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市場調查報告書
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1980048

AI驅動的藥物發現市場預測至2034年：按組件、治療領域、技術、應用、最終用戶和地區進行全球分析。

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個工作天內

價格

根據 Stratistics MRC 的研究，全球人工智慧驅動的藥物發現市場預計將在 2026 年達到 29.3 億美元，在預測期內以 24.8% 的複合年成長率成長，到 2034 年達到 172.5 億美元。

人工智慧驅動的藥物發現是指應用機器學習、深度學習和自然語言處理等先進的人工智慧技術來簡化和增強藥物發現過程。透過分析涵蓋分子結構、生物路徑以及臨床試驗結果等大量資料集，人工智慧模型可以預測化合物的療效、識別潛在的藥物標靶、最佳化分子設計並預測安全性。這縮短了研究週期，降低了成本，並提高了新療法上市的成功率。它使製藥和生物技術領域的藥物發現更加精準、高效和數據驅動。

機器學習和深度學習的進展

機器學習和深度學習技術的快速發展是人工智慧驅動藥物發現市場的主要驅動力。這些進步使得分析龐大而複雜的生物醫學資料整合為可能，使人工智慧模型能夠準確預測化合物療效、最佳化分子設計並識別新的藥物標靶。透過減少傳統實驗所需的時間和資源，這些技術提高了研究效率，增強了臨床前和臨床試驗的決策能力，並加速了整個藥物開發生命週期，惠及製藥和生物技術領域。

高昂的實施成本

高昂的實施成本仍是人工智慧在藥物研發領域應用的主要障礙。建構強大的人工智慧基礎設施需要對硬體、軟體和專業人員進行大量投資。中小製藥公司往往難以調配必要的資金和技術資源。此外，將人工智慧整合到現有的研發流程中需要耗費大量時間和專業知識，這也將減緩其應用速度。這些成本障礙會限制人工智慧的廣泛應用，尤其是在預算限制和基礎設施不足的新興市場。

對個人化醫療的需求日益成長

個人化醫療需求的日益成長為人工智慧在藥物研發領域帶來了巨大的機會。患者越來越希望獲得根據自身基因譜和健康狀況量身定做的治療方法。人工智慧技術能夠分析基因組學、蛋白質組學和臨床數據，從而識別患者特異性的藥物標靶並最佳化治療效果。這種能力有助於精準醫療的發展，減少副作用，並改善治療效果。製藥和生物技術公司正在利用人工智慧來滿足這一需求，並在這個不斷成長且高度專業化的市場中佔據競爭優勢。

資料隱私和安全問題

資料隱私和安全問題對人工智慧驅動的藥物研發構成重大威脅。該領域高度依賴敏感的患者和臨床數據，包括基因組資訊、電子健康記錄和臨床試驗結果。未授權存取和資料外洩會損害病患隱私，導致監管處罰，並損害機構聲譽。確保強大的網路安全、遵守資料保護條例以及建立安全的資料共用機制至關重要。未能解決這些問題可能會阻礙人工智慧技術的應用，延緩合作研究，並削弱相關人員的信任。

新冠疫情的影響：

新冠疫情凸顯了人工智慧在加速藥物發現和疫苗研發方面的巨大潛力。在疫情危機中，人工智慧模型被用於快速識別潛在療法並最佳化臨床試驗設計。儘管由於傳統研究流程的中斷，研發進度最初有所延誤，但疫情也凸顯了人工智慧在應對緊急公共衛生危機方面的價值。這加速了研發領域的數位轉型，加強了技術提供者與製藥公司之間的合作，並再次強調了在藥物發現過程中對數據驅動型快速回應能力的需求。

在預測期內，機器人流程自動化 (RPA) 細分市場預計將佔據最大的市場佔有率。

在預測期內，機器人流程自動化 (RPA) 預計將佔據最大的市場佔有率。這主要歸功於其能夠簡化重複性且耗時的任務。 RPA 可自動從各種來源資料提取，使研究人員能夠專注於關鍵決策和複雜分析。 RPA 的應用提高了臨床前和臨床階段工作流程的效率，並提升了生產力。製藥和生物技術公司正在擴大 RPA 的應用範圍，以加速藥物發現過程，並在其藥物開發專案中取得持續、高品質的成果。

在預測期內，藥品濫用產業預計將呈現最高的複合年成長率。

在預測期內，藥物重定位領域預計將呈現最高的成長率。這是因為該領域涉及分析分子結構和臨床結果，以挖掘現有藥物的新治療用途。與新藥研發相比，這種方法顯著縮短了研發時間並降低了成本。快速應對新興疾病和未滿足的醫療需求的能力正在推動其進一步普及。製藥公司正在利用人工智慧進行藥物重定位，以有效地擴展其研發管線，增強市場競爭力，並加快患者獲得有效治療方法的速度。

市佔率最大的地區：

在整個預測期內，北美預計將憑藉其強大的醫藥和生物技術生態系統保持最大的市場佔有率。該地區受益於先進的技術基礎設施和對人工智慧創新技術的早期應用。領先的人工智慧解決方案供應商、支援性的法規結構以及科技公司與研究機構之間的合作，都在鞏固其市場領導地位。高昂的醫療保健支出和對具成本效益藥物研發的需求，使北美能夠保持其主導地位，並推動行業標準的製定和全球創新。

複合年成長率最高的地區：

在預測期內，由於技術的快速普及和政府的支持，亞太地區預計將呈現最高的複合年成長率。新興經濟體正迅速採用人工智慧來克服傳統的研發挑戰、縮短研發週期並提高藥物療效。製藥製造地的擴張、臨床試驗的增加以及與全球人工智慧解決方案供應商的合作，都推動了市場的加速發展。該地區大規模的患者群體和成本效益高的商業環境為人工智慧驅動的藥物舉措提供了巨大的成長潛力。

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：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 主要參與者（最多3家公司）的SWOT分析
  • 區域細分
    • 主要國家的市場估算和預測，以及根據客戶需求量身定做的複合年成長率（註：需要進行可行性測試）。
  • 競爭性標竿分析
    • 根據主要參與者的產品系列、地理覆蓋範圍和策略聯盟進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 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
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧驅動藥物發現市場：按組成部分分類

  • 硬體
  • 軟體
  • 服務

第6章：全球人工智慧驅動藥物發現市場：按治療領域分類

  • 腫瘤學
  • 循環系統
  • 神經病學
  • 免疫學
  • 感染疾病
  • 罕見疾病

第7章：全球人工智慧驅動藥物發現市場：按技術分類

  • 機器學習
  • 深度學習
  • 自然語言處理（NLP）
  • 機器人流程自動化 (RPA)
  • 其他技術

第8章：全球人工智慧驅動藥物發現市場：按應用領域分類

  • 藥物標靶識別
  • 藥物發現設計與開發
  • 藥物轉換
  • 臨床試驗的最佳化
  • 生物標記發現

第9章：全球人工智慧驅動藥物發現市場：按最終用戶分類

  • 製藥和生物技術公司
  • 受託研究機構（CRO）
  • 學術研究機構
  • 其他最終用戶

第10章：全球人工智慧驅動藥物發現市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • 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.