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市場調查報告書
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1776725

2032 年藥物研發市場 AI 預測：按類型、藥物類型、產品、技術、應用、最終用戶和地區進行全球分析

AI in Drug Discovery Market Forecasts to 2032 - Global Analysis By Type (Preclinical and Clinical Testing, Molecule Screening, Target Identification and De Novo Drug Design), Drug Type, Offering, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球藥物研發人工智慧市場規模預計在 2025 年將達到 26 億美元，到 2032 年將達到 178 億美元，預測期內的複合年成長率為 31.7%。

藥物研發中的人工智慧 (AI) 是指應用機器學習和數據驅動演算法來加速和最佳化開發新藥流程。 AI 可以分析從分子結構到臨床試驗結果的大量資料集，從而識別有潛力的候選藥物，預測藥物與標靶的相互作用，甚至設計新型化合物。 AI 可以減少傳統藥物研發方法所需的時間、成本和失敗率。透過模擬生物系統並從現有數據中學習，AI 可以幫助研究人員發現規律並做出更準確的決策。

根據世界衛生組織估計，2022年全球將新增2,000萬癌症病例，並有970萬人死於癌症。

研發成本上升與時間壓力

不斷上升的研發成本和時間壓力正在加速人工智慧在藥物研發中的應用，並成為創新的催化劑。這些挑戰迫使製藥公司採用人工智慧主導的解決方案，以簡化標靶識別、最佳化臨床試驗並減少代價高昂的失敗。因此，人工智慧可以提高研發效率、縮短開發時間並提高成功率。這種迫切性正在推動對智慧技術的投資，以改變傳統的工作流程，實現更快、更具成本效益的藥物開發，從而滿足日益成長的醫療保健需求。

缺乏標準化、高品質的數據

缺乏標準化、高品質的數據嚴重阻礙了人工智慧在藥物研發中的有效性。不一致的格式、不完整的註釋以及偏差的資料集損害了模型的準確性和可重複性。這些數據挑戰導致預測錯誤、開發成本增加以及研發進度延遲。缺乏統一的數據，人工智慧難以識別可行的候選藥物或可靠地預測結果，這限制了其變革潛力，並擴大了研究創新與現實世界藥物應用之間的差距。

生物醫學數據的爆炸性成長

生物醫學數據的爆炸性成長正推動人工智慧主導的藥物研發實現變革性飛躍。來自基因組學、蛋白質組學和臨床記錄的海量資料集使人工智慧模型能夠發現隱藏的模式，預測藥物-標靶相互作用，並加速先導化合物的識別。這些豐富的數據提高了準確性，減少了試驗，並支持個人化醫療。因此，藥物研發變得更快、更有效率、更具成本效益。巨量資料與人工智慧的協同作用有望將藥物研發轉變為一個更智慧、數據驅動的前沿領域。

實施成本高

高昂的實施成本是人工智慧在藥物研發中的應用面臨的一大障礙，尤其對於中小型製藥公司而言。這些費用包括先進的基礎設施、熟練的人員以及持續的系統維護。這些財務障礙減緩了整合速度，限制了創新，並擴大了大公司與新興企業之間的差距。因此，人工智慧的潛力尚未得到充分開發，阻礙了更快、更具成本效益和個人化治療方案的開發。

COVID-19的影響

新冠疫情顯著加速了人工智慧在藥物研發中的應用，因為製藥公司迫切需要更快速、更具成本效益的解決方案。人工智慧工具在識別治療標靶、藥物再利用和最佳化疫苗開發方面發揮了關鍵作用。這種需求激增，促使整個研發開發平臺中對人工智慧平台的投資、合作和整合不斷增加。這場疫情最終凸顯了人工智慧的變革潛力，使其成為未來製藥創新和危機應變的關鍵資產。

預計腫瘤學將成為預測期內最大的領域

由於對精準個人化癌症治療的迫切需求，預計腫瘤學領域將在預測期內佔據最大的市場佔有率。人工智慧將加速生物標記的發現，預測治療反應，並增強臨床試驗設計，特別是在肺癌和乳癌等複雜癌症領域。腫瘤學在人工智慧藥物研發投資中佔比最大，將推動標靶治療和免疫腫瘤學的創新。這種協同效應將提高成功率，縮短研發週期，使人工智慧成為癌症研究和治療領域的變革力量。

預計深度學習領域在預測期內將以最高複合年成長率成長

深度學習領域預計將在預測期內實現最高成長率，因為它能夠快速分析複雜的生物醫學數據。對複雜生物相互作用進行建模的能力可以加速標靶識別，最佳化化合物篩檢，並增強藥物的全新設計。深度學習透過提高預測準確性和最大限度地減少試驗失敗，從而縮短開發時間和降低成本。隨著製藥公司擴大機會這些模型，它將實現可擴展的數據驅動型創新，使藥物研發成為一個更快、更準確、更具成本效益的過程。

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，這得益於其強大的研發生態系統、政府支持以及新興企業的蓬勃發展。中國、印度和日本等國家正在利用人工智慧加速臨床試驗、降低成本並增強精準醫療。憑藉龐大的基因組資料集和數位基礎設施，該地區正在推動腫瘤學、免疫學和罕見疾病領域的創新。這一勢頭使亞太地區成為將藥物開發流程轉變為更快速、更聰明、更便利的全球領導者。

複合年成長率最高的地區：

預計北美將在預測期內實現最高的複合年成長率，因為該地區憑藉其強大的製藥基礎設施和先進的技術創新者，引領全球人工智慧應用。人工智慧能夠實現快速化合物篩檢、預測建模和個人化醫療開發。生物技術公司與人工智慧新興企業之間的策略合作夥伴關係正在激發創新，而監管機構的支持則促進了成長。這種協同效應使北美成為人工智慧賦能製藥突破的強國，推動了市場的快速成長。

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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
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

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

第5章全球藥物發現人工智慧市場（按類型）

  • 臨床前和臨床試驗
  • 分子篩檢
  • 目標識別
  • 從頭藥物設計

第6章 全球藥物發現人工智慧市場（以藥物類型）

  • 小分子
  • 高分子

第7章 全球藥物研發人工智慧市場（按產品）

  • 軟體
  • 服務

第8章 全球藥物研發人工智慧市場（按技術）

  • 機器學習
    • 監督學習
    • 強化學習
    • 無監督學習
  • 深度學習
  • 自然語言處理（NLP）
  • 其他技術

第9章全球藥物研發人工智慧市場（按應用）

  • 腫瘤學
  • 神經病學
  • 感染疾病
  • 心血管疾病
  • 代謝疾病
  • 免疫學
  • 其他用途

第 10 章全球藥物發現 AI 市場（以最終用戶分類）

  • 製藥公司
  • 生技公司
  • 學術研究機構
  • 合約研究組織（CRO）
  • 其他最終用戶

第 11 章全球藥物發現 AI 市場（按地區）

  • 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
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 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12章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第13章：企業概況

  • Atomwise, Inc.
  • BenevolentAI
  • Insilico Medicine
  • Exscientia Ltd.
  • Recursion Pharmaceuticals
  • BioXcel Therapeutics
  • Deep Genomics
  • Cloud Pharmaceuticals
  • Numerate, Inc.
  • Cyclica Inc.
  • Iktos
  • Evaxion Biotech
  • BERG LLC
  • Verge Genomics
  • Healx
  • PathAI
  • NVIDIA Corporation
  • IBM Watson Health
  • Google DeepMind
  • Schrodinger, Inc.
Product Code: SMRC30056

According to Stratistics MRC, the Global AI in Drug Discovery Market is accounted for $2.6 billion in 2025 and is expected to reach $17.8 billion by 2032 growing at a CAGR of 31.7% during the forecast period. Artificial Intelligence (AI) in drug discovery refers to the application of machine learning and data-driven algorithms to accelerate and optimize the process of developing new drugs. AI can analyze vast datasets-from molecular structures to clinical trial results-to identify promising drug candidates, predict drug-target interactions, and even design novel compounds. It reduces the time, cost, and failure rate associated with traditional drug development methods. By simulating biological systems and learning from existing data, AI helps researchers uncover patterns and make decisions with greater precision.

According to the estimates by WHO, in 2022, 20 million new cancer cases and 9.7 million deaths were reported globally.

Market Dynamics:

Driver:

Rising R&D Costs and Time Pressure

Rising R&D costs and time pressure are accelerating the adoption of AI in drug discovery, acting as catalysts for innovation. These challenges push pharmaceutical companies to embrace AI-driven solutions that streamline target identification, optimize clinical trials, and reduce costly failures. As a result, AI enhances R&D productivity, shortens development timelines, and improves success rates. This urgency fosters investment in intelligent technologies, transforming traditional workflows and enabling faster, more cost-effective drug development to meet growing healthcare demands.

Restraint:

Lack of Standardized, High-Quality Data

The lack of standardized, high-quality data severely hampers AI's effectiveness in drug discovery. Inconsistent formats, incomplete annotations, and biased datasets compromise model accuracy and reproducibility. These data issues lead to flawed predictions, increased development costs, and delayed timelines. Without harmonized data, AI struggles to identify viable drug candidates or predict outcomes reliably, limiting its transformative potential and widening the gap between research innovation and real-world pharmaceutical application.

Opportunity:

Explosion of Biomedical Data

The explosion of biomedical data is fueling a transformative leap in AI-driven drug discovery. With vast datasets from genomics, proteomics, and clinical records, AI models can now uncover hidden patterns, predict drug-target interactions, and accelerate lead identification. This data abundance enhances precision, reduces trial-and-error, and supports personalized medicine. As a result, pharmaceutical R&D becomes faster, more efficient, and cost-effective. The synergy between big data and AI is reshaping drug development into a smarter, data-powered frontier.

Threat:

High Implementation Costs

High implementation costs significantly hinder the adoption of AI in drug discovery, especially among small and mid-sized pharmaceutical firms. These expenses include advanced infrastructure, skilled personnel, and ongoing system maintenance. Such financial barriers delay integration, limit innovation, and widen the gap between large corporations and emerging players. As a result, the full potential of AI remains underutilized, slowing progress in developing faster, cost-effective, and personalized therapeutic solutions.

Covid-19 Impact

The COVID-19 pandemic significantly accelerated the adoption of AI in drug discovery, as pharmaceutical companies urgently sought faster, cost-effective solutions. AI tools were pivotal in identifying therapeutic targets, repurposing drugs, and optimizing vaccine development. This surge in demand led to increased investments, collaborations, and integration of AI platforms across R&D pipelines. The pandemic ultimately highlighted AI's transformative potential, establishing it as a critical asset in future pharmaceutical innovation and crisis response.

The oncology segment is expected to be the largest during the forecast period

The oncology segment is expected to account for the largest market share during the forecast period due to the urgent demand for precise, personalized cancer treatments. AI accelerates biomarker discovery, predicts therapeutic responses, and enhances clinical trial design, especially in complex cancers like lung and breast cancer. With oncology accounting for the largest share of AI drug discovery investments, it fosters innovation in targeted therapies and immuno-oncology. This synergy improves success rates, reduces development time, and positions AI as a transformative force in cancer research and treatment.

The deep learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the deep learning segment is predicted to witness the highest growth rate as it enables rapid analysis of complex biomedical data. Its ability to model intricate biological interactions accelerates target identification, optimizes compound screening, and enhances de novo drug design. Deep learning reduces development time and costs by improving prediction accuracy and minimizing trial failures. As pharmaceutical companies increasingly adopt these models, they unlock scalable, data-driven innovation-transforming drug discovery into a faster, more precise, and cost-effective process.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share due to robust R&D ecosystems, government support, and a surge in biotech startups. Countries like China, India, and Japan are leveraging AI to accelerate clinical trials, reduce costs, and enhance precision medicine. With vast genomic datasets and digital infrastructure, the region fosters innovation in oncology, immunology, and rare diseases. This momentum positions Asia Pacific as a global leader, transforming drug development into a faster, smarter, and more accessible process.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, owing to robust pharmaceutical infrastructure and leading tech innovators, the region leads global adoption. AI enables rapid compound screening, predictive modeling, and personalized medicine development. Strategic collaborations between biotech firms and AI startups are fueling innovation, while regulatory support fosters growth. This synergy is driving a projected market surge, positioning North America as a powerhouse in AI-driven pharmaceutical breakthroughs

Key players in the market

Some of the key players profiled in the AI in Drug Discovery Market include Atomwise, Inc., BenevolentAI, Insilico Medicine, Exscientia Ltd., Recursion Pharmaceuticals, BioXcel Therapeutics, Deep Genomics, Cloud Pharmaceuticals, Numerate, Inc., Cyclica Inc., Iktos, Evaxion Biotech, BERG LLC, Verge Genomics, Healx, PathAI, NVIDIA Corporation, IBM Watson Health, Google DeepMind and Schrodinger, Inc.

Key Developments:

In August 2022, Atomwise and Sanofi have launched a strategic, exclusive collaboration to harness Atomwise's AtomNet(R) AI platform for structure-based drug discovery targeting up to five molecular targets.

In March 2020, Atomwise and Bridge Biotherapeutics struck potential $1 billion research collaboration, aiming to develop up to 13 AI-driven small-molecule programs targeting inflammation-related proteins, especially Pellino E3 ubiquitin ligases.

Types Covered:

  • Preclinical and Clinical Testing
  • Molecule Screening
  • Target Identification
  • De Novo Drug Design

Drug Types Covered:

  • Small Molecules
  • Large Molecules

Offerings Covered:

  • Software
  • Services

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Other Technologies

Applications Covered:

  • Oncology
  • Neurology
  • Infectious Diseases
  • Cardiovascular Diseases
  • Metabolic Diseases
  • Immunology
  • Other Applications

End Users Covered:

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

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 2022, 2023, 2024, 2026, and 2030
  • 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 in Drug Discovery Market, By Type

  • 5.1 Introduction
  • 5.2 Preclinical and Clinical Testing
  • 5.3 Molecule Screening
  • 5.4 Target Identification
  • 5.5 De Novo Drug Design

6 Global AI in Drug Discovery Market, By Drug Type

  • 6.1 Introduction
  • 6.2 Small Molecules
  • 6.3 Large Molecules

7 Global AI in Drug Discovery Market, By Offering

  • 7.1 Introduction
  • 7.2 Software
  • 7.3 Services

8 Global AI in Drug Discovery Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning
    • 8.2.1 Supervised Learning
    • 8.2.2 Reinforcement Learning
    • 8.2.3 Unsupervised Learning
  • 8.3 Deep Learning
  • 8.4 Natural Language Processing (NLP)
  • 8.5 Other Technologies

9 Global AI in Drug Discovery Market, By Application

  • 9.1 Introduction
  • 9.2 Oncology
  • 9.3 Neurology
  • 9.4 Infectious Diseases
  • 9.5 Cardiovascular Diseases
  • 9.6 Metabolic Diseases
  • 9.7 Immunology
  • 9.8 Other Applications

10 Global AI in Drug Discovery Market, By End User

  • 10.1 Introduction
  • 10.2 Pharmaceutical Companies
  • 10.3 Biotechnology Companies
  • 10.4 Academic & Research Institutes
  • 10.5 Contract Research Organizations (CROs)
  • 10.6 Other End Users

11 Global AI in Drug Discovery Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 Atomwise, Inc.
  • 13.2 BenevolentAI
  • 13.3 Insilico Medicine
  • 13.4 Exscientia Ltd.
  • 13.5 Recursion Pharmaceuticals
  • 13.6 BioXcel Therapeutics
  • 13.7 Deep Genomics
  • 13.8 Cloud Pharmaceuticals
  • 13.9 Numerate, Inc.
  • 13.10 Cyclica Inc.
  • 13.11 Iktos
  • 13.12 Evaxion Biotech
  • 13.13 BERG LLC
  • 13.14 Verge Genomics
  • 13.15 Healx
  • 13.16 PathAI
  • 13.17 NVIDIA Corporation
  • 13.18 IBM Watson Health
  • 13.19 Google DeepMind
  • 13.20 Schrodinger, Inc.

List of Tables

  • Table 1 Global AI in Drug Discovery Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Drug Discovery Market Outlook, By Type (2024-2032) ($MN)
  • Table 3 Global AI in Drug Discovery Market Outlook, By Preclinical and Clinical Testing (2024-2032) ($MN)
  • Table 4 Global AI in Drug Discovery Market Outlook, By Molecule Screening (2024-2032) ($MN)
  • Table 5 Global AI in Drug Discovery Market Outlook, By Target Identification (2024-2032) ($MN)
  • Table 6 Global AI in Drug Discovery Market Outlook, By De Novo Drug Design (2024-2032) ($MN)
  • Table 7 Global AI in Drug Discovery Market Outlook, By Drug Type (2024-2032) ($MN)
  • Table 8 Global AI in Drug Discovery Market Outlook, By Small Molecules (2024-2032) ($MN)
  • Table 9 Global AI in Drug Discovery Market Outlook, By Large Molecules (2024-2032) ($MN)
  • Table 10 Global AI in Drug Discovery Market Outlook, By Offering (2024-2032) ($MN)
  • Table 11 Global AI in Drug Discovery Market Outlook, By Software (2024-2032) ($MN)
  • Table 12 Global AI in Drug Discovery Market Outlook, By Services (2024-2032) ($MN)
  • Table 13 Global AI in Drug Discovery Market Outlook, By Technology (2024-2032) ($MN)
  • Table 14 Global AI in Drug Discovery Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 15 Global AI in Drug Discovery Market Outlook, By Supervised Learning (2024-2032) ($MN)
  • Table 16 Global AI in Drug Discovery Market Outlook, By Reinforcement Learning (2024-2032) ($MN)
  • Table 17 Global AI in Drug Discovery Market Outlook, By Unsupervised Learning (2024-2032) ($MN)
  • Table 18 Global AI in Drug Discovery Market Outlook, By Deep Learning (2024-2032) ($MN)
  • Table 19 Global AI in Drug Discovery Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 20 Global AI in Drug Discovery Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 21 Global AI in Drug Discovery Market Outlook, By Application (2024-2032) ($MN)
  • Table 22 Global AI in Drug Discovery Market Outlook, By Oncology (2024-2032) ($MN)
  • Table 23 Global AI in Drug Discovery Market Outlook, By Neurology (2024-2032) ($MN)
  • Table 24 Global AI in Drug Discovery Market Outlook, By Infectious Diseases (2024-2032) ($MN)
  • Table 25 Global AI in Drug Discovery Market Outlook, By Cardiovascular Diseases (2024-2032) ($MN)
  • Table 26 Global AI in Drug Discovery Market Outlook, By Metabolic Diseases (2024-2032) ($MN)
  • Table 27 Global AI in Drug Discovery Market Outlook, By Immunology (2024-2032) ($MN)
  • Table 28 Global AI in Drug Discovery Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 29 Global AI in Drug Discovery Market Outlook, By End User (2024-2032) ($MN)
  • Table 30 Global AI in Drug Discovery Market Outlook, By Pharmaceutical Companies (2024-2032) ($MN)
  • Table 31 Global AI in Drug Discovery Market Outlook, By Biotechnology Companies (2024-2032) ($MN)
  • Table 32 Global AI in Drug Discovery Market Outlook, By Academic & Research Institutes (2024-2032) ($MN)
  • Table 33 Global AI in Drug Discovery Market Outlook, By Contract Research Organizations (CROs) (2024-2032) ($MN)
  • Table 34 Global AI in Drug Discovery Market Outlook, By Other End Users (2024-2032) ($MN)

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