見基因突變的首選藥物。其成本效益和監管方面的便利性進一步推動了小分子藥物在人工智慧輔助的罕見疾病藥物研發中的廣泛應用。

預計在預測期內，罕見癌症領域將實現最高的複合年成長率。

在預測期內，受未被滿足的需求和基因組數據不斷成長的推動，罕見癌症領域預計將呈現最高的成長率。人工智慧工具正被擴大用於識別生物標記、對患者進行分層以及設計針對罕見癌症的標靶治療方案。多組體學整合和真實世界證據分析的進步正在促進個人化治療。隨著精準腫瘤學的擴展，人工智慧從有限的資料集中提取可操作見解的能力正在為罕見癌症研究做出貢獻。該領域的緊迫性和創新性正在推動其快速成長。

佔比最大的地區：

預計亞太地區將在預測期內佔據最大的市場佔有率，這主要得益於醫療保健投資的成長、生物技術生態系統的擴展以及政府主導的人工智慧舉措。中國、日本和韓國等國家正在將人工智慧融入其國家藥物研發計劃和罕見疾病登記系統中。區域內的製藥公司正與人工智慧Start-Ups合作，以加速產品管線的開發。該地區龐大的人口規模和不斷提高的罕見疾病診斷率正在進一步推動市場需求。亞太地區對數位醫療的積極態度正推動該地區成為市場領導。

預計年複合成長率最高的地區：

在預測期內，北美預計將實現最高的複合年成長率，這主要得益於其先進的人工智慧基礎設施、強大的製藥產業實力以及有利的法規環境。美國透過學術研究、創業投資和FDA試驗計畫，在人工智慧藥物研發領域處於領先地位。罕見疾病倡導組織和資料共用網路正在促進臨床試驗的招募和模型訓練。科技巨頭與製藥公司之間的合作正在加速創新。隨著精準醫療和孤兒藥研發的蓬勃發展，北美正在推動市場的快速擴張。

免費客製化服務：

購買此報告的客戶可以選擇以下免費自訂選項之一：

• 公司概況
  • 對其他市場公司（最多 3 家公司）進行全面分析
  • 主要企業SWOT分析（最多3家公司）
• 區域細分
  • 根據客戶要求，對主要國家的市場規模和複合年成長率進行估算和預測（註：可行性需確認）。
• 競爭基準化分析
  • 根據主要企業的產品系列、地理覆蓋範圍和策略聯盟基準化分析

目錄

第1章執行摘要

第2章 前言

• 概述
• 相關利益者
• 調查範圍
• 調查方法
  • 資料探勘
  • 數據分析
  • 數據檢驗
  • 研究途徑
• 研究材料
  • 原始研究資料
  • 次級研究資訊來源
  • 先決條件

第3章 市場趨勢分析

• 介紹
• 促進要素
• 抑制因素
• 機會
• 威脅
• 技術分析
• 應用分析
• 終端用戶分析
• 新興市場
• 新冠疫情的影響

第4章 波特五力分析

• 供應商的議價能力
• 買方的議價能力
• 替代品的威脅
• 新進入者的威脅
• 競爭對手之間的競爭

第5章 全球罕見疾病人工智慧藥物開發市場（依藥物類型分類）

• 介紹
• 小分子藥物
• 生物製藥
• 基因治療
• 基於RNA的療法

第6章 全球罕見疾病人工智慧藥物開發市場（依適應症分類）

• 介紹
• 神經肌肉疾病
• 罕見癌症
• 代謝紊亂
• 遺傳症候群
• 免疫疾病

7. 全球罕見疾病人工智慧藥物研發市場（依技術分類）

• 介紹
• 機器學習
• 深度學習
• 自然語言處理與生物資訊學
• 計算化學
• 知識圖譜建模

第8章 全球罕見疾病人工智慧藥物開發市場（按應用領域分類）

• 介紹
• 目標識別
• 藥物再利用
• 臨床試驗最佳化
• 生物標記發現

第9章 全球人工智慧賦能罕見疾病藥物開發市場（依最終用戶分類）

• 介紹
• 製藥公司
• 生技Start-Ups
• 研究所
• 合約研究組織

第10章 全球罕見疾病人工智慧藥物研發市場（按地區分類）

• 介紹
• 北美洲
  • 美國
  • 加拿大
  • 墨西哥
• 歐洲
  • 德國
  • 英國
  • 義大利
  • 法國
  • 西班牙
  • 其他歐洲
• 亞太地區
  • 日本
  • 中國
  • 印度
  • 澳洲
  • 紐西蘭
  • 韓國
  • 亞太其他地區
• 南美洲
  • 阿根廷
  • 巴西
  • 智利
  • 南美洲其他地區
• 中東和非洲
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • 卡達
  • 南非
  • 其他中東和非洲地區

第11章 重大進展

• 協議、夥伴關係、合作和合資企業
• 收購與併購
• 新產品上市
• 業務拓展
• 其他關鍵策略

第12章 企業概況

• 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.