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市场调查报告书
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2007854

人工智慧药物发现平台市场预测至2034年—按平台类型、部署模式、技术、应用、最终用户和地区分類的全球分析

AI Drug Discovery Platforms Market Forecasts to 2034 - Global Analysis By Platform Type, Deployment Mode, 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 的数据，全球 AI 药物发现平台市场预计将在 2026 年达到 48 亿美元，并在预测期内以 21.3% 的复合年增长率成长，到 2034 年达到 226 亿美元。

人工智慧药物发现平台是指利用机器学习、深度学习和预测分析等技术，透过软体主导的运算系统，加速候选药物的辨识、筛检和优化。这些平台整合基因体学、蛋白质体学和临床数据，绘製生物标靶图谱，模拟分子间相互作用，并预测治疗效果和毒性特征。这些平台能够帮助製药和生物技术公司识别癌症治疗标靶、优化先导化合物研发流程、进行药物重定位研究以及设计适应性临床试验。

简化药物研发流程

随着製药公司面临研发成本飙升和传统药物研发流程盈利下滑的困境，精简药物研发流程成为关键驱动因素。人工智慧驱动的平台透过计算筛选数十亿个分子结构，并与检验的目标进行比对，将候选化合物的筛检时间从数年缩短至数週。人工智慧专家与大型製药企业之间的策略合作日益增多，不仅能够产生基于里程碑的合作收益，还能加强对平台在癌症和罕见疾病适应症方面的商业性检验。

对资料隐私和智慧财产权的担忧

对资料隐私和智慧财产权的担忧阻碍了人工智慧药物发现平台的普及应用。这一点在成熟的製药公司中尤其明显，它们不愿与第三方人工智慧供应商共用其专有的基因组资料集和化合物库。关于人工智慧生成分子的智慧财产权归属，监管方面的模糊性为平台开发商和製药合作伙伴带来了法律上的不确定性。这些障碍会延迟企业采用人工智慧药物发现平台的决策，延长销售週期，并限制资料共用协议对于训练高性能人工智慧药物发现模型至关重要。

扩展应用范围，适用于罕见疾病

将人工智慧技术应用于罕见疾病领域蕴藏着巨大的机会。即使在患者群体较小、传统临床经济学难以奏效的疾病领域，人工智慧平台也能帮助识别出具有成本效益的候选药物。包括美国食品药物管理局（FDA）在内的监管机构正在为罕见疾病治疗药物提供快速核准流程，降低上市风险。慈善机构和患者权益倡导组织对罕见疾病研究投入的不断增加，正在推动人工智慧驱动的药物发现能力在目前占据主导地位的肿瘤市场之外，持续发展。

无法成功过渡到临床应用的风险

人工智慧驱动的药物发现平台信誉面临的结构性威胁之一是临床试验失败的风险。人工智慧预测的候选化合物仍需成功通过临床前和临床检验阶段。人工智慧识别的化合物在第二期和第三期临床试验中的高脱落率会削弱製药合作伙伴的信心，并延缓平台的推广应用。监管机构对人工智慧产生的证据包的审查以及缺乏统一的人工智慧药物申请指南，进一步加剧了临床试验推广应用的不确定性。

新冠疫情的影响：

新冠疫情大大加速了人工智慧药物发现平台的应用，因为製药公司迫切需要快速识别抗病毒候选化合物。疫情期间，人工智慧与生物製药公司的合作使得多个符合FDA审查条件的候选化合物在更短的时间内涌现。自疫情爆发以来，对人工智慧药物发现基础设施的结构性投资持续进行，各机构已将平台功能整合到标准的早期药物发现工作流程中。

在预测期内，临床试验设计平台细分市场预计将成为最大的细分市场。

预计在预测期内，临床试验设计平台细分市场将占据最大的市场份额，这主要得益于製药公司面临的降低临床开发成本和提高患者招募效率的日益增长的压力。人工智慧驱动的试验设计工具透过优化通讯协定参数、识别最佳生物标记定义患者群体以及预测脱落率，显着降低了营运成本。在关键市场，基于人工智慧的自适应试验设计获得监管部门的核准不断扩大，进一步推动了该平台的应用。

在预测期内，基于云端的细分市场预计将呈现最高的复合年增长率。

在预测期内，受大规模多组体学资料集在人工智慧模型训练中对可扩展性的需求，以及跨地域协作存取共用药物研发基础设施的需求驱动，基于云端的细分市场预计将呈现最高的成长率。采用云端技术无需对本地运算硬体进行资本投资，并支援新兴生技公司青睐的灵活订阅模式。超大规模资料中心业者大规模云端服务商对生命科学领域云端基础设施的投资正在加速提升云端药物研发工作负载的效能标准。

市占率最大的地区：

在整个预测期内，北美预计将保持最大的市场份额，这得益于其集中了主要企业的製药和生物技术公司、对人工智慧医疗创新领域的大量创业投资投资，以及支持人工智慧药物研发的完善法规结构。美国拥有大多数平台开发商和积极采用人工智慧药物研发解决方案的製药合作伙伴。此外，美国国立卫生研究院 (NIH) 和生物医学高级研究与发展局 (BARDA) 的资助计画正在津贴人工智慧药物研发研究，从而深化创新生态系统。

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，这主要得益于中国、日本和韩国生物技术行业的快速扩张、政府主导的基因组数据基础设施投资以及国内製药行业日益增长的雄心。中国的国家人工智慧发展策略已明确将应用领域锁定在製药业，国家资助的人工智慧药物研发联盟正在加速平台升级。该地区的生物技术投资稳步成长，吸引了来自世界各地的人工智慧药物研发平台供应商与其建立伙伴关係。

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
    • 应客户要求，我们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：需进行可行性检查）。
  • 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 全球人工智慧药物发现平台市场：依平台类型划分

  • 目标识别平台
  • 分子筛检平台
  • 先导药物最适化平台
  • 药物回收平台
  • 临床试验设计平台
  • 生物标记发现平台
  • 其他平台类型

第六章 全球人工智慧药物发现平台市场：依部署模式划分

  • 基于云端的
  • 现场
  • 混合模式
  • SaaS型平台
  • 基于 API 的平台
  • 整合平台

第七章 全球人工智慧药物发现平台市场：依技术划分

  • 机器学习
  • 深度学习
  • 自然语言处理
  • 电脑视觉
  • 预测分析
  • 基于云端的人工智慧
  • 其他技术

第八章 全球人工智慧药物发现平台市场：按应用划分

  • 肿瘤药物研发
  • 神经病学
  • 感染疾病
  • 心血管疾病
  • 罕见疾病
  • 免疫学
  • 其他用途

第九章 全球人工智慧药物发现平台市场：按最终用户划分

  • 製药公司
  • 生技公司
  • CRO
  • 学术机构
  • 医疗机构
  • 政府附属研究机构
  • 其他最终用户

第十章 全球人工智慧药物发现平台市场：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

第十一章 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

第十二章 产业趋势与策略倡议

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

第十三章：公司简介

  • IBM Corporation
  • Google LLC
  • Microsoft Corporation
  • Atomwise Inc.
  • BenevolentAI
  • Insilico Medicine
  • Exscientia plc
  • Recursion Pharmaceuticals
  • Schrodinger, Inc.
  • Deep Genomics
  • Cloud Pharmaceuticals
  • Berg LLC
  • BioSymetrics Inc.
  • Cyclica Inc.
  • Numerate Inc.
  • Owkin Inc.
  • Tempus Labs
  • Relay Therapeutics
Product Code: SMRC34760

According to Stratistics MRC, the Global AI Drug Discovery Platforms Market is accounted for $4.8 billion in 2026 and is expected to reach $22.6 billion by 2034 growing at a CAGR of 21.3% during the forecast period. AI drug discovery platforms refer to software-driven computational systems that apply machine learning, deep learning, and predictive analytics to accelerate the identification, screening, and optimization of drug candidates. They integrate genomic, proteomic, and clinical data to map biological targets, simulate molecular interactions, and predict therapeutic efficacy and toxicity profiles. These platforms support oncology target identification, lead optimization workflows, drug repurposing initiatives, and adaptive clinical trial design for pharmaceutical and biotechnology organizations.

Market Dynamics:

Driver:

Accelerated Drug Pipeline Efficiency

Accelerated drug pipeline efficiency is a primary driver as pharmaceutical companies face escalating R&D costs and diminishing returns from traditional discovery workflows. AI-driven platforms reduce candidate screening timelines from years to weeks by computationally filtering billions of molecular structures against validated targets. Strategic collaborations between AI specialists and major pharmaceutical firms are multiplying, generating milestone-based partnership revenues and reinforcing commercial validation of platform efficacy across oncology and rare disease indications.

Restraint:

Data Privacy and IP Concerns

Data privacy and intellectual property concerns restrain AI drug discovery platform adoption, particularly among established pharmaceutical companies reluctant to share proprietary genomic datasets and compound libraries with third-party AI vendors. Regulatory ambiguity around AI-generated molecular intellectual property ownership creates legal uncertainty for platform developers and pharmaceutical partners. These barriers slow enterprise procurement decisions, extend sales cycles, and constrain data-sharing agreements critical for training high-performance AI discovery models.

Opportunity:

Rare Disease Application Expansion

Rare disease application expansion represents a significant opportunity as AI platforms enable cost-effective drug candidate identification for conditions affecting small patient populations where traditional clinical economics are unfavorable. Regulatory agencies including the FDA offer expedited approval pathways for rare disease therapeutics, reducing time-to-market risk. Growing philanthropic funding and patient advocacy organization investment in rare disease research is creating sustained demand for AI discovery capabilities beyond the oncology-dominated current market.

Threat:

Clinical Translation Failure Risk

Clinical translation failure risk represents a structural threat to AI drug discovery platform credibility, as AI-predicted candidates must still successfully navigate preclinical and clinical validation stages. High attrition rates in Phase II and Phase III trials for AI-identified compounds could erode pharmaceutical partner confidence and slow platform adoption. Regulatory scrutiny of AI-derived evidence packages and the absence of harmonized guidelines for AI-generated drug submissions further amplify translation uncertainty.

Covid-19 Impact:

COVID-19 dramatically accelerated AI drug discovery platform adoption as pharmaceutical firms urgently required rapid antiviral candidate identification capabilities. Pandemic-era collaborations between AI companies and biopharmaceutical organizations produced several FDA-reviewed candidates within compressed timelines. Post-pandemic structural investment in AI discovery infrastructure has persisted, with organizations embedding platform capabilities into standard early-stage discovery workflows.

The clinical trial design platforms segment is expected to be the largest during the forecast period

The clinical trial design platforms segment is expected to account for the largest market share during the forecast period, due to mounting pressure on pharmaceutical companies to reduce clinical development costs and improve patient recruitment efficiency. AI-driven trial design tools optimize protocol parameters, identify optimal biomarker-defined patient populations, and predict dropout probabilities, materially reducing operational expenditure. Regulatory acceptance of adaptive trial designs informed by AI is expanding in key markets, further validating platform adoption.

The cloud-based segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate, driven by the scalability demands of AI model training on massive multi-omics datasets and the need for collaborative multi-site access to shared drug discovery infrastructure. Cloud deployment eliminates capital expenditure on on-premise computing hardware and enables flexible subscription economics preferred by emerging biotech firms. Hyperscaler investments in life sciences cloud infrastructure are accelerating performance benchmarks for cloud-hosted discovery workloads.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to concentration of leading pharmaceutical and biotechnology companies, substantial venture capital investment in AI health innovation, and advanced regulatory frameworks supporting AI drug development. The United States hosts the majority of platform developers and pharmaceutical partners actively deploying AI discovery solutions. NIH and BARDA funding programs are additionally subsidizing AI drug discovery research at academic institutions, deepening the innovation ecosystem.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to rapidly expanding biotechnology sectors in China, Japan, and South Korea, government-backed genomic data infrastructure investments, and growing domestic pharmaceutical industry ambitions. China's national AI development strategy explicitly targets pharmaceutical applications, with state-funded AI drug discovery consortia accelerating platform capabilities. Regional biotech investment volumes are compounding, drawing global AI drug discovery platform vendors into partnership structures.

Key players in the market

Some of the key players in AI Drug Discovery Platforms Market include IBM Corporation, Google LLC, Microsoft Corporation, Atomwise Inc., BenevolentAI, Insilico Medicine, Exscientia plc, Recursion Pharmaceuticals, Schrodinger, Inc., Deep Genomics, Cloud Pharmaceuticals, Berg LLC, BioSymetrics Inc., Cyclica Inc., Numerate Inc., Owkin Inc., Tempus Labs, and Relay Therapeutics.

Key Developments:

In February 2026, Insilico Medicine advanced its AI-generated drug candidate for idiopathic pulmonary fibrosis into Phase II clinical trials, marking a generative AI discovery milestone.

In January 2026, Exscientia plc secured a multi-target oncology drug discovery partnership with a top-ten global pharmaceutical company valued at over $500 million.

In October 2025, Recursion Pharmaceuticals launched an expanded phenomics data platform integrating new cell biology imaging capabilities to enhance multi-disease drug candidate generation.

Platform Types Covered:

  • Target Identification Platforms
  • Molecule Screening Platforms
  • Lead Optimization Platforms
  • Drug Repurposing Platforms
  • Clinical Trial Design Platforms
  • Biomarker Discovery Platforms
  • Other Platform Types

Deployment Modes Covered:

  • Cloud-based
  • On-premise
  • Hybrid Models
  • SaaS-based Platforms
  • API-based Platforms
  • Integrated Platforms

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Computer Vision
  • Predictive Analytics
  • Cloud-based AI
  • Other Technologies

Applications Covered:

  • Oncology Drug Discovery
  • Neurology
  • Infectious Diseases
  • Cardiovascular Diseases
  • Rare Diseases
  • Immunology
  • Other Applications

End Users Covered:

  • Pharmaceutical Companies
  • Biotechnology Firms
  • CROs
  • Academic Institutes
  • Healthcare Organizations
  • Government Research Bodies
  • 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, 3032 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 Drug Discovery Platforms Market, By Platform Type

  • 5.1 Target Identification Platforms
  • 5.2 Molecule Screening Platforms
  • 5.3 Lead Optimization Platforms
  • 5.4 Drug Repurposing Platforms
  • 5.5 Clinical Trial Design Platforms
  • 5.6 Biomarker Discovery Platforms
  • 5.7 Other Platform Types

6 Global AI Drug Discovery Platforms Market, By Deployment Mode

  • 6.1 Cloud-based
  • 6.2 On-premise
  • 6.3 Hybrid Models
  • 6.4 SaaS-based Platforms
  • 6.5 API-based Platforms
  • 6.6 Integrated Platforms

7 Global AI Drug Discovery Platforms Market, By Technology

  • 7.1 Machine Learning
  • 7.2 Deep Learning
  • 7.3 Natural Language Processing
  • 7.4 Computer Vision
  • 7.5 Predictive Analytics
  • 7.6 Cloud-based AI
  • 7.7 Other Technologies

8 Global AI Drug Discovery Platforms Market, By Application

  • 8.1 Oncology Drug Discovery
  • 8.2 Neurology
  • 8.3 Infectious Diseases
  • 8.4 Cardiovascular Diseases
  • 8.5 Rare Diseases
  • 8.6 Immunology
  • 8.7 Other Applications

9 Global AI Drug Discovery Platforms Market, By End User

  • 9.1 Pharmaceutical Companies
  • 9.2 Biotechnology Firms
  • 9.3 CROs
  • 9.4 Academic Institutes
  • 9.5 Healthcare Organizations
  • 9.6 Government Research Bodies
  • 9.7 Other End Users

10 Global AI Drug Discovery Platforms 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 IBM Corporation
  • 13.2 Google LLC
  • 13.3 Microsoft Corporation
  • 13.4 Atomwise Inc.
  • 13.5 BenevolentAI
  • 13.6 Insilico Medicine
  • 13.7 Exscientia plc
  • 13.8 Recursion Pharmaceuticals
  • 13.9 Schrodinger, Inc.
  • 13.10 Deep Genomics
  • 13.11 Cloud Pharmaceuticals
  • 13.12 Berg LLC
  • 13.13 BioSymetrics Inc.
  • 13.14 Cyclica Inc.
  • 13.15 Numerate Inc.
  • 13.16 Owkin Inc.
  • 13.17 Tempus Labs
  • 13.18 Relay Therapeutics

List of Tables

  • Table 1 Global AI Drug Discovery Platforms Market Outlook, By Region (2023-2034)($MN)
  • Table 2 Global AI Drug Discovery Platforms Market Outlook, By Platform Type (2023-2034)($MN)
  • Table 3 Global AI Drug Discovery Platforms Market Outlook, By Target Identification Platforms (2023-2034)($MN)
  • Table 4 Global AI Drug Discovery Platforms Market Outlook, By Molecule Screening Platforms (2023-2034)($MN)
  • Table 5 Global AI Drug Discovery Platforms Market Outlook, By Lead Optimization Platforms (2023-2034)($MN)
  • Table 6 Global AI Drug Discovery Platforms Market Outlook, By Drug Repurposing Platforms (2023-2034)($MN)
  • Table 7 Global AI Drug Discovery Platforms Market Outlook, By Clinical Trial Design Platforms (2023-2034)($MN)
  • Table 8 Global AI Drug Discovery Platforms Market Outlook, By Biomarker Discovery Platforms (2023-2034)($MN)
  • Table 9 Global AI Drug Discovery Platforms Market Outlook, By Other Platform Types (2023-2034)($MN)
  • Table 10 Global AI Drug Discovery Platforms Market Outlook, By Deployment Mode (2023-2034)($MN)
  • Table 11 Global AI Drug Discovery Platforms Market Outlook, By Cloud-based (2023-2034)($MN)
  • Table 12 Global AI Drug Discovery Platforms Market Outlook, By On-premise (2023-2034)($MN)
  • Table 13 Global AI Drug Discovery Platforms Market Outlook, By Hybrid Models (2023-2034)($MN)
  • Table 14 Global AI Drug Discovery Platforms Market Outlook, By SaaS-based Platforms (2023-2034)($MN)
  • Table 15 Global AI Drug Discovery Platforms Market Outlook, By API-based Platforms (2023-2034)($MN)
  • Table 16 Global AI Drug Discovery Platforms Market Outlook, By Integrated Platforms (2023-2034)($MN)
  • Table 17 Global AI Drug Discovery Platforms Market Outlook, By Technology (2023-2034)($MN)
  • Table 18 Global AI Drug Discovery Platforms Market Outlook, By Machine Learning (2023-2034)($MN)
  • Table 19 Global AI Drug Discovery Platforms Market Outlook, By Deep Learning (2023-2034)($MN)
  • Table 20 Global AI Drug Discovery Platforms Market Outlook, By Natural Language Processing (2023-2034)($MN)
  • Table 21 Global AI Drug Discovery Platforms Market Outlook, By Computer Vision (2023-2034)($MN)
  • Table 22 Global AI Drug Discovery Platforms Market Outlook, By Predictive Analytics (2023-2034)($MN)
  • Table 23 Global AI Drug Discovery Platforms Market Outlook, By Cloud-based AI (2023-2034)($MN)
  • Table 24 Global AI Drug Discovery Platforms Market Outlook, By Other Technologies (2023-2034)($MN)
  • Table 25 Global AI Drug Discovery Platforms Market Outlook, By Application (2023-2034)($MN)
  • Table 26 Global AI Drug Discovery Platforms Market Outlook, By Oncology Drug Discovery (2023-2034)($MN)
  • Table 27 Global AI Drug Discovery Platforms Market Outlook, By Neurology (2023-2034)($MN)
  • Table 28 Global AI Drug Discovery Platforms Market Outlook, By Infectious Diseases (2023-2034)($MN)
  • Table 29 Global AI Drug Discovery Platforms Market Outlook, By Cardiovascular Diseases (2023-2034)($MN)
  • Table 30 Global AI Drug Discovery Platforms Market Outlook, By Rare Diseases (2023-2034)($MN)
  • Table 31 Global AI Drug Discovery Platforms Market Outlook, By Immunology (2023-2034)($MN)
  • Table 32 Global AI Drug Discovery Platforms Market Outlook, By Other Applications (2023-2034)($MN)
  • Table 33 Global AI Drug Discovery Platforms Market Outlook, By End User (2023-2034)($MN)
  • Table 34 Global AI Drug Discovery Platforms Market Outlook, By Pharmaceutical Companies (2023-2034)($MN)
  • Table 35 Global AI Drug Discovery Platforms Market Outlook, By Biotechnology Firms (2023-2034)($MN)
  • Table 36 Global AI Drug Discovery Platforms Market Outlook, By CROs (2023-2034)($MN)
  • Table 37 Global AI Drug Discovery Platforms Market Outlook, By Academic Institutes (2023-2034)($MN)
  • Table 38 Global AI Drug Discovery Platforms Market Outlook, By Healthcare Organizations (2023-2034)($MN)
  • Table 39 Global AI Drug Discovery Platforms Market Outlook, By Government Research Bodies (2023-2034)($MN)
  • Table 40 Global AI Drug Discovery Platforms Market Outlook, By Other End Users (2023-2034)($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.