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市场调查报告书
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1889439

人工智慧药物发现市场预测至2032年：按药物类型、治疗领域、技术、应用、最终用户和地区分類的全球分析

AI Drug Discovery Market Forecasts to 2032 - Global Analysis By Drug Type, 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个工作天内

价格

根据 Stratistics MRC 的一项研究，预计到 2025 年，全球人工智慧药物发现市场价值将达到 48 亿美元，到 2032 年将达到 96 亿美元，预测期内复合年增长率为 10.4%。

人工智慧药物发现利用先进的演算法分析生物数据、预测分子交互作用，并加速潜在治疗候选药物的辨识。机器学习平台简化了标靶选择、先导化合物优化和毒性预测流程，显着缩短了研发时间和成本。这些系统能够快速筛检庞大的化合物库，并在实验室检验前模拟化合物的生化行为。因此，製药公司能够更快实现创新，提高研发效率，并增加攻克复杂罕见疾病的成功率。

根据 Clinical Trials Arena 2025 年的分析，人工智慧与製药公司之间的策略合作伙伴关係将从 2015 年的 4 个激增至 2024 年的 27 个，凸显了旨在加速药物开发和降低临床前失败率的合作创新。

快速药物开发平臺的需求日益增长

随着製药公司寻求缩短药物研发週期并降低研发风险，对更快药物研发管线的需求日益增长，人工智慧的应用也随之加速。为了高效识别先导化合物，人工智慧演算法正在辅助进行高通量筛检、分子对接和预测建模。快速将疗法商业化的压力不断增加，尤其是在复杂疾病领域，这进一步加剧了对自动化的依赖。随着竞争加剧，开发人员越来越将人工智慧驱动的药物发现引擎视为提高早期药物开发流程效率和成功率的关键工具。

平台实施成本高昂

高昂的平台部署成本仍然是一大障碍，尤其对于资金有限的中小型生技公司更是如此。先进的人工智慧药物发现引擎需要对云端运算、生物数据集、模型训练和专业人才进行大量投资。与现有实验室系统的整合进一步增加了支出，并使扩充性更加复杂。此外，持续的演算法改进和资料收集需求也增加了长期营运成本。这些财务限制减缓了科技的普及，并在大型製药企业和新兴研究机构之间造成了鸿沟。

计算生物学整合进展

计算生物学的整合发展正透过加深对疾病机制的理解，创造巨大的成长机会。体学数据、分子模拟和人工智慧驱动的通路分析的融合，加速了标靶辨识和作用机制研究。随着多模态资料集的日益丰富，人工智慧平台能够更精准地预测治疗反应。这种协同效应将显着提升精准药物研发水平，并拓展其在罕见疾病、免疫学和个人化医疗等众多领域的应用。这些进展已确立了人工智慧在推动下一代药物研发管线变革中的作用。

影响独家调查的资料外洩事件

影响专有研究的资料外洩构成重大威胁，尤其是在大量分子资料储存于云端环境的情况下。未授权存取和模型篡改可能导致竞争策略洩露、监管申报延误或机密化合物库暴露。生物技术领域网路攻击的增加加剧了脆弱性，并削弱了人们对数位化研究工作流程的信任。缺乏健全安全态势的公司将面临声誉受损和经济损失的风险，这凸显了在人工智慧驱动的药物发现生态系统中建立严格的网路安全通讯协定的必要性。

新冠疫情的影响：

新冠疫情加速了人工智慧药物研发技术的应用，製药公司急需快速找到抗病毒药物和免疫调节剂。人工智慧工具辅助虚拟筛检和药物重定位，显着缩短了初步研究的时间。疫情凸显了传统研发方法的低效，促使企业对机器学习平台进行长期投资。此外，全球合作提高了资料集的可用性，从而提升了模型的准确性。即使在疫情结束后，对快速治疗反应和应对疫情的持续重视，也保持了人工智慧驱动的药物研发框架的市场成长势头。

预计在预测期内，小分子药物研发将占据最大的市场规模。

由于小分子药物适应症广泛且研发路径成熟，预计在预测期内，小分子药物研发领域将占据最大的市场份额。人工智慧平台在优化分子结构、预测ADMET特性以及加速先导药物最适化週期方面表现出色。製药公司持续优先研发小分子药物，因为它们具有可扩展性强、生产製程复杂度低以及商业性化成功率高等优点。与其他药物类别相比，这些因素促使人工智慧技术在小分子药物研发管线中得到主导的应用。

预计在预测期内，肿瘤治疗领域将达到最高的复合年增长率。

在预测期内，肿瘤学领域预计将保持最高的成长率，这主要得益于精准医疗需求的不断增长以及复杂标靶识别技术的进步。癌症的异质性生物学特征需要广泛的数据建模，这使得人工智慧在生物标记发现、通路映射和个人化治疗方案设计方面具有特别重要的价值。对免疫肿瘤学和标靶抑制剂领域投资的增加将进一步推动对人工智慧驱动洞察的依赖。随着全球癌症发生率的上升，开发商正在加速采用先进的分析技术，从而支撑该领域的强劲成长动能。

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，这主要得益于中国、印度、韩国和日本等国医药研发基地的扩张。政府对生物技术创新的大力支持、临床试验活动的增加以及人工智慧研究能力的提升，都推动了市场需求。该地区的成本优势正促使全球企业将药物研发活动外包。此外，快速发展的医疗保健生态系统以及对电脑辅助药物研发领域不断增长的投资，也进一步巩固了亚太地区的主导地位。

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，这主要得益于其强大的人工智慧基础设施、强劲的药物创新能力以及对先进发现工具的早期应用。领先的生物技术公司、人工智慧Start-Ups和研究机构正在加速将机器学习融入药物开发平臺。有利于数位化研发工具的监管环境将进一步促进其广泛应用。精心整理的资料集、创业投资资金以及丰富的多学科人才储备，正巩固北美作为人工智慧驱动药物发现领域成长最快市场的地位。

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  • 公司概况
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    • 根据主要参与者的产品系列、地理覆盖范围和策略联盟基准化分析

目录

第一章执行摘要

第二章 前言

  • 摘要
  • 相关利益者
  • 调查范围
  • 调查方法
  • 研究材料

第三章 市场趋势分析

  • 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 全球人工智慧药物发现市场（按药物类型划分）

  • 小分子药物研发
  • 生物製剂药物发现
  • 肽类和蛋白质类药物
  • 再生医学和细胞疗法
  • 基因治疗候选药物
  • 新治疗方法

第六章 全球人工智慧药物发现市场（按治疗领域划分）

  • 肿瘤学
  • 神经病学
  • 免疫学
  • 感染疾病
  • 循环系统
  • 罕见疾病和孤儿疾病

第七章 全球人工智慧药物发现市场（按技术划分）

  • 机器学习平台
  • 深度学习与神经网络
  • 用于分子设计的生成式人工智慧
  • 量子人工智慧药物建模
  • 基于结构的药物设计工具
  • 体学资料分析系统

第八章 全球人工智慧药物发现市场（按应用划分）

  • 标靶分子的鑑定
  • 潜在客户开发与最佳化
  • 化合物筛检
  • 临床前试验自动化
  • 生物标记鑑定
  • 毒性预测与检验

第九章 全球人工智慧药物发现市场（按最终用户划分）

  • 製药和生物技术公司
  • 学术和研究机构
  • 合约研究组织（CRO）
  • 医院和临床试验机构
  • 其他最终用户

第十章 全球人工智慧药物发现市场（按地区划分）

  • 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
    • 其他南美国家
  • 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
  • 併购
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章 企业概况

  • Pfizer
  • Roche
  • AstraZeneca
  • Moderna
  • Sanofi
  • Novartis
  • Johnson &Johnson
  • GSK
  • Eli Lilly
  • Bayer
  • Boehringer Ingelheim
  • Merck &Co.
  • AbbVie
  • Schrodinger
  • Exscientia
  • Atomwise
  • Insilico Medicine
Product Code: SMRC32795

According to Stratistics MRC, the Global AI Drug Discovery Market is accounted for $4.8 billion in 2025 and is expected to reach $9.6 billion by 2032 growing at a CAGR of 10.4% during the forecast period. AI Drug Discovery involves deploying advanced algorithms to analyze biological data, predict molecular interactions, and accelerate identification of potential therapeutic candidates. Machine-learning platforms streamline target selection, lead optimization, and toxicity prediction, significantly reducing development time and costs. These systems enable rapid screening of vast compound libraries and simulate biochemical behavior before laboratory validation. As a result, pharmaceutical companies gain faster pathways to innovation, improved R&D productivity, and a higher probability of success in addressing complex and rare diseases.

According to Clinical Trials Arena's 2025 analysis, strategic partnerships between AI firms and pharmaceutical companies surged to 27 in 2024 from 4 in 2015, highlighting collaborative innovation in accelerating drug development and reducing preclinical failure rates.

Market Dynamics:

Driver:

Rising demand for faster drug pipelines

Rising demand for faster drug pipelines is accelerating AI adoption as pharma companies strive to shorten discovery timelines and reduce R&D risks. Propelled by the need to identify lead compounds more efficiently, AI algorithms support high-throughput screening, molecular docking, and predictive modeling. Increasing pressure to commercialize therapeutics rapidly especially for complex diseases further boosts reliance on automation. As competitive intensity heightens, developers increasingly view AI-driven discovery engines as essential tools to enhance productivity and improve success rates across early-stage drug workflows.

Restraint:

High deployment costs for platforms

High deployment costs for platforms remain a significant barrier, especially for small and mid-sized biotech firms with limited capital. Advanced AI discovery engines require substantial investments in cloud computing, biological datasets, model training, and skilled personnel. Integration with legacy laboratory systems further increases expenditures, complicating scalability. Additionally, the need for ongoing algorithm refinement and data acquisition adds long-term operational costs. These financial constraints slow adoption and create disparities between large pharmaceutical companies and emerging research organizations.

Opportunity:

Advances in computational biology integration

Advances in computational biology integration create substantial growth opportunities by enabling deeper understanding of disease mechanisms. The fusion of omics data, molecular simulations, and AI-driven pathway analysis accelerates target identification and mechanism-of-action studies. As multi-modal datasets become more accessible, AI platforms gain the ability to predict therapeutic responses with higher accuracy. This synergy significantly enhances precision-drug development and broadens applicability across rare diseases, immunology, and personalized medicine. These advancements position AI as a transformative enabler of next-generation drug pipelines.

Threat:

Data breaches affecting proprietary research

Data breaches affecting proprietary research pose a major threat, particularly as vast volumes of molecular data reside in cloud environments. Unauthorized access or model manipulation could compromise competitive strategies, delay regulatory submissions, or reveal confidential compound libraries. Increasing cyberattacks in the biotech sector amplify vulnerabilities, undermining trust in digitalized research workflows. Companies lacking robust security frameworks risk reputational damage and financial losses, emphasizing the necessity for stringent cybersecurity protocols across AI-driven discovery ecosystems.

Covid-19 Impact:

COVID-19 accelerated AI drug discovery adoption as pharma companies sought rapid solutions for antiviral and immunomodulatory candidates. AI tools supported virtual screening and repurposing efforts, significantly compressing early research timelines. The pandemic highlighted inefficiencies in traditional R&D approaches, prompting long-term investments in machine learning platforms. Additionally, global collaboration increased dataset availability, improving model accuracy. Post-pandemic, continued emphasis on rapid therapeutic response and preparedness sustains market momentum for AI-enabled discovery frameworks.

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

The small molecule drug discovery segment is expected to account for the largest market share during the forecast period, resulting from its broad therapeutic applicability and well-established development pathways. AI platforms excel at optimizing molecular structures, predicting ADMET profiles, and accelerating lead optimization cycles. Pharmaceutical companies continue prioritizing small molecules due to their scalability, lower manufacturing complexity, and strong commercial success rates. These factors reinforce dominant adoption of AI technologies across small molecule pipelines compared to other drug classes.

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

Over the forecast period, the oncology segment is predicted to witness the highest growth rate, propelled by rising demand for precision therapies and complex target identification. Cancer's heterogeneous biology requires extensive data modeling, making AI particularly valuable for biomarker discovery, pathway mapping, and personalized treatment design. Increasing investment in immuno-oncology and targeted inhibitors further boosts reliance on AI-driven insights. As cancer incidence climbs globally, developers accelerate adoption of advanced analytics, supporting this segment's exceptional growth trajectory.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, attributed to expanding pharmaceutical R&D hubs across China, India, South Korea, and Japan. Strong government support for biotech innovation, increasing clinical trial activity, and growing AI research capabilities fuel demand. Regional cost advantages attract global companies to outsource discovery tasks. Additionally, rapidly developing health ecosystems and increasing investment in computational drug discovery strengthen Asia Pacific's leadership position.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with strong AI infrastructure, robust pharmaceutical innovation, and early adoption of advanced discovery tools. Leading biotech companies, AI start-ups, and research institutes accelerate integration of machine learning into drug pipelines. Favorable regulatory pathways for digital R&D tools further enhance uptake. High availability of curated datasets, venture funding, and interdisciplinary talent solidify North America as the fastest-expanding market for AI-driven drug discovery.

Key players in the market

Some of the key players in AI Drug Discovery Market include Pfizer, Roche, AstraZeneca, Moderna, Sanofi, Novartis, Johnson & Johnson, GSK, Eli Lilly, Bayer, Boehringer Ingelheim, Merck & Co., AbbVie, Schrodinger, Exscientia, Atomwise and Insilico Medicine.

Key Developments:

In November 2025, AstraZeneca launched an AI collaboration with BenevolentAI, applying predictive algorithms to respiratory and cardiovascular drug pipelines, aiming to shorten discovery timelines and improve patient-specific treatment outcomes.

In October 2025, Pfizer advanced its AI-driven oncology pipeline, integrating machine learning for target identification and biomarker discovery, accelerating clinical trial readiness and enhancing precision medicine strategies across multiple cancer indications.

In September 2025, Roche expanded its AI-enabled drug discovery platform, focusing on immunology and rare diseases, leveraging deep learning to optimize molecular design and reduce early-stage attrition rates in therapeutic development.

Drug Types Covered:

  • Small Molecule Drug Discovery
  • Biologics Discovery
  • Peptide & Protein-Based Drugs
  • Regenerative & Cell Therapies
  • Gene Therapy Candidates
  • Novel Therapeutic Modalities

Therapeutic Areas Covered:

  • Oncology
  • Neurology
  • Immunology
  • Infectious Diseases
  • Cardiology
  • Rare & Orphan Diseases

Technologies Covered:

  • Machine Learning Platforms
  • Deep Learning & Neural Networks
  • Generative AI for Molecule Design
  • Quantum AI Drug Modeling
  • Structure-Based Drug Design Tools
  • Omics Data Analysis Systems

Applications Covered:

  • Target Identification
  • Lead Generation & Optimization
  • Compound Screening
  • Preclinical Testing Automation
  • Biomarker Identification
  • Toxicity Prediction & Validation

End Users Covered:

  • Pharmaceutical Companies & Biotechnology Companies
  • Academic & Research Institutes
  • Contract Research Organizations (CROs)
  • Hospitals & Clinical Labs
  • 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 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 Drug Discovery Market, By Drug Type

  • 5.1 Introduction
  • 5.2 Small Molecule Drug Discovery
  • 5.3 Biologics Discovery
  • 5.4 Peptide & Protein-Based Drugs
  • 5.5 Regenerative & Cell Therapies
  • 5.6 Gene Therapy Candidates
  • 5.7 Novel Therapeutic Modalities

6 Global AI Drug Discovery Market, By Therapeutic Area

  • 6.1 Introduction
  • 6.2 Oncology
  • 6.3 Neurology
  • 6.4 Immunology
  • 6.5 Infectious Diseases
  • 6.6 Cardiology
  • 6.7 Rare & Orphan Diseases

7 Global AI Drug Discovery Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning Platforms
  • 7.3 Deep Learning & Neural Networks
  • 7.4 Generative AI for Molecule Design
  • 7.5 Quantum AI Drug Modeling
  • 7.6 Structure-Based Drug Design Tools
  • 7.7 Omics Data Analysis Systems

8 Global AI Drug Discovery Market, By Application

  • 8.1 Introduction
  • 8.2 Target Identification
  • 8.3 Lead Generation & Optimization
  • 8.4 Compound Screening
  • 8.5 Preclinical Testing Automation
  • 8.6 Biomarker Identification
  • 8.7 Toxicity Prediction & Validation

9 Global AI Drug Discovery Market, By End User

  • 9.1 Introduction
  • 9.2 Pharmaceutical Companies & Biotechnology Companies
  • 9.3 Academic & Research Institutes
  • 9.4 Contract Research Organizations (CROs)
  • 9.5 Hospitals & Clinical Labs
  • 9.6 Other End Users

10 Global AI 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 Pfizer
  • 12.2 Roche
  • 12.3 AstraZeneca
  • 12.4 Moderna
  • 12.5 Sanofi
  • 12.6 Novartis
  • 12.7 Johnson & Johnson
  • 12.8 GSK
  • 12.9 Eli Lilly
  • 12.10 Bayer
  • 12.11 Boehringer Ingelheim
  • 12.12 Merck & Co.
  • 12.13 AbbVie
  • 12.14 Schrodinger
  • 12.15 Exscientia
  • 12.16 Atomwise
  • 12.17 Insilico Medicine

List of Tables

  • Table 1 Global AI Drug Discovery Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI Drug Discovery Market Outlook, By Drug Type (2024-2032) ($MN)
  • Table 3 Global AI Drug Discovery Market Outlook, By Small Molecule Drug Discovery (2024-2032) ($MN)
  • Table 4 Global AI Drug Discovery Market Outlook, By Biologics Discovery (2024-2032) ($MN)
  • Table 5 Global AI Drug Discovery Market Outlook, By Peptide & Protein-Based Drugs (2024-2032) ($MN)
  • Table 6 Global AI Drug Discovery Market Outlook, By Regenerative & Cell Therapies (2024-2032) ($MN)
  • Table 7 Global AI Drug Discovery Market Outlook, By Gene Therapy Candidates (2024-2032) ($MN)
  • Table 8 Global AI Drug Discovery Market Outlook, By Novel Therapeutic Modalities (2024-2032) ($MN)
  • Table 9 Global AI Drug Discovery Market Outlook, By Therapeutic Area (2024-2032) ($MN)
  • Table 10 Global AI Drug Discovery Market Outlook, By Oncology (2024-2032) ($MN)
  • Table 11 Global AI Drug Discovery Market Outlook, By Neurology (2024-2032) ($MN)
  • Table 12 Global AI Drug Discovery Market Outlook, By Immunology (2024-2032) ($MN)
  • Table 13 Global AI Drug Discovery Market Outlook, By Infectious Diseases (2024-2032) ($MN)
  • Table 14 Global AI Drug Discovery Market Outlook, By Cardiology (2024-2032) ($MN)
  • Table 15 Global AI Drug Discovery Market Outlook, By Rare & Orphan Diseases (2024-2032) ($MN)
  • Table 16 Global AI Drug Discovery Market Outlook, By Technology (2024-2032) ($MN)
  • Table 17 Global AI Drug Discovery Market Outlook, By Machine Learning Platforms (2024-2032) ($MN)
  • Table 18 Global AI Drug Discovery Market Outlook, By Deep Learning & Neural Networks (2024-2032) ($MN)
  • Table 19 Global AI Drug Discovery Market Outlook, By Generative AI for Molecule Design (2024-2032) ($MN)
  • Table 20 Global AI Drug Discovery Market Outlook, By Quantum AI Drug Modeling (2024-2032) ($MN)
  • Table 21 Global AI Drug Discovery Market Outlook, By Structure-Based Drug Design Tools (2024-2032) ($MN)
  • Table 22 Global AI Drug Discovery Market Outlook, By Omics Data Analysis Systems (2024-2032) ($MN)
  • Table 23 Global AI Drug Discovery Market Outlook, By Application (2024-2032) ($MN)
  • Table 24 Global AI Drug Discovery Market Outlook, By Target Identification (2024-2032) ($MN)
  • Table 25 Global AI Drug Discovery Market Outlook, By Lead Generation & Optimization (2024-2032) ($MN)
  • Table 26 Global AI Drug Discovery Market Outlook, By Compound Screening (2024-2032) ($MN)
  • Table 27 Global AI Drug Discovery Market Outlook, By Preclinical Testing Automation (2024-2032) ($MN)
  • Table 28 Global AI Drug Discovery Market Outlook, By Biomarker Identification (2024-2032) ($MN)
  • Table 29 Global AI Drug Discovery Market Outlook, By Toxicity Prediction & Validation (2024-2032) ($MN)
  • Table 30 Global AI Drug Discovery Market Outlook, By End User (2024-2032) ($MN)
  • Table 31 Global AI Drug Discovery Market Outlook, By Pharmaceutical Companies & Biotechnology Companies (2024-2032) ($MN)
  • Table 32 Global AI Drug Discovery Market Outlook, By Academic & Research Institutes (2024-2032) ($MN)
  • Table 33 Global AI Drug Discovery Market Outlook, By Contract Research Organizations (CROs) (2024-2032) ($MN)
  • Table 34 Global AI Drug Discovery Market Outlook, By Hospitals & Clinical Labs (2024-2032) ($MN)
  • Table 35 Global AI 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.