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市场调查报告书
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2017580

人工智慧在药物研发领域的市场：按技术、治疗领域、应用、最终用户和部署方式划分——全球市场预测（2026-2032 年）

Artificial Intelligence in Drug Discovery Market by Technology, Therapeutic Area, Application, End User, Deployment Mode - Global Forecast 2026-2032
出版日期: | 出版商: 360iResearch | 英文 192 Pages | 商品交期: 最快1-2个工作天内

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2025 年，人工智慧 (AI) 药物发现市场价值为 15.5 亿美元，预计到 2026 年将成长至 18.1 亿美元，复合年增长率为 17.90%，到 2032 年将达到 49.3 亿美元。

主要市场统计数据
基准年 2025 15.5亿美元
预计年份：2026年 18.1亿美元
预测年份 2032 49.3亿美元
复合年增长率 (%) 17.90%

为正在药物研发流程和营运模式中应用人工智慧的组织定义一个策略转折点。

人工智慧已从单纯的研究兴趣发展成为一项核心能力，它重塑了治疗候选药物的发现、优化和风险规避方式。本文将人工智慧的现状置于演算法进步、生物数据扩展和计算化学突破的轨迹中，这些进展促成了生成模型、预测分析和结构模拟在工业工作流程中的实用化。製药公司、生物技术Start-Ups、合约研究组织 (CRO) 和学术实验室的相关人员正在将人工智慧整合到整个药物发现价值链中，以缩短设计週期、提高转化研究的准确性并指南策略性产品组合的选择。

本书揭示了加速人工智慧主导药物发现并改变研发决策框架的关键技术和组织变革。

药物发现领域正经历一系列相互关联的变革，这些变革远不止于演算法的改进。首先，蛋白质结构预测的突破性进展降低了标靶表征的门槛，使研究团队能够模拟结合口袋和结构动力学。这使得先导化合物化合物的发现和最佳化速度达到了前所未有的水平。其次，生成式化学模型的成熟使得新型骨架的建构成为可能，这些骨架可以更快地合成和测试，从而将虚拟设计与实验可行性研究结合。第三，整合基因体学、蛋白质体学、高内涵成像和真实世界临床证据等多模态数据，能够更详细地展现疾病生物学特征，进而提高ADMET和毒性预测的准确性。

本研究分析了美国在 2025 年征收的关税对人工智慧驱动的药物发现流程和基础设施决策的多层次运作和战略影响。

2025年实施的关税政策为整个生物製药供应链以及支援人工智慧主导药物研发的软硬体体系带来了担忧和实际调整。对于依赖高效能GPU或实验室设备等专用硬体且这些硬体通常从海外采购的机构而言，关税增加了其筹资策略的复杂性，并迫使企业重新评估本地部署和云端方案的总体拥有成本（TCO）。为此，许多团队调整了其采用策略。一些团队加快了云端部署以规避进口瓶颈，而另一些团队则投资于在地采购或签订长期供应商合约以确保关键设备的供应。

我们确定应用、技术、治疗领域、最终用户和部署模式之间的策略交集，以推动能够带来最大价值的成果。

全面观点人工智慧应用有助于明确哪些领域的投资能带来最直接的科学和营运回报。在ADMET和毒性预测领域，动态、药物动力学和毒性预测技术的进步使研究团队能够更早筛选候选化合物，并降低后期试验的脱落率。在临床试验优化方面，优化患者招募策略和试验设计已被证明能够有效提高试验效率和代表性。先导化合物筛选流程利用高通量筛检、In Silico标靶检验和虚拟筛检来更快地识别有前景的化合物。先导化合物优化越来越依赖从头药物发现、定量构效关係（QSAR）建模和基于结构的药物发现，这些方法共同迭代地提高分子效力和开发潜力。蛋白质结构预测，在基于第一原理计算、同源建模和分子动力学模拟的支持下，在目标检验和合理设计中继续发挥至关重要的作用。

本文说明了美洲、欧洲、中东和非洲以及亚太地区的生态系统将如何影响技术采纳速度、监管重点和合作网络。

每个地区的实际情况决定了人工智慧驱动的药物发现的实施和规模化方式。在美洲，强大的创业投资系统和成熟的生物技术丛集支援演算法创新快速商业化。同时，接近性大型製药企业的研发中心也促进了早期应用和产业合作。该地区与监管机构的对话日益侧重于模型检验、透明度和证据标准，这些标准将计算预测与安全性和有效性评估联繫起来。因此，研发项目往往优先考虑可重复性和审计追踪，以满足严格的合规要求。

分析竞争对手和合作模式，以推动成熟製药公司、人工智慧优先型Start-Ups和服务供应商。

竞争格局的特点是角色互补而非纯粹的零和博弈。成熟的製药公司正利用其深厚的专业知识、广泛的临床研发管线和丰富的监管经验，将人工智慧主导的工作流程扩展到后期研发阶段。他们通常优先考虑将人工智慧的输出结果整合到现有的决策管治中，同时保持严格的检验标准。同时，人工智慧原生Start-Ups正凭藉其专业的建模技术、敏捷的工程方法以及对新资料来源的探索精神，为快速迭代和利基创新创造机会。受託研究机构（CRO）和服务供应商正在将人工智慧融入其服务产品中，以缩短週期，并为寻求外包药物研发能力的客户提供差异化的价值提案。

领导者在负责任地扩大人工智慧在药物发现领域的应用，同时保持科学诚信和应对力。

领导者应先将人工智慧倡议与明确的科学和业务目标结合，而不是将工具部署本身作为最终目标。这首先要选择资料品质充足且结果可衡量的应用场景，例如迭代式先导化合物优化或靶向毒性分级，并建立结合预测性能和营运影响的成功指标。接下来，要投资数据基础建设。开发高品质的内部资料集，并辅以管理良好的外部资料来源，同时实施元资料标准，以提高模型的可解释性和可复现性。同时，投资于针对生命科学工作流程客製化的机器学习运作（MLOps）可以缩短部署时间，并创建监管机构和安全团队所需的审计追踪。

概述了一种严谨的混合方法研究途径，该方法结合了专家访谈、文献整合、可重复性检查和透明的局限性声明。

本研究整合了多方面的证据，旨在对人工智慧在药物研发中的应用呈现平衡的观点。主要资料来源包括对製药公司研发部​​门、生物技术公司、受託研究机构和学术研究中心的专家进行的结构化访谈，以及对记录调查方法调查方法进展的同行评审文献和预印本的技术审查。次要资料来源包括公开的监管指南、企业关于平台采用情况的资讯披露，以及对成功将人工智慧与实验室流程相结合的案例研究的分析。方法论主张的定量检验是透过技术文献中报告的可重复性评估以及在有独立基准资料集的情况下进行的比较评估来实现的。

本报告总结了在不断变化的政策和营运限制下，将人工智慧主导的能力转化为永续药物发现优势的关键战略意义。

对于那些致力于提升药物研发速度和转换准确性的机构而言，人工智慧在药物研发领域已不再是可选项。然而，这项技术的影响并非一成不变。为了获得可重复的结果，它需要与实验设计进行精心整合，并辅以稳健的资料管治和循序渐进的检验策略。那些专注于高价值应用案例、重视资料管理并组建跨职能团队的领导者，将比那些只进行孤立的先导计画而缺乏端到端整合的领导者获得更大的收益。

目录

第一章 引言

第二章 分析方法

  • 分析设计
  • 分析框架
  • 市场规模预测
  • 数据三角测量
  • 分析结果
  • 分析的前提条件
  • 分析的局限性

第三章执行摘要

  • 首席体验长的观点
  • 市场规模和成长趋势
  • 市占率分析（2025 年）
  • FPNV定位矩阵（2025）
  • 新的商机
  • 下一代经营模式
  • 产业蓝图

第四章 市场概览

  • 产业生态系与价值链分析
  • 波特五力分析
  • PESTEL 分析
  • 市场展望
  • 市场进入（GTM）策略

第五章 市场洞察

  • 消费者分析与最终用户观点
  • 消费者体验基准测试
  • 机会映射
  • 分销通路分析
  • 价格趋势分析
  • 监理合规和标准框架
  • ESG与永续性分析
  • 中断和风险情景
  • 成本效益分析（ROI）和成本效益分析（CBA）

第六章：美国关税的累积影响（2025 年）

第七章：人工智慧（AI）的累积影响（2025 年）

第八章：人工智慧在药物发现领域的市场：依技术划分

  • 电脑视觉
  • 深度学习
  • 机器学习
  • 自然语言处理

第九章：人工智慧在药物研发领域的市场：依治疗领域划分

  • 心血管疾病
  • 中枢神经系统
  • 感染疾病
  • 瘤

第十章：人工智慧在药物发现领域的市场：按应用划分

  • ADMET毒性预测
    • 动态预测
    • 药物动力学预测
    • 毒性预测
  • 临床试验的优化
    • 招募受试者
    • 测试设计优化
  • 命中识别
    • 高通量筛检
    • 电脑模拟标靶检验
    • 虚拟筛检
  • 先导药物最适化
    • 新药设计
    • QSAP（定量结构-物理相关性）
    • SBDD（基于结构的药物发现）
  • 蛋白质结构预测
    • 第一原理计算
    • 同源建模
    • 分子动力学模拟

第十一章：人工智慧在药物发现领域的市场：按最终用户划分

  • 学术和研究机构
  • 生技公司
  • 合约研究机构
  • 製药公司

第十二章：人工智慧在药物研发领域的市场：以部署方式划分

  • 云
  • 杂交种
  • 现场

第十三章：人工智慧在药物研发领域的市场：按地区划分

  • 北美洲和南美洲
    • 北美洲
    • 拉丁美洲
  • 欧洲、中东和非洲
    • 欧洲
    • 中东
    • 非洲
  • 亚太地区

第十四章：人工智慧在药物研发领域的市场：以群体划分

  • ASEAN
  • GCC
  • EU
  • BRICS
  • G7
  • NATO

第十五章：人工智慧在药物研发领域的市场：依国家划分

  • 我们
  • 加拿大
  • 墨西哥
  • 巴西
  • 英国
  • 德国
  • 法国
  • 俄罗斯
  • 义大利
  • 西班牙
  • 中国
  • 印度
  • 日本
  • 澳洲
  • 韩国

第十六章：美国药物研发领域的人工智慧市场

第十七章：中国药物研发领域的人工智慧市场

第十八章 竞争格局

  • 市场集中度分析（2025 年）
    • 浓度比（CR）
    • 赫芬达尔-赫希曼指数 (HHI)
  • 近期趋势及影响分析（2025 年）
  • 产品系列分析（2025 年）
  • 基准分析（2025 年）
  • AstraZeneca plc
  • Atomwise, Inc.
  • BenevolentAI Limited
  • Exscientia plc
  • Iktos SAS
  • Insilico Medicine
  • insitro, Inc.
  • Isomorphic Labs Limited
  • Novartis AG
  • Owkin, Inc.
  • Pfizer Inc.
  • Recursion Pharmaceuticals
  • Schrodinger, Inc.
  • Tempus Labs, Inc.
Product Code: MRR-031BF22F953D

The Artificial Intelligence in Drug Discovery Market was valued at USD 1.55 billion in 2025 and is projected to grow to USD 1.81 billion in 2026, with a CAGR of 17.90%, reaching USD 4.93 billion by 2032.

KEY MARKET STATISTICS
Base Year [2025] USD 1.55 billion
Estimated Year [2026] USD 1.81 billion
Forecast Year [2032] USD 4.93 billion
CAGR (%) 17.90%

Defining the strategic inflection point for organizations adopting artificial intelligence across drug discovery pipelines and operational models

Artificial intelligence has evolved from a research curiosity into a core capability reshaping how therapeutic candidates are discovered, optimized, and de-risked. This introduction situates the current moment in a trajectory where algorithmic advances, expanding biological data, and computational chemistry breakthroughs converge to make generative models, predictive analytics, and structural simulations practical for industrial workflows. Stakeholders across pharmaceutical firms, biotechnology startups, contract research organizations, and academic labs are integrating AI across the discovery value chain to shorten design cycles, improve translational accuracy, and inform strategic portfolio choices.

As organizations adapt, the central questions pivot from whether AI can add value to how it should be governed, validated, and scaled. Key considerations now include aligning AI initiatives with experimental throughput, defining realistic benchmarks for in silico predictions, and integrating AI outputs with wet-lab pipelines so that human expertise and computational models complement each other. Moreover, leadership must contend with operational trade-offs-choosing between cloud-native platforms that support rapid iteration and on-premises deployments that meet stringent data governance requirements. In short, the next phase of AI in drug discovery emphasizes disciplined integration, reproducible validation, and strategic prioritization of candidature where AI can produce measurable value.

Unpacking the pivotal technological and organizational shifts that are accelerating AI-driven drug discovery and transforming R&D decision frameworks

The landscape of drug discovery is being transformed by several interlocking shifts that extend beyond algorithmic improvements alone. First, breakthroughs in protein structure prediction have lowered barriers to target characterization, enabling teams to model binding pockets and conformational dynamics that inform hit discovery and lead optimization with unprecedented speed. Second, the maturation of generative chemistry models allows ideation of novel scaffolds that can be synthesized and tested more rapidly, linking virtual designs to experimental feasibility considerations. Third, integration of multimodal data-combining genomics, proteomics, high-content imaging, and real-world clinical evidence-permits richer representations of disease biology that enhance ADMET and toxicity prediction performance.

Concurrently, enterprise readiness has improved as MLOps practices tailored to scientific workflows bring reproducibility and pipeline governance into focus. Investment in explainable AI and interpretability methods is helping regulatory and safety teams engage with model outputs more confidently. Additionally, an expanding ecosystem of partnerships among academic groups, biotech innovators, and platform providers is accelerating knowledge diffusion while creating new commercialization pathways. Together, these shifts are not only improving individual capabilities but also changing how teams are organized, how experiments are prioritized, and how risk is managed across the drug development continuum.

Analyzing the layered operational and strategic consequences of United States tariffs in 2025 on AI-enabled drug discovery pipelines and infrastructure decisions

Tariff policy enacted in 2025 introduced anxieties and pragmatic adjustments across biopharma supply chains and the software-hardware stack that supports AI-driven discovery. For organizations that rely on specialized hardware, such as high-performance GPUs, or on laboratory instrumentation sourced internationally, tariffs increased the complexity of sourcing strategies and compelled firms to reassess total cost of ownership for on-premises compute versus cloud alternatives. In response, many teams recalibrated their deployment decisions: some accelerated cloud adoption to avoid importation bottlenecks, while others invested in localized procurement and long-term supplier agreements to secure essential equipment.

Beyond hardware, tariffs influenced the structure of international research collaborations. Licensing negotiations and cross-border data transfer agreements were re-examined to ensure resilience against shifting trade barriers. This led to a more cautious approach to overseas manufacturing partnerships for synthesized compounds and an emphasis on distributed development models that localize critical capabilities. At the same time, regulatory coordination and cross-jurisdictional validation efforts gained priority to preserve continuity in multi-site clinical programs and preclinical workflows. While tariffs created near-term dislocations, they also highlighted the strategic value of flexible infrastructure, diversified supplier networks, and governance frameworks that can absorb policy volatility without disrupting discovery momentum.

Revealing strategic intersection points across application, technology, therapeutic area, end user, and deployment mode segmentation that drive highest value outcomes

A comprehensive view of AI applications clarifies where investments yield the most immediate scientific and operational returns. In the space of ADMET and toxicology prediction, advances in pharmacodynamics prediction, pharmacokinetics prediction, and toxicity prediction are enabling teams to triage candidates earlier and reduce attrition in later stages. Clinical trial optimization is benefiting from patient recruitment strategies and trial design optimization that increase trial efficiency and enhance representativeness. Hit identification workflows draw value from high-throughput screening, in silico target validation, and virtual screening to surface plausible chemical matter faster. Lead optimization is increasingly driven by de novo drug design, quantitative structure-activity relationship modeling, and structure-based drug design that together iterate molecules toward potency and developability. Protein structure prediction, supported by ab initio modeling, homology modeling, and molecular dynamics simulation, remains foundational for both target validation and rational design.

Across enabling technologies, deep learning and machine learning techniques power feature extraction and predictive modeling, while computer vision interprets high-content imaging and phenotypic assays to connect molecular perturbations with cellular responses. Natural language processing organizes and mines the vast corpus of biomedical literature, patents, and clinical notes to reveal prior art and mechanistic hypotheses. Therapeutically, AI adoption shows strong alignment with oncology and infectious diseases where molecular targets and high-throughput readouts accelerate learning cycles; cardiovascular and central nervous system programs also leverage predictive models but face unique translational challenges tied to physiology and clinical endpoints. The end-user landscape includes academic and research institutes that push methodological frontiers, biotechnology companies that marry AI with nimble experimental platforms, contract research organizations that embed predictive tools to reduce timelines, and pharmaceutical companies that integrate AI across enterprise R&D. Deployment choices-cloud-based, hybrid, and on-premises-reflect trade-offs among speed, cost, data governance, and regulatory concerns, prompting organizations to tailor infrastructure strategies to their data sensitivity and collaboration models.

Taken together, this segmentation structure underscores that value accrues where domain-specific models intersect with high-quality data and aligned operational processes. Strategic clarity about which application-technology-therapeutic-end user combinations to prioritize enables organizations to sequence pilots and build reusable capabilities rather than dispersing resources across disconnected experiments.

Explaining how regional ecosystems across the Americas, Europe Middle East & Africa, and Asia-Pacific shape adoption velocity, regulatory focus, and collaboration networks

Regional realities shape how AI-enabled drug discovery is implemented and scaled. In the Americas, strong venture capital ecosystems and mature biotech clusters support rapid commercialization of algorithmic innovations, while proximity to large pharmaceutical R&D centers facilitates early adoption and industrial partnerships. Regulatory dialogues with authorities in this region increasingly focus on model validation, transparency, and evidence standards that link computational predictions to safety and efficacy assessments. Consequently, development programs tend to emphasize reproducibility and audit trails that satisfy stringent compliance requirements.

Europe, Middle East & Africa demonstrates a diverse mosaic of academic excellence and public-private consortia that advance foundational methods and translational research. Regulatory frameworks across European jurisdictions are evolving to address AI-specific concerns, and cross-border collaborations are common, leveraging national strengths in specific therapeutic areas. In the Middle East and Africa, capacity-building initiatives and investment in local infrastructure are beginning to enable participation in global discovery networks, although challenges around data availability and standardized clinical datasets remain.

Asia-Pacific exhibits rapid deployment of AI in discovery, supported by large patient populations, significant public and private investment in life sciences, and robust manufacturing capabilities. Talent flows between hubs in East Asia, South Asia, and Oceania support a dynamic ecosystem where startups and established firms experiment with both cloud-native and hybrid deployment architectures. Across all regions, cross-border partnerships remain a catalyst for innovation, but regional regulatory nuances, talent availability, and infrastructure constraints shape how quickly discoveries transition into clinical development and commercial programs.

Mapping competitive and collaborative patterns among pharmaceutical incumbents, AI-first startups, and service providers that drive adoption and partner selection

The competitive landscape is characterized by complementary roles rather than pure zero-sum dynamics. Established pharmaceutical companies leverage deep domain knowledge, extensive clinical pipelines, and regulatory experience to scale AI-driven workflows into late-stage development. They often prioritize integrating AI outputs into existing decision governance while maintaining stringent validation standards. In parallel, AI-native startups bring specialized modeling expertise, agile engineering practices, and willingness to pursue novel data sources, creating opportunities for fast iteration and niche innovation. Contract research organizations and service providers are embedding AI into their service offerings to reduce cycle times and provide differentiated value propositions for clients seeking externalized discovery capabilities.

Collaborative models range from strategic alliances and co-development projects to technology licensing and data-sharing consortia. These arrangements frequently involve academic groups that contribute foundational science and bespoke algorithmic approaches. Cloud and infrastructure providers play an enabling role, supplying scalable compute and platforms that host collaborative workspaces, model registries, and reproducible pipelines. Across these interactions, successful players differentiate themselves through transparent validation, clear IP frameworks, and demonstrable ability to translate computational hypotheses into experimental results. Buyers and partners evaluate vendors not only on algorithmic sophistication but on integration maturity, data stewardship practices, and evidence of real-world impact.

Actionable strategic and operational priorities for leaders to scale AI in drug discovery responsibly while preserving scientific integrity and regulatory readiness

Leaders should start by aligning AI initiatives to clearly defined scientific and business objectives rather than pursuing tool adoption for its own sake. This begins with selecting use cases where data quality is sufficient and outcomes can be measured, such as iterative lead optimization or targeted toxicity triage, and then establishing success metrics that combine predictive performance with operational impact. Next, invest in data foundations: curate high-quality internal datasets, augment them with well-governed external sources, and implement metadata standards that improve model interpretability and reproducibility. Parallel investments in MLOps tailored to life-science workflows will reduce time to deploy and create audit trails that regulators and safety teams require.

Operationally, build interdisciplinary teams that pair computational scientists with medicinal chemists, toxicologists, and clinical scientists to ensure model outputs are actionable. Adopt a staged validation approach where models inform experiments in confined pilots before being integrated into broader decision frameworks. For procurement and infrastructure, weigh cloud, hybrid, and on-premises trade-offs against data sensitivity, speed of iteration, and total cost of ownership; negotiate supplier agreements that include data portability and service-level commitments. Finally, define governance that addresses IP, data privacy, and ethical use, and establish continuous learning processes so insights from experiments feed back into model refinement. By sequencing these actions, organizations can scale AI capabilities responsibly while preserving scientific rigor.

Outlining a rigorous mixed-methods research approach combining expert interviews, literature synthesis, reproducibility checks, and transparent limitation statements

This research synthesizes multiple evidence streams to produce a balanced view of AI applications in drug discovery. Primary inputs included structured interviews with domain experts across pharmaceutical R&D, biotechnology firms, contract research organizations, and academic research centers, coupled with technical reviews of peer-reviewed literature and preprints that document methodological advances. Secondary inputs involved analysis of publicly available regulatory guidance, company disclosures regarding platform deployments, and case studies that illustrate successful integrations of AI and wet-lab processes. Quantitative validation of methodological claims drew on reproducibility assessments reported in technical sources and comparative evaluations where independent benchmark datasets were available.

Analytic methods emphasized triangulation: combining expert perspectives with literature evidence and documented case examples to surface robust patterns rather than rely on single-study findings. Where proprietary datasets or vendor claims were cited in source materials, findings were cross-referenced against independent technical evaluations or reproduced results when possible. The research acknowledges limitations, including uneven availability of detailed performance metrics from private companies, variability in dataset standards across institutions, and the rapid pace of methodological change that can outstrip static reporting. To mitigate these constraints, the analysis highlights recurring themes corroborated by multiple sources and explicitly notes areas where further primary research or technical benchmarking is warranted.

Summarizing the essential strategic takeaways that translate AI-driven capabilities into durable discovery advantages under shifting policy and operational constraints

AI in drug discovery is no longer optional for organizations seeking to improve discovery velocity and translational accuracy. The technology's impact is conditional: it requires deliberate integration with experimental design, robust data governance, and phased validation strategies to deliver reproducible outcomes. Leaders who focus on high-value use cases, invest in data stewardship, and establish cross-functional teams will realize disproportionate benefits compared with those who pursue isolated pilots without end-to-end integration.

Moreover, geopolitical and policy factors, such as tariff-induced supply chain adjustments and regional regulatory variation, underscore the importance of flexible infrastructure and diversified partnerships. Success depends on coupling technical excellence with operational discipline: clear metrics, transparent validation, and governance frameworks that address IP, ethics, and regulatory expectations. By prioritizing these elements, organizations can convert algorithmic promise into sustainable capabilities that accelerate therapeutic discovery and improve patient outcomes.

Table of Contents

1. Preface

  • 1.1. Objectives of the Study
  • 1.2. Market Definition
  • 1.3. Market Segmentation & Coverage
  • 1.4. Years Considered for the Study
  • 1.5. Currency Considered for the Study
  • 1.6. Language Considered for the Study
  • 1.7. Key Stakeholders

2. Research Methodology

  • 2.1. Introduction
  • 2.2. Research Design
    • 2.2.1. Primary Research
    • 2.2.2. Secondary Research
  • 2.3. Research Framework
    • 2.3.1. Qualitative Analysis
    • 2.3.2. Quantitative Analysis
  • 2.4. Market Size Estimation
    • 2.4.1. Top-Down Approach
    • 2.4.2. Bottom-Up Approach
  • 2.5. Data Triangulation
  • 2.6. Research Outcomes
  • 2.7. Research Assumptions
  • 2.8. Research Limitations

3. Executive Summary

  • 3.1. Introduction
  • 3.2. CXO Perspective
  • 3.3. Market Size & Growth Trends
  • 3.4. Market Share Analysis, 2025
  • 3.5. FPNV Positioning Matrix, 2025
  • 3.6. New Revenue Opportunities
  • 3.7. Next-Generation Business Models
  • 3.8. Industry Roadmap

4. Market Overview

  • 4.1. Introduction
  • 4.2. Industry Ecosystem & Value Chain Analysis
    • 4.2.1. Supply-Side Analysis
    • 4.2.2. Demand-Side Analysis
    • 4.2.3. Stakeholder Analysis
  • 4.3. Porter's Five Forces Analysis
  • 4.4. PESTLE Analysis
  • 4.5. Market Outlook
    • 4.5.1. Near-Term Market Outlook (0-2 Years)
    • 4.5.2. Medium-Term Market Outlook (3-5 Years)
    • 4.5.3. Long-Term Market Outlook (5-10 Years)
  • 4.6. Go-to-Market Strategy

5. Market Insights

  • 5.1. Consumer Insights & End-User Perspective
  • 5.2. Consumer Experience Benchmarking
  • 5.3. Opportunity Mapping
  • 5.4. Distribution Channel Analysis
  • 5.5. Pricing Trend Analysis
  • 5.6. Regulatory Compliance & Standards Framework
  • 5.7. ESG & Sustainability Analysis
  • 5.8. Disruption & Risk Scenarios
  • 5.9. Return on Investment & Cost-Benefit Analysis

6. Cumulative Impact of United States Tariffs 2025

7. Cumulative Impact of Artificial Intelligence 2025

8. Artificial Intelligence in Drug Discovery Market, by Technology

  • 8.1. Computer Vision
  • 8.2. Deep Learning
  • 8.3. Machine Learning
  • 8.4. Natural Language Processing

9. Artificial Intelligence in Drug Discovery Market, by Therapeutic Area

  • 9.1. Cardiovascular Diseases
  • 9.2. Central Nervous System
  • 9.3. Infectious Diseases
  • 9.4. Oncology

10. Artificial Intelligence in Drug Discovery Market, by Application

  • 10.1. ADMET & Toxicology Prediction
    • 10.1.1. Pharmacodynamics Prediction
    • 10.1.2. Pharmacokinetics Prediction
    • 10.1.3. Toxicity Prediction
  • 10.2. Clinical Trial Optimization
    • 10.2.1. Patient Recruitment
    • 10.2.2. Trial Design Optimization
  • 10.3. Hit Identification
    • 10.3.1. High Throughput Screening
    • 10.3.2. In Silico Target Validation
    • 10.3.3. Virtual Screening
  • 10.4. Lead Optimization
    • 10.4.1. De Novo Drug Design
    • 10.4.2. Quantitative Structure Activity Relationship
    • 10.4.3. Structure Based Drug Design
  • 10.5. Protein Structure Prediction
    • 10.5.1. Ab Initio Modeling
    • 10.5.2. Homology Modeling
    • 10.5.3. Molecular Dynamics Simulation

11. Artificial Intelligence in Drug Discovery Market, by End User

  • 11.1. Academic & Research Institutes
  • 11.2. Biotechnology Companies
  • 11.3. Contract Research Organizations
  • 11.4. Pharmaceutical Companies

12. Artificial Intelligence in Drug Discovery Market, by Deployment Mode

  • 12.1. Cloud Based
  • 12.2. Hybrid
  • 12.3. On Premises

13. Artificial Intelligence in Drug Discovery Market, by Region

  • 13.1. Americas
    • 13.1.1. North America
    • 13.1.2. Latin America
  • 13.2. Europe, Middle East & Africa
    • 13.2.1. Europe
    • 13.2.2. Middle East
    • 13.2.3. Africa
  • 13.3. Asia-Pacific

14. Artificial Intelligence in Drug Discovery Market, by Group

  • 14.1. ASEAN
  • 14.2. GCC
  • 14.3. European Union
  • 14.4. BRICS
  • 14.5. G7
  • 14.6. NATO

15. Artificial Intelligence in Drug Discovery Market, by Country

  • 15.1. United States
  • 15.2. Canada
  • 15.3. Mexico
  • 15.4. Brazil
  • 15.5. United Kingdom
  • 15.6. Germany
  • 15.7. France
  • 15.8. Russia
  • 15.9. Italy
  • 15.10. Spain
  • 15.11. China
  • 15.12. India
  • 15.13. Japan
  • 15.14. Australia
  • 15.15. South Korea

16. United States Artificial Intelligence in Drug Discovery Market

17. China Artificial Intelligence in Drug Discovery Market

18. Competitive Landscape

  • 18.1. Market Concentration Analysis, 2025
    • 18.1.1. Concentration Ratio (CR)
    • 18.1.2. Herfindahl Hirschman Index (HHI)
  • 18.2. Recent Developments & Impact Analysis, 2025
  • 18.3. Product Portfolio Analysis, 2025
  • 18.4. Benchmarking Analysis, 2025
  • 18.5. AstraZeneca plc
  • 18.6. Atomwise, Inc.
  • 18.7. BenevolentAI Limited
  • 18.8. Exscientia plc
  • 18.9. Iktos SAS
  • 18.10. Insilico Medicine
  • 18.11. insitro, Inc.
  • 18.12. Isomorphic Labs Limited
  • 18.13. Novartis AG
  • 18.14. Owkin, Inc.
  • 18.15. Pfizer Inc.
  • 18.16. Recursion Pharmaceuticals
  • 18.17. Schrodinger, Inc.
  • 18.18. Tempus Labs, Inc.

LIST OF FIGURES

  • FIGURE 1. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, 2018-2032 (USD MILLION)
  • FIGURE 2. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SHARE, BY KEY PLAYER, 2025
  • FIGURE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, FPNV POSITIONING MATRIX, 2025
  • FIGURE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY REGION, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY GROUP, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 11. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 12. UNITED STATES ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, 2018-2032 (USD MILLION)
  • FIGURE 13. CHINA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, 2018-2032 (USD MILLION)

LIST OF TABLES

  • TABLE 1. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, 2018-2032 (USD MILLION)
  • TABLE 2. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COMPUTER VISION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COMPUTER VISION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COMPUTER VISION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEEP LEARNING, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEEP LEARNING, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEEP LEARNING, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MACHINE LEARNING, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MACHINE LEARNING, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 11. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MACHINE LEARNING, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 12. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 13. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 14. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 15. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 16. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 17. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 18. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 19. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CENTRAL NERVOUS SYSTEM, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 20. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CENTRAL NERVOUS SYSTEM, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 21. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CENTRAL NERVOUS SYSTEM, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 22. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY INFECTIOUS DISEASES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 23. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY INFECTIOUS DISEASES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 24. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY INFECTIOUS DISEASES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 25. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ONCOLOGY, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 26. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ONCOLOGY, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 27. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ONCOLOGY, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 28. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 29. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 30. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 31. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 32. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 33. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACODYNAMICS PREDICTION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 34. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACODYNAMICS PREDICTION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 35. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACODYNAMICS PREDICTION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 36. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACOKINETICS PREDICTION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 37. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACOKINETICS PREDICTION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 38. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACOKINETICS PREDICTION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 39. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TOXICITY PREDICTION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 40. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TOXICITY PREDICTION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 41. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TOXICITY PREDICTION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 42. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 43. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 44. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 45. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 46. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PATIENT RECRUITMENT, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 47. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PATIENT RECRUITMENT, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 48. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PATIENT RECRUITMENT, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 49. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TRIAL DESIGN OPTIMIZATION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 50. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TRIAL DESIGN OPTIMIZATION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 51. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TRIAL DESIGN OPTIMIZATION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 52. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 53. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 54. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 55. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 56. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIGH THROUGHPUT SCREENING, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 57. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIGH THROUGHPUT SCREENING, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 58. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIGH THROUGHPUT SCREENING, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 59. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY IN SILICO TARGET VALIDATION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 60. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY IN SILICO TARGET VALIDATION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 61. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY IN SILICO TARGET VALIDATION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 62. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY VIRTUAL SCREENING, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 63. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY VIRTUAL SCREENING, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 64. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY VIRTUAL SCREENING, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 65. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 66. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 67. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 68. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 69. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DE NOVO DRUG DESIGN, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 70. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DE NOVO DRUG DESIGN, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 71. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DE NOVO DRUG DESIGN, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 72. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 73. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 74. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 75. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY STRUCTURE BASED DRUG DESIGN, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 76. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY STRUCTURE BASED DRUG DESIGN, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 77. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY STRUCTURE BASED DRUG DESIGN, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 78. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 79. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 80. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 81. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 82. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY AB INITIO MODELING, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 83. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY AB INITIO MODELING, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 84. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY AB INITIO MODELING, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 85. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HOMOLOGY MODELING, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 86. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HOMOLOGY MODELING, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 87. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HOMOLOGY MODELING, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 88. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MOLECULAR DYNAMICS SIMULATION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 89. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MOLECULAR DYNAMICS SIMULATION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 90. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MOLECULAR DYNAMICS SIMULATION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 91. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 92. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ACADEMIC & RESEARCH INSTITUTES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 93. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ACADEMIC & RESEARCH INSTITUTES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 94. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ACADEMIC & RESEARCH INSTITUTES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 95. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY BIOTECHNOLOGY COMPANIES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 96. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY BIOTECHNOLOGY COMPANIES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 97. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY BIOTECHNOLOGY COMPANIES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 98. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CONTRACT RESEARCH ORGANIZATIONS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 99. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CONTRACT RESEARCH ORGANIZATIONS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 100. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CONTRACT RESEARCH ORGANIZATIONS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 101. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 102. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 103. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 104. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 105. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLOUD BASED, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 106. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLOUD BASED, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 107. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLOUD BASED, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 108. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HYBRID, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 109. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HYBRID, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 110. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HYBRID, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 111. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ON PREMISES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 112. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ON PREMISES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 113. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ON PREMISES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 114. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 115. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY SUBREGION, 2018-2032 (USD MILLION)
  • TABLE 116. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 117. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 118. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 119. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 120. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 121. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 122. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 123. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 124. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 125. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 126. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 127. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 128. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 129. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 130. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 131. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 132. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 133. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 134. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 135. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 136. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 137. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 138. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 139. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 140. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 141. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 142. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 143. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 144. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 145. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 146. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 147. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 148. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY SUBREGION, 2018-2032 (USD MILLION)
  • TABLE 149. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 150. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 151. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 152. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 153. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 154. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 155. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 156. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 157. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 158. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 159. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 160. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 161. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 162. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 163. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 164. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 165. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 166. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 167. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 168. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 169. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 170. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 171. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 172. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 173. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 174. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 175. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 176. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 177. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 178. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 179. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 180. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 181. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 182. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 183. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 184. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 185. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 186. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 187. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 188. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 189. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 190. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 191. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 192. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 193. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 194. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 195. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 196. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 197. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 198. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 199. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 200. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 201. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 202. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 203. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 204. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 205. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 206. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 207. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 208. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 209. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 210. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 211. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 212. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 213. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 214. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 215. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 216. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 217. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 218. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 219. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 220. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 221. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 222. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 223. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 224. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 225. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 226. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 227. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 228. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 229. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 230. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 231. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 232. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 233. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 234. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 235. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 236. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 237. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 238. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 239. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 240. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 241. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 242. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 243. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 244. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 245. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 246. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 247. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 248. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 249. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2032 (USD MILLION)
  • TABLE 250. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2032 (USD MILLION)
  • TABLE 251. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 252. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET & TOXICOLOGY PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 253. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 254. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2032 (USD MILLION)
  • TABLE 255. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2032 (USD MILLION)
  • TABLE 256. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2032 (USD MILLION)
  • TABLE 257. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 258. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 259. NATO ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 201