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市场调查报告书
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2007823

人工智慧驱动的药物发现市场预测至2034年：全球按组件、技术、药物类型、治疗领域、应用、最终用户和地区分類的分析

AI Driven Drug Discovery Market Forecasts to 2034 - Global Analysis By Component (Software and Services), Technology, Drug Type, Therapeutic Area, 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 的数据，全球人工智慧驱动的药物发现市场预计将在 2026 年达到 42 亿美元，并在预测期内以 17.5% 的复合年增长率增长，到 2034 年达到 161 亿美元。

人工智慧驱动的药物发现是一项利用机器学习、深度学习和进阶数据分析等人工智慧技术来增强和加速新药研发的计画。这些技术分析海量的生物学、化学和临床数据，以识别有前景的药物靶点，设计和优化分子化合物，并评估药物的安全性和有效性。透过自动化复杂的研发方法并挖掘庞大资料集中的模式，人工智慧有助于降低传统药物研发所需的时间、成本和风险。

研发加速与成本压力

製药业正面临巨大的压力，需要缩短新药上市所需的时间和资金投入。传统上，新药上市往往需要十多年时间，耗资超过26亿美元。人工智慧平台正透过自动化标靶识别、早期预测药物毒性以及优化临床试验设计，直接应对这项挑战。机器学习演算法可以在几天内（而非几年）分析大量资料集，使企业能够及早淘汰前景不佳的候选药物，并将资源集中在最有希望的资产上。这种对提高研发效率的需求正迫使大型製药公司将人工智慧解决方案整合到其整个药物研发流程中，从而显着提升营运效率。

数据可用性和互通性挑战

人工智慧模型的有效性很大程度上取决于高品质、标准化且经过标註的资料集的可用性。然而，生物医学资料领域往往支离破碎，包含不相容且分散的电子健康记录、专有化学库和非结构化的研究论文。对资料隐私、智慧财产权以及专有资料集孤岛的担忧进一步限制了稳健演算法的训练。如果无法取得全面、干净且统一的数据，人工智慧模型就有可能产生偏差或不准确的预测，导致其潜力无法充分发挥，并减缓其在整个产业的应用。

开发针对复杂疾病的新治疗方法和应用

随着人工智慧演算法日益复杂，其应用范围已从传统的小分子药物扩展到基因疗法、RNA疗法和抗体药物复合体（ADC）等复杂治疗方法，并涌现出巨大的机会。生成式人工智慧和深度学习正在引领新型生物製剂的设计，并揭示神经退化性疾病疾病和罕见遗传疾病等多标靶疾病的复杂性。将多体学资料（基因体学、蛋白质体学）与人工智慧结合，能够发现以往难以治疗的全新药物类别。这项技术将为专注于人工智慧的公司带来庞大的新收入来源，并加速在历来极具挑战性的治疗领域开发治疗方法。

不断演变的监管和检验框架

许多人工智慧演算法的「黑箱」特性对其广泛应用构成重大威胁。美国食品药物管理局（FDA）和欧洲药品管理局（EMA）等监管机构正努力寻找检验和核准透过不透明的人工智慧流程发现的药物的方法。目前，尚缺乏用于检验人工智慧产生的候选药物的安全性、有效性和可重复性的标准化指南。人工智慧发明化合物的智慧财产权不确定性也进一步加剧了商业化策略的复杂性。随着市场扩张，监管路径的製定若出现延误，或人工智慧预测的候选化合物在后期临床试验中失败，都可能削弱投资人信心，并减缓市场成长动能。

新冠疫情的影响

新冠疫情加速了人工智慧驱动的药物研发市场的发展，研究人员迫切需要快速解决方案。人工智慧平台被广泛用于现有药物的再利用以及针对SARS-CoV-2病毒设计新型抗病毒药物，显着缩短了早期药物研发阶段。这场危机展现了人工智慧前所未有的快速反应能力，促使创业投资和资金筹措合作激增。然而，供应链中断和临床资源的转移最初阻碍了检验工作。疫情过后，该行业采取了更具韧性的策略，利用人工智慧已取得的成功，建立强大而灵活的药物研发流程，以应对未来的流行病和慢性疾病。

在预测期内，机器学习领域预计将占据最大的市场份额。

机器学习领域预计将在预测期内占据最大的市场份额，因为它在复杂生物数据集的分析中发挥至关重要的作用。作为最成熟的人工智慧技术，机器学习演算法被广泛应用于基因组学、蛋白质折迭和生物标记识别等领域的模式识别。其多功能性使其能够应用于从标靶检验到临床前建模的各个阶段。

在预测期内，製药公司板块预计将呈现最高的复合年增长率。

在预测期内，受急需补充非专利药物产品组合的驱动，製药公司板块预计将呈现最高的成长率。大型製药企业正积极采用人工智慧来降低研发风险、简化营运流程并降低临床试验的高失败率。从内部研发转向策略性收购人工智慧Start-Ups新创公司的混合模式，正在加速人工智慧的普及应用。

市占率最大的地区：

在预测期内，北美预计将占据最大的市场份额，这主要得益于其成熟的製药生态系统和人工智慧技术公司的高度集中。美国在研发投入方面处于主导地位，这得益于美国国立卫生研究院 (NIH) 的大力政府资助和有利的创业投资投资。大型製药公司和科技巨头在药物研发平台上的合作，构成了一个强大的创新中心。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于快速的数位化和不断壮大的合约研究组织（CRO）行业。中国、印度和韩国等国家正大力投资人工智慧基础设施和生物资讯学，以降低生产成本并加速学名药的研发。各国政府推行的「人工智慧医疗」措施正在培育本土Start-Ups生态系统并吸引外资。

免费客製化服务：

所有购买此报告的客户均可享受以下免费自订选项之一：

  • 企业概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户要求，我们可以提供主要国家和地区的市场估算和预测，以及复合年增长率（註：需经可行性确认）。
  • 竞争性标竿分析
    • 根据产品系列、地理覆盖范围和策略联盟对主要企业进行基准分析。

目录

第一章执行摘要

  • 市场概览及主要亮点
  • 驱动因素、挑战与机会
  • 竞争格局概述
  • 战略洞察与建议

第二章：研究框架

  • 研究目标和范围
  • 相关人员分析
  • 研究假设和限制
  • 调查方法

第三章 市场动态与趋势分析

  • 市场定义与结构
  • 主要市场驱动因素
  • 市场限制与挑战
  • 投资成长机会和重点领域
  • 产业威胁与风险评估
  • 技术与创新展望
  • 新兴市场/高成长市场
  • 监管和政策环境
  • 新冠疫情的影响及復苏前景

第四章：竞争环境与策略评估

  • 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
    • 竞争公司之间的竞争
  • 主要企业市占率分析
  • 产品基准评效和效能比较

第五章：全球人工智慧驱动药物发现市场：按组成部分划分

  • 软体
    • 人工智慧药物发现平台
    • 数据分析和建模软体
    • 分子建模和模拟工具
  • 服务
    • 人工智慧咨询服务
    • 资料处理和整合服务
    • 药物发现支持服务

第六章：全球人工智慧驱动药物发现市场：按技术划分

  • 机器学习
  • 深度学习
  • 自然语言处理（NLP）
  • 人工智慧世代
  • 电脑视觉
  • 其他人工智慧技术

第七章：全球人工智慧驱动药物发现市场：按药物类型划分

  • 低分子化合物
  • 聚合物化合物/生物製药

第八章：全球人工智慧驱动药物发现市场：按治疗领域划分

  • 肿瘤学
  • 神经退化性疾病
  • 心血管疾病
  • 感染疾病
  • 代谢性疾病
  • 免疫学
  • 呼吸系统疾病
  • 其他治疗领域

第九章：全球人工智慧驱动药物发现市场：按应用领域划分

  • 目标识别与检验
  • 命中辨识/分子筛检
  • 潜在客户开发
  • 先导药物最适化
  • 药物再利用
  • 临床前试验
  • 临床试验的优化

第十章：全球人工智慧驱动药物发现市场：按最终用户划分

  • 製药公司
  • 生技公司
  • 受託研究机构（CRO）
  • 学术研究机构
  • 其他最终用户

第十一章：全球人工智慧驱动药物发现市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 比利时
    • 瑞典
    • 瑞士
    • 波兰
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 泰国
    • 马来西亚
    • 新加坡
    • 越南
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥伦比亚
    • 智利
    • 秘鲁
    • 其他南美国家
  • 世界其他地区（RoW）
    • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 以色列
      • 其他中东国家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲国家

第十二章 策略市场资讯

  • 工业价值网络和供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十三章 产业趋势与策略倡议

  • 併购
  • 伙伴关係、联盟、合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十四章：公司简介

  • Insilico Medicine
  • BenevolentAI
  • Exscientia plc
  • Recursion Pharmaceuticals
  • Atomwise Inc.
  • Deep Genomics
  • Schrodinger, Inc.
  • Evotec SE
  • Valo Health
  • Verge Genomics
  • Healx
  • XtalPi
  • Standigm
  • Cyclica Inc.
  • Iktos
Product Code: SMRC34698

According to Stratistics MRC, the Global AI Driven Drug Discovery Market is accounted for $4.2 billion in 2026 and is expected to reach $16.1 billion by 2034 growing at a CAGR of 17.5% during the forecast period. AI-driven drug discovery involves the application of artificial intelligence technologies such as machine learning, deep learning, and advanced data analytics to enhance and accelerate the development of new medicines. These technologies analyze large volumes of biological, chemical, and clinical data to identify promising drug targets, design and optimize molecular compounds, and evaluate drug safety and effectiveness. By automating complex research processes and uncovering patterns within extensive datasets, AI helps reduce the time, cost, and risk traditionally associated with pharmaceutical research and drug development.

Market Dynamics:

Driver:

Accelerating R&D timelines and cost pressures

The pharmaceutical industry faces immense pressure to reduce the substantial time and financial investment required to bring a drug to market, which traditionally exceeds a decade and costs over $2.6 billion. AI-driven platforms directly address this by automating target identification, predicting drug toxicity early, and optimizing clinical trial designs. Machine learning algorithms can analyze vast datasets in days rather than years, allowing companies to fail unsuccessful candidates faster and focus resources on the most promising assets. This imperative to improve R&D productivity is compelling pharmaceutical giants to integrate AI solutions across their discovery pipelines, transforming operational efficiency.

Restraint:

Data availability and interoperability challenges

The effectiveness of AI models is heavily dependent on the availability of high-quality, standardized, and annotated datasets. However, the biomedical data landscape is often fragmented, consisting of disparate electronic health records, proprietary chemical libraries, and unstructured research papers that lack interoperability. Concerns regarding data privacy, intellectual property rights, and the siloed nature of proprietary datasets further restrict the training of robust algorithms. Without access to comprehensive, clean, and harmonized data, AI models risk generating biased or inaccurate predictions, which limits their full potential and slows down mainstream adoption across the industry.

Opportunity:

Expansion into novel therapeutic modalities and complex diseases

As AI algorithms become more sophisticated, there is a significant opportunity to apply them beyond traditional small molecules to complex modalities such as gene therapies, RNA therapeutics, and antibody-drug conjugates. Generative AI and deep learning are unlocking the ability to design novel biologics and navigate the complexities of multi-target diseases like neurodegeneration and rare genetic disorders. The integration of multi-omics data (genomics, proteomics) with AI is enabling the discovery of entirely new classes of drugs that were previously undruggable. This capability opens vast new revenue streams for AI-focused firms and accelerates the development of cures for historically challenging therapeutic areas.

Threat:

Evolving regulatory and validation frameworks

The "black box" nature of many AI algorithms poses a significant threat to widespread adoption, as regulatory bodies like the FDA and EMA grapple with how to validate and approve drugs discovered through opaque AI processes. There is currently a lack of standardized guidelines for verifying the safety, efficacy, and reproducibility of AI-generated drug candidates. Uncertainty surrounding intellectual property rights for AI-invented compounds further complicates commercialization strategies. As the market grows, any delays in establishing clear regulatory pathways or failures in AI-predicted candidates during late-stage trials could erode investor confidence and slow market momentum.

Covid-19 Impact

The COVID-19 pandemic served as a catalyst for the AI-driven drug discovery market, as researchers urgently sought rapid solutions. AI platforms were deployed extensively to repurpose existing drugs and design novel antivirals against the SARS-CoV-2 virus, significantly compressing the initial discovery phase. The crisis validated AI's capability to operate at unprecedented speeds, leading to a surge in venture capital funding and strategic partnerships. However, supply chain disruptions and the redirection of clinical resources initially hampered validation efforts. Post-pandemic, the industry has adopted a more resilient mindset, leveraging the proven success of AI to build robust, agile discovery pipelines for future pandemics and chronic diseases.

The Machine Learning segment is expected to be the largest during the forecast period

The Machine Learning segment is expected to account for the largest market share during the forecast period, due to its foundational role in analyzing complex biological datasets. As the most mature AI technology, ML algorithms are extensively used for pattern recognition in genomics, protein folding, and biomarker identification. Its versatility allows for application across various stages, from target validation to preclinical modeling.

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

Over the forecast period, the Pharmaceutical Companies segment is predicted to witness the highest growth rate, driven by the urgent need to replenish patent-expired drug portfolios. Major pharma players are aggressively adopting AI to de-risk R&D, streamline operations, and lower the high attrition rates associated with clinical trials. The shift from in-house R&D to hybrid models involving strategic acquisitions of AI-native startups is accelerating adoption.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, fuelled by a mature pharmaceutical ecosystem and high concentration of AI technology firms. The United States leads in R&D expenditure, supported by strong government funding through the NIH and favorable venture capital investments. The presence of major pharmaceutical companies and tech giants collaborating on drug discovery platforms creates a robust innovation hub.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, supported by rapid digitalization and a growing contract research organization (CRO) sector. Countries like China, India, and South Korea are investing heavily in AI infrastructure and bioinformatics to reduce manufacturing costs and accelerate generic drug development. Government initiatives promoting "AI for Healthcare" are fostering local startup ecosystems and attracting foreign investment.

Key players in the market

Some of the key players in AI Driven Drug Discovery Market include Insilico Medicine, BenevolentAI, Exscientia plc, Recursion Pharmaceuticals, Atomwise Inc., Deep Genomics, Schrodinger, Inc., Evotec SE, Valo Health, Verge Genomics, Healx, XtalPi, Standigm, Cyclica Inc., and Iktos.

Key Developments:

In March 2026, Insilico Medicine announced a strategic research collaboration with ASKA Pharmaceutical Co., Ltd., a specialized pharmaceutical company with a strong focus on internal medicine, obstetrics, and gynecology. This partnership aims to identify novel therapeutic targets with high drug development potential for challenging gynecological conditions, including endometriosis, uterine fibroids, and adenomyosis, by leveraging Insilico's proprietary AI-driven target identification engine, PandaOmics.

Components Covered:

  • Software
  • Services

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Generative AI
  • Computer Vision
  • Other AI Technologies

Drug Types Covered:

  • Small Molecules
  • Large Molecules / Biologics

Therapeutic Areas Covered:

  • Oncology
  • Neurodegenerative Diseases
  • Cardiovascular Diseases
  • Infectious Diseases
  • Metabolic Disorders
  • Immunology
  • Respiratory Diseases
  • Other Therapeutic Areas

Applications Covered:

  • Target Identification & Validation
  • Hit Identification / Molecule Screening
  • Lead Generation
  • Lead Optimization
  • Drug Repurposing
  • Preclinical Testing
  • Clinical Trial Optimization

End Users Covered:

  • Pharmaceutical Companies
  • 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 Driven Drug Discovery Market, By Component

  • 5.1 Software
    • 5.1.1 AI Drug Discovery Platforms
    • 5.1.2 Data Analytics & Modeling Software
    • 5.1.3 Molecular Modeling & Simulation Tools
  • 5.2 Services
    • 5.2.1 AI Consulting Services
    • 5.2.2 Data Processing & Integration Services
    • 5.2.3 Drug Discovery Support Services

6 Global AI Driven Drug Discovery Market, By Technology

  • 6.1 Machine Learning
  • 6.2 Deep Learning
  • 6.3 Natural Language Processing (NLP)
  • 6.4 Generative AI
  • 6.5 Computer Vision
  • 6.6 Other AI Technologies

7 Global AI Driven Drug Discovery Market, By Drug Type

  • 7.1 Small Molecules
  • 7.2 Large Molecules / Biologics

8 Global AI Driven Drug Discovery Market, By Therapeutic Area

  • 8.1 Oncology
  • 8.2 Neurodegenerative Diseases
  • 8.3 Cardiovascular Diseases
  • 8.4 Infectious Diseases
  • 8.5 Metabolic Disorders
  • 8.6 Immunology
  • 8.7 Respiratory Diseases
  • 8.8 Other Therapeutic Areas

9 Global AI Driven Drug Discovery Market, By Application

  • 9.1 Target Identification & Validation
  • 9.2 Hit Identification / Molecule Screening
  • 9.3 Lead Generation
  • 9.4 Lead Optimization
  • 9.5 Drug Repurposing
  • 9.6 Preclinical Testing
  • 9.7 Clinical Trial Optimization

10 Global AI Driven Drug Discovery Market, By End User

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

11 Global AI Driven Drug Discovery Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 Insilico Medicine
  • 14.2 BenevolentAI
  • 14.3 Exscientia plc
  • 14.4 Recursion Pharmaceuticals
  • 14.5 Atomwise Inc.
  • 14.6 Deep Genomics
  • 14.7 Schrodinger, Inc.
  • 14.8 Evotec SE
  • 14.9 Valo Health
  • 14.10 Verge Genomics
  • 14.11 Healx
  • 14.12 XtalPi
  • 14.13 Standigm
  • 14.14 Cyclica Inc.
  • 14.15 Iktos

List of Tables

  • Table 1 Global AI Driven Drug Discovery Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI Driven Drug Discovery Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI Driven Drug Discovery Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI Driven Drug Discovery Market Outlook, By AI Drug Discovery Platforms (2023-2034) ($MN)
  • Table 5 Global AI Driven Drug Discovery Market Outlook, By Data Analytics & Modeling Software (2023-2034) ($MN)
  • Table 6 Global AI Driven Drug Discovery Market Outlook, By Molecular Modeling & Simulation Tools (2023-2034) ($MN)
  • Table 7 Global AI Driven Drug Discovery Market Outlook, By Services (2023-2034) ($MN)
  • Table 8 Global AI Driven Drug Discovery Market Outlook, By AI Consulting Services (2023-2034) ($MN)
  • Table 9 Global AI Driven Drug Discovery Market Outlook, By Data Processing & Integration Services (2023-2034) ($MN)
  • Table 10 Global AI Driven Drug Discovery Market Outlook, By Drug Discovery Support Services (2023-2034) ($MN)
  • Table 11 Global AI Driven Drug Discovery Market Outlook, By Technology (2023-2034) ($MN)
  • Table 12 Global AI Driven Drug Discovery Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 13 Global AI Driven Drug Discovery Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 14 Global AI Driven Drug Discovery Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 15 Global AI Driven Drug Discovery Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 16 Global AI Driven Drug Discovery Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 17 Global AI Driven Drug Discovery Market Outlook, By Other AI Technologies (2023-2034) ($MN)
  • Table 18 Global AI Driven Drug Discovery Market Outlook, By Drug Type (2023-2034) ($MN)
  • Table 19 Global AI Driven Drug Discovery Market Outlook, By Small Molecules (2023-2034) ($MN)
  • Table 20 Global AI Driven Drug Discovery Market Outlook, By Large Molecules / Biologics (2023-2034) ($MN)
  • Table 21 Global AI Driven Drug Discovery Market Outlook, By Therapeutic Area (2023-2034) ($MN)
  • Table 22 Global AI Driven Drug Discovery Market Outlook, By Oncology (2023-2034) ($MN)
  • Table 23 Global AI Driven Drug Discovery Market Outlook, By Neurodegenerative Diseases (2023-2034) ($MN)
  • Table 24 Global AI Driven Drug Discovery Market Outlook, By Cardiovascular Diseases (2023-2034) ($MN)
  • Table 25 Global AI Driven Drug Discovery Market Outlook, By Infectious Diseases (2023-2034) ($MN)
  • Table 26 Global AI Driven Drug Discovery Market Outlook, By Metabolic Disorders (2023-2034) ($MN)
  • Table 27 Global AI Driven Drug Discovery Market Outlook, By Immunology (2023-2034) ($MN)
  • Table 28 Global AI Driven Drug Discovery Market Outlook, By Respiratory Diseases (2023-2034) ($MN)
  • Table 29 Global AI Driven Drug Discovery Market Outlook, By Other Therapeutic Areas (2023-2034) ($MN)
  • Table 30 Global AI Driven Drug Discovery Market Outlook, By Application (2023-2034) ($MN)
  • Table 31 Global AI Driven Drug Discovery Market Outlook, By Target Identification & Validation (2023-2034) ($MN)
  • Table 32 Global AI Driven Drug Discovery Market Outlook, By Hit Identification / Molecule Screening (2023-2034) ($MN)
  • Table 33 Global AI Driven Drug Discovery Market Outlook, By Lead Generation (2023-2034) ($MN)
  • Table 34 Global AI Driven Drug Discovery Market Outlook, By Lead Optimization (2023-2034) ($MN)
  • Table 35 Global AI Driven Drug Discovery Market Outlook, By Drug Repurposing (2023-2034) ($MN)
  • Table 36 Global AI Driven Drug Discovery Market Outlook, By Preclinical Testing (2023-2034) ($MN)
  • Table 37 Global AI Driven Drug Discovery Market Outlook, By Clinical Trial Optimization (2023-2034) ($MN)
  • Table 38 Global AI Driven Drug Discovery Market Outlook, By End User (2023-2034) ($MN)
  • Table 39 Global AI Driven Drug Discovery Market Outlook, By Pharmaceutical Companies (2023-2034) ($MN)
  • Table 40 Global AI Driven Drug Discovery Market Outlook, By Biotechnology Companies (2023-2034) ($MN)
  • Table 41 Global AI Driven Drug Discovery Market Outlook, By Contract Research Organizations (CROs) (2023-2034) ($MN)
  • Table 42 Global AI Driven Drug Discovery Market Outlook, By Academic & Research Institutes (2023-2034) ($MN)
  • Table 43 Global AI Driven 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) are also represented in the same manner as above.