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市场调查报告书
商品编码
1776725

2032 年药物研发市场 AI 预测：按类型、药物类型、产品、技术、应用、最终用户和地区进行全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，全球药物研发人工智慧市场规模预计在 2025 年将达到 26 亿美元，到 2032 年将达到 178 亿美元，预测期内的复合年增长率为 31.7%。

药物研发中的人工智慧 (AI) 是指应用机器学习和数据驱动演算法来加速和优化开发新药流程。 AI 可以分析从分子结构到临床试验结果的大量资料集，从而识别有潜力的候选药物，预测药物与标靶的相互作用，甚至设计新型化合物。 AI 可以减少传统药物研发方法所需的时间、成本和失败率。透过模拟生物系统并从现有数据中学习，AI 可以帮助研究人员发现规律并做出更准确的决策。

根据世界卫生组织估计，2022年全球将新增2,000万癌症病例，并有970万人死于癌症。

研发成本上升与时间压力

不断上升的研发成本和时间压力正在加速人工智慧在药物研发中的应用，并成为创新的催化剂。这些挑战迫使製药公司采用人工智慧主导的解决方案，以简化标靶识别、优化临床试验并减少代价高昂的失败。因此，人工智慧可以提高研发效率、缩短开发时间并提高成功率。这种迫切性正在推动对智慧技术的投资，以改变传统的工作流程，实现更快、更具成本效益的药物开发，从而满足日益增长的医疗保健需求。

缺乏标准化、高品质的数据

缺乏标准化、高品质的数据严重阻碍了人工智慧在药物研发中的有效性。不一致的格式、不完整的註释以及偏差的资料集损害了模型的准确性和可重复性。这些数据挑战导致预测错误、开发成本增加以及研发进度延迟。缺乏统一的数据，人工智慧难以识别可行的候选药物或可靠地预测结果，这限制了其变革潜力，并扩大了研究创新与现实世界药物应用之间的差距。

生物医学数据的爆炸性成长

生物医学数据的爆炸性成长正推动人工智慧主导的药物研发实现变革性飞跃。来自基因组学、蛋白质组学和临床记录的海量数据集使人工智慧模型能够发现隐藏的模式，预测药物-标靶相互作用，并加速先导化合物的识别。这些丰富的数据提高了准确性，减少了试验，并支持个人化医疗。因此，药物研发变得更快、更有效率、更具成本效益。巨量资料与人工智慧的协同作用有望将药物研发转变为一个更智慧、数据驱动的前沿领域。

实施成本高

高昂的实施成本是人工智慧在药物研发中的应用面临的一大障碍，尤其对于中小型製药公司而言。这些费用包括先进的基础设施、熟练的人员以及持续的系统维护。这些财务障碍减缓了整合速度，限制了创新，并扩大了大公司与新兴企业之间的差距。因此，人工智慧的潜力尚未得到充分开发，阻碍了更快、更具成本效益和个人化治疗方案的开发。

COVID-19的影响

新冠疫情显着加速了人工智慧在药物研发中的应用，因为製药公司迫切需要更快速、更具成本效益的解决方案。人工智慧工具在识别治疗标靶、药物再利用和优化疫苗开发方面发挥了关键作用。这种需求激增，促使整个研发开发平臺中对人工智慧平台的投资、合作和整合不断增加。这场疫情最终凸显了人工智慧的变革潜力，使其成为未来製药创新和危机应变的关键资产。

预计肿瘤学将成为预测期内最大的领域

由于对精准个人化癌症治疗的迫切需求，预计肿瘤学领域将在预测期内占据最大的市场占有率。人工智慧将加速生物标记的发现，预测治疗反应，并增强临床试验设计，特别是在肺癌和乳癌等复杂癌症领域。肿瘤学在人工智慧药物研发投资中占比最大，将推动标靶治疗和免疫肿瘤学的创新。这种协同效应将提高成功率，缩短研发週期，使人工智慧成为癌症研究和治疗领域的变革力量。

预计深度学习领域在预测期内将以最高复合年增长率成长

深度学习领域预计将在预测期内实现最高成长率，因为它能够快速分析复杂的生物医学数据。对复杂生物相互作用进行建模的能力可以加速标靶识别，优化化合物筛检，并增强药物的全新设计。深度学习透过提高预测准确性和最大限度地减少试验失败，从而缩短开发时间和降低成本。随着製药公司越来越多地机会这些模型，它将实现可扩展的数据驱动型创新，使药物研发成为一个更快、更准确、更具成本效益的过程。

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占有率，这得益于其强大的研发生态系统、政府支持以及新兴企业的蓬勃发展。中国、印度和日本等国家正在利用人工智慧加速临床试验、降低成本并增强精准医疗。凭藉庞大的基因组数据集和数位基础设施，该地区正在推动肿瘤学、免疫学和罕见疾病领域的创新。这一势头使亚太地区成为将药物开发流程转变为更快速、更聪明、更便利的全球领导者。

复合年增长率最高的地区：

预计北美将在预测期内实现最高的复合年增长率，因为该地区凭藉其强大的製药基础设施和先进的技术创新者，引领全球人工智慧应用。人工智慧能够实现快速化合物筛检、预测建模和个人化医疗开发。生物技术公司与人工智慧新兴企业之间的策略合作伙伴关係正在激发创新，而监管机构的支持则促进了成长。这种协同效应使北美成为人工智慧赋能製药突破的强国，推动了市场的快速成长。

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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
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第五章全球药物发现人工智慧市场（按类型）

  • 临床前和临床试验
  • 分子筛检
  • 目标识别
  • 从头药物设计

第六章 全球药物发现人工智慧市场（以药物类型）

  • 小分子
  • 高分子

第七章 全球药物研发人工智慧市场（按产品）

  • 软体
  • 服务

第八章 全球药物研发人工智慧市场（按技术）

  • 机器学习
    • 监督学习
    • 强化学习
    • 无监督学习
  • 深度学习
  • 自然语言处理（NLP）
  • 其他技术

第九章全球药物研发人工智慧市场（按应用）

  • 肿瘤学
  • 神经病学
  • 感染疾病
  • 心血管疾病
  • 代谢疾病
  • 免疫学
  • 其他用途

第 10 章全球药物发现 AI 市场（以最终用户划分）

  • 製药公司
  • 生技公司
  • 学术研究机构
  • 合约研究组织（CRO）
  • 其他最终用户

第 11 章全球药物发现 AI 市场（按地区）

  • 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
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 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

第十二章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十三章：企业概况

  • 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.