人工智慧在药物研发市场的应用、技术、治疗领域、最终用户和部署模式—2025-2032年全球预测

预计到 2032 年，人工智慧在药物发现领域的市场规模将达到 99 亿美元，复合年增长率为 28.19%。

关键市场统计数据
基准年2024年	13.5亿美元
预计年份：2025年	17.4亿美元
预测年份 2032	99亿美元
复合年增长率 (%)	28.19%

人工智慧在药物研发领域具有变革性潜力，以及为什么领导者现在必须重新思考他们的策略

人工智慧已从最初的研究兴趣发展成为一项核心能力，它正在重塑治疗候选药物的发现、优化和风险评估方式。本文将介绍当前时代，演算法进步、不断扩展的生物数据以及计算化学的突破正在融合，将生成模型、预测分析和结构模拟引入实用化的工业工作流程中。製药公司、生物技术新兴企业、受託研究机构和学术实验室等各方相关人员正在将人工智慧整合到药物发现价值链的各个环节，以缩短设计週期、提高转化准确性并为策略性药物组合选择提供资讯。

随着各组织机构的调整，核心问题正从人工智慧能否创造价值转变为如何管理、检验和扩展人工智慧。如今的关键考虑包括：将人工智慧倡议与实验吞吐量相匹配，为In Silico预测设定切合实际的基准，以及将人工智慧输出与湿实验室流程整合，以确保人类专业知识和计算模型相互补充。此外，领导层还必须权衡云端原生平台（支援快速迭代）和本地配置（满足严格的资料管治要求）之间的营运利弊。简而言之，人工智慧在药物研发领域的下一阶段将强调严谨的整合、可重复的检验以及对能够产生可衡量价值的候选药物进行策略性优先排序。

根本性的技术变革和跨产业动态如何重塑药物发现生态系统并加速新能力的产生

药物发现格局正经历多重相互交织的变革，而这些变革远不止于演算法的改进所能涵盖。首先，蛋白质结构预测的突破降低了标靶表征的门槛，使得结合口袋和构象动力学的建模速度空前提升，从而助力先导化合物。其次，生成式化学模型的成熟使得新型骨架的构思成为可能，这些骨架可以更快地合成和测试，并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，能够更全面地展现疾病生物学特征，进而提升ADMET和毒性预测的表现。

同时，与科学工作流程相契合的MLOps实践透过强调可重复性和管线管治，提高了企业的准备度。对可解释人工智慧和可解读方法的投资，有助于监管机构和安全团队更有信心地解读模型输出。此外，学术团体、生物技术创新者和平台提供者之间日益壮大的伙伴关係生态系统，正在加速知识传播，并开闢新的商业化路径。这些转变不仅提升了个人能力，也改变了药物研发过程中团队的组织方式、实验的整体、风险管理方式。

评估2025年美国关税环境将如何影响全球供应链、创新伙伴关係和运算平台部署选择。

2025年关税政策为生物製药供应链以及支援人工智慧主导研发的软硬体体系带来了不确定性和实际调整。对于依赖高效能GPU或国际采购实验室设备等专用硬体的机构而言，关税政策增加了筹资策略的复杂性，迫使企业重新评估本地部署和云端方案的整体拥有成本。一些团队加快了云端部署的步伐以避免进口瓶颈，而另一些团队则投资于在地采购和长期供应商协议，以确保关键设备的供应。

除了硬体之外，关税也影响了国际研究合作的结构。为了确保应对不断变化的贸易壁垒，许可谈判和跨境资料传输协议都进行了重新审视。因此，合成化合物的海外生产合作变得更加谨慎，并强调采用分散式研发模式，将关键能力在地化。同时，监管协调和跨司法管辖区的检验工作成为优先事项，以确保多中心临床计画和临床前工作流程的连续性。虽然关税造成了短期中断，但也凸显了灵活的基础设施、多元化的供应商网络以及能够吸收政策波动而不阻碍研发势头的管治框架的战略价值。

揭示应用、技术、治疗领域、最终用户和部署模式细分之间的策略交集，以实现最有价值的成果。

解读细分架构可以揭示人工智慧将在哪些应用、技术、治疗领域、最终用户和部署模式下产生最大影响

全面了解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的进步使研究团队能够更早筛选候选化合物，并减少后期研发的失败率。优化临床试验受益于优化的患者招募策略和研究设计，从而提高试验效率和代表性。高通量筛检、电脑模拟标靶验证和虚拟筛检能够更快发现潜在的化合物，从而提升先导化合物的发现流程的价值。先导化合物结构预测，在基于第一原理建模、同源建模和分子动力学模拟的支持下，仍然是目标检验和合理设计的基础。

深度学习和机器学习技术驱动特征提取和预测建模，而电脑视觉则解读高内涵成像和表型分析，将分子扰动与细胞反应联繫起来。自然语言处理技术组织并挖掘海量的生物医学文献、专利和临床记录，以揭示领先技术和机制假设。在治疗方面，人工智慧的应用已在肿瘤学和感染疾病，分子标靶和高通量检测加速了学习週期。心血管和中枢神经系统计画也在利用预测模型，但面临与生理和临床终点相关的独特转化挑战。最终用户包括：致力于拓展方法论前沿的学术和研究机构、将人工智慧与敏捷实验平台结合的生物技术公司、采用预测工具缩短研发週期的委外研发机构，以及将人工智慧融入其研发工作的製药公司。部署选项——云端基础、混合和本地部署——反映了速度、成本、资料管治和监管问题之间的权衡，要求组织根据其资料敏感度和协作模式来调整其基础设施策略。

这种细分结构强调，价值在于特定领域模型、高品质资料和协调一致的业务流程的交会点。透过策略性地明确哪些应用、技术和治疗终端使用者组合需要优先考虑，组织可以合理安排试点计画并建立可重复使用的能力，而不是将资源分散到互不相干的实验中。

决定美洲、欧洲、中东和非洲以及亚太地区招募模式、人才流动、监管参与和伙伴关係模式的区域动态

区域实际情况正在影响人工智慧驱动的药物发现技术的应用和规模化。在美洲，强大的创业投资生态系统和成熟的生物技术丛集为演算法创新技术的快速商业化提供了支持。该地区与监管机构的对话日益聚焦于模型检验、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，研发项目越来越重视可重复性和审核追踪，以满足严格的合规要求。

欧洲、中东和非洲汇聚了许多杰出的学术机构和公私合作组织，共同推动基础研究和转化研究。欧洲各地的法律规范正在不断改进，以应对人工智慧领域的特定问题，跨境合作也十分普遍，各方都在利用各自在特定治疗领域的优势开展合作。在中东和非洲，数据可用性和标准化临床数据集方面仍面临挑战，但能力建设倡议和对当地基础设施的投资正逐步推动其参与全球药物研发网路。

亚太地区在药物研发领域正迅速应用人工智慧技术，这得益于庞大的患者群体、生命科学领域大量的公共和私人投资以及强大的生产能力。东亚、南亚和大洋洲等中心城市之间的人才流动，支撑着一个充满活力的生态系统，在这个生态系统中，新兴企业和成熟公司都在尝试云端原生和混合部署架构。儘管跨国伙伴关係持续推动整个地区的创新，但每个地区的监管差异、人才储备和基础设施限制，都会影响药物研发成果进入临床开发和商业化计画的速度。

现有製药公司、人工智慧新兴企业和服务供应商之间的竞争与合作将决定能力的扩散和伙伴关係关係的架构。

竞争格局的特点是角色互补而非纯粹的零和博弈。现有製药公司利用其深厚的领域知识、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，人工智慧新兴企业带来了专业的建模技术、敏捷的工程实践以及探索新型资料来源的意愿，从而为快速迭代和利基创新创造了机会。合约研究组织和服务供应商正在将人工智慧融入其服务产品中，以缩短週期时间，并为寻求外部研发能力的客户提供差异化的价值提案。

合作开发模式涵盖策略联盟、共同开发计划、技术授权和资料共用联盟等多种形式。此类合作通常涉及学术团体贡献基础科学成果和客製化演算法。云端和基础设施供应商则扮演着推动者的角色，提供可扩展的运算资源和平台，用于託管协作工作空间、模型註册和可重现的流程。在这些互动中，成功的公司凭藉透明的检验、清晰的智慧财产权框架以及将计算假设转化为实验结果的实际能力脱颖而出。买家和合作伙伴在评估供应商时，不仅关注演算法的复杂程度，还关注其整合成熟度、资料管理实务以及实际应用效果。

为了在管理风险和合规性的同时获得人工智慧主导的优势，领导者现在应该采取哪些切实可行的策略倡议和营运干预措施？

与其为了采用工具而采用工具，领导者首先应该将人工智慧倡议与明确的科学和业务目标结合。这首先要选择数据品质充足且结果可衡量的应用场景，例如迭代式先导药物最适化或靶点毒性分级，并建立结合预测性能和营运影响的成功指标。接下来，要投资于资料基础建置：精心维护高品质的内部资料集，利用管理良好的外部资料来源进行扩充，并实施能够提高模型可解释性和可重现性的元资料标准。同时，对与生命科学工作流程相契合的机器学习运维（MLOps）进行投资，将加快部署速度，并创建监管机构和安全团队所需的审核追踪。

在营运层面，应组成跨学科团队，将计算科学家与药物化学家、毒理学家和临床科学家结合，以确保模型输出具有实际应用价值。采用分阶段验证方法，即在将模型整合到更广泛的决策框架之前，先利用有限的试点实验来验证其有效性。在采购和基础设施方面，应权衡云端部署、混合部署和本地部署与资料保密性、迭代速度和整体拥有成本之间的检验。最后，应制定管治，以解决智慧财产权、资料隐私和伦理使用问题，并建立持续学习流程，将实验中获得的洞见回馈到模型改进中。透过按顺序采取这些措施，组织可以在保持科学严谨性的同时，负责任地扩展其人工智慧能力。

对用于产生研究结果的调查方法进行透明的描述，包括资料来源、检验技术、专家参与和限制。

本研究整合了多方面的证据，以全面了解人工智慧在药物研发中的应用。主要研究包括对製药和生物技术公司、合约研究机构和学术研究中心的专家进行结构化访谈，以及对记录方法学进展的同行评审文献和预印本进行技术审查。次要研究包括分析已发布的监管指南、公司关于平台采用情况的披露信息，以及展示人工智慧与湿实验室流程成功整合的案例研究。方法论论点的定量检验依赖于技术资讯来源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。

我们的分析方法强调三角验证。我们结合专家观点、文献证据和已记录的案例，以识别稳健的模式，而非依赖单一研究。在条件允许的情况下，我们将资料中引用的专有资料集或供应商声明与独立的技术评估和復现结果进行交叉核对。我们承认研究有其局限性，包括难以从私人公司获取详细的绩效指标、不同机构间资料集标准的差异，以及方法论的快速变化超过了静态报告的局限性。为了弥补这些局限性，我们的分析重点突出了由多个资讯来源支持的反覆出现的主题，并明确指出了需要进一步开展一手研究和技术基准测试的领域。

提炼出对决策者在人工智慧、生命科学和不断变化的全球动态融合过程中具有核心意义的启示

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。要获得可重复的结果，需要将人工智慧与实验设计、强大的资料管治以及分阶段的检验策略进行有意识的整合。专注于高价值应用案例、重视资料管理并组成跨职能团队的领导者，相比那些只进行孤立的试点研究而缺乏端到端整合的机构，将获得更大的收益。

此外，地缘政治和政策因素，例如关税驱动的供应链调整和区域监管差异，凸显了灵活的基础设施和多元化伙伴关係关係的重要性。成功取决于将卓越的技术与营运规范相结合，例如清晰的指标、透明的检验以及涵盖智慧财产权、伦理和监管要求的治理框架。透过优先考虑这些要素，企业可以将管治的潜力转化为永续的能力，从而加速治疗方法的研发并改善患者的治疗效果。

目录

第一章：序言

第二章调查方法

第三章执行摘要

第四章 市场概览

第五章 市场洞察

• 先进的生成式人工智慧模型加速了新型小分子的设计与合成。
• 将多组体学资料集与深度学习结合，用于肿瘤药物研发中的精准标靶识别
• 实施人工智慧驱动的预测性 ADMET 模型以减少后期临床试验失败
• 引入强化学习演算法以优化抗体设计和治疗效果。
• 采用云端原生人工智慧平台实现可扩展的虚拟筛检和协作调查工作流程
• 利用真实世界证据和人工智慧分析快速进行药物再利用，以应对新出现的健康危机
• 生物製药公司与科技巨头达成策略合作，共同开发人工智慧驱动的药物发现流程。
• 应用联邦学习框架，在分散式专有资料集上安全地训练人工智慧模型
• 管理方案和指南正在塑造人工智慧在药物发现过程中的检验和透明度
• 引入可预测的人工智慧技术，以提高预测结果的可解释性和监管机构的核准。

第六章：美国关税的累积影响，2025年

第七章：人工智慧的累积影响，2025年

第八章 按应用程式分類的人工智慧在药物发现领域的市场

• ADMET和毒性预测
  • 动态预测
  • 药物动力学预测
  • 毒性预测
• 临床试验优化
  • 病患招募
  • 测试设计优化
• 命中识别
  • 高通量筛检
  • In Silico靶检验
  • 虚拟筛选
• 先导药物最适化
  • 从头开始的药物设计
  • 定量构效关係
  • 基于结构的药物设计
• 蛋白质结构预测
  • 第一原理建模
  • 同源性建模
  • 分子动力学模拟

9. 人工智慧在药物发现市场的应用（依技术划分）

• 电脑视觉
• 深度学习
• 机器学习
• 自然语言处理

第十章 人工智慧在药物发现市场的应用（依治疗领域划分）

• 心血管疾病
• 中枢神经系统
• 感染疾病
• 肿瘤学

第十一章 人工智慧在药物发现市场的应用（依最终用户划分）

• 学术研究机构
• 生技公司
• 合约研究组织
• 製药公司

第十二章 人工智慧在药物发现市场的部署模式

• 云端基础的
• 杂交种
• 本地部署

第十三章 各地区药物研发市场中的人工智慧

• 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 药物发现领域人工智慧市场（按集团划分）

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

第十五章 各国药物研发市场中的人工智慧

• 美国
• 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 竞争格局

• 2024年市占率分析
• FPNV定位矩阵，2024
• 竞争分析
  • Schrodinger, Inc.
  • Recursion Pharmaceuticals, Inc.
  • Exscientia plc
  • Valo Health, Inc.
  • Atomwise, Inc.
  • Insilico Medicine, Inc.
  • BenevolentAI Limited
  • Cloud Pharmaceuticals, Inc.
  • Healx Limited
  • Microsoft Corporation

The Artificial Intelligence in Drug Discovery Market is projected to grow by USD 9.90 billion at a CAGR of 28.19% by 2032.

KEY MARKET STATISTICS
Base Year [2024]	USD 1.35 billion
Estimated Year [2025]	USD 1.74 billion
Forecast Year [2032]	USD 9.90 billion
CAGR (%)	28.19%

Framing the transformative promise of artificial intelligence in drug discovery and why leaders must reassess strategy now

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.

How foundational technological shifts and cross-industry dynamics are reshaping the drug discovery ecosystem and accelerating new capabilities

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.

Assessing how the 2025 tariff environment in the United States interacts with global supply chains, innovation partnerships, and computational platform deployment choices

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

Interpreting the segmentation architecture to reveal where AI is most impactful across applications, technologies, therapeutic areas, end users, and deployment modes

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.

Regional dynamics that determine adoption patterns, talent flows, regulatory engagement, and partnership models across the Americas, EMEA, and Asia-Pacific

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.

Competitive and collaborative behaviors among incumbent pharma, AI-native startups, and service providers that determine capability diffusion and partnership architectures

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.

Practical strategic moves and operational interventions leaders should pursue now to capture AI-driven advantages while managing risk and compliance

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.

Transparent description of the research approach used to produce insights, including data sourcing, validation techniques, expert engagement, and limitations

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.

Distilling the core implications for decision-makers navigating the convergence of AI, life sciences, and evolving global dynamics

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 Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Advanced generative AI models accelerating de novo small molecule design and synthesis planning
• 5.2. Integration of multi-omics datasets with deep learning for precision target identification in oncology drug discovery
• 5.3. Implementation of AI-driven predictive ADMET modeling to reduce late-stage clinical trial failures
• 5.4. Deployment of reinforcement learning algorithms to optimize antibody design and therapeutic efficacy profiles
• 5.5. Adoption of cloud-native AI platforms for scalable virtual screening and collaborative research workflows
• 5.6. Use of real-world evidence and AI analytics for rapid drug repurposing in response to emerging health crises
• 5.7. Strategic partnerships between biopharma and tech giants to co-develop AI-powered drug discovery pipelines
• 5.8. Application of federated learning frameworks to train AI models on distributed proprietary datasets securely
• 5.9. Regulatory initiatives and guidelines shaping AI validation and transparency in drug discovery processes
• 5.10. Incorporation of explainable AI techniques to enhance interpretability and regulatory acceptance of predictions

6. Cumulative Impact of United States Tariffs 2025

7. Cumulative Impact of Artificial Intelligence 2025

8. Artificial Intelligence in Drug Discovery Market, by Application

• 8.1. ADMET And Toxicology Prediction
  • 8.1.1. Pharmacodynamics Prediction
  • 8.1.2. Pharmacokinetics Prediction
  • 8.1.3. Toxicity Prediction
• 8.2. Clinical Trial Optimization
  • 8.2.1. Patient Recruitment
  • 8.2.2. Trial Design Optimization
• 8.3. Hit Identification
  • 8.3.1. High Throughput Screening
  • 8.3.2. In Silico Target Validation
  • 8.3.3. Virtual Screening
• 8.4. Lead Optimization
  • 8.4.1. De Novo Drug Design
  • 8.4.2. Quantitative Structure Activity Relationship
  • 8.4.3. Structure Based Drug Design
• 8.5. Protein Structure Prediction
  • 8.5.1. Ab Initio Modeling
  • 8.5.2. Homology Modeling
  • 8.5.3. Molecular Dynamics Simulation

9. Artificial Intelligence in Drug Discovery Market, by Technology

• 9.1. Computer Vision
• 9.2. Deep Learning
• 9.3. Machine Learning
• 9.4. Natural Language Processing

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

• 10.1. Cardiovascular Diseases
• 10.2. Central Nervous System
• 10.3. Infectious Diseases
• 10.4. Oncology

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

• 11.1. Academic And 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. Competitive Landscape

• 16.1. Market Share Analysis, 2024
• 16.2. FPNV Positioning Matrix, 2024
• 16.3. Competitive Analysis
  • 16.3.1. Schrodinger, Inc.
  • 16.3.2. Recursion Pharmaceuticals, Inc.
  • 16.3.3. Exscientia plc
  • 16.3.4. Valo Health, Inc.
  • 16.3.5. Atomwise, Inc.
  • 16.3.6. Insilico Medicine, Inc.
  • 16.3.7. BenevolentAI Limited
  • 16.3.8. Cloud Pharmaceuticals, Inc.
  • 16.3.9. Healx Limited
  • 16.3.10. Microsoft Corporation

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 SIZE, BY APPLICATION, 2024 VS 2032 (%)
• FIGURE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2024 VS 2032 (%)
• FIGURE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2024 VS 2032 (%)
• FIGURE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2024 VS 2032 (%)
• FIGURE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2024 VS 2032 (%)
• FIGURE 11. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 12. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY REGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 13. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY SUBREGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 14. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 15. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 16. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY SUBREGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 17. EUROPE ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 18. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 19. AFRICA ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 20. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 21. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY GROUP, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 22. ASEAN ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 23. GCC ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 24. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 25. BRICS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 26. G7 ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 27. NATO ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 28. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 29. ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SHARE, BY KEY PLAYER, 2024
• FIGURE 30. ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET, FPNV POSITIONING MATRIX, 2024

LIST OF TABLES

• TABLE 1. ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SEGMENTATION & COVERAGE
• TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2024
• TABLE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, 2018-2024 (USD MILLION)
• TABLE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, 2025-2032 (USD MILLION)
• TABLE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2024 (USD MILLION)
• TABLE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2025-2032 (USD MILLION)
• TABLE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET AND TOXICOLOGY PREDICTION, 2018-2024 (USD MILLION)
• TABLE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET AND TOXICOLOGY PREDICTION, 2025-2032 (USD MILLION)
• TABLE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET AND TOXICOLOGY PREDICTION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET AND TOXICOLOGY PREDICTION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 11. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET AND TOXICOLOGY PREDICTION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 12. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET AND TOXICOLOGY PREDICTION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 13. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET AND TOXICOLOGY PREDICTION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 14. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET AND TOXICOLOGY PREDICTION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 15. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACODYNAMICS PREDICTION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 16. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACODYNAMICS PREDICTION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 17. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACODYNAMICS PREDICTION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 18. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACODYNAMICS PREDICTION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 19. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACODYNAMICS PREDICTION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 20. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACODYNAMICS PREDICTION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 21. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACOKINETICS PREDICTION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 22. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACOKINETICS PREDICTION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 23. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACOKINETICS PREDICTION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 24. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACOKINETICS PREDICTION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 25. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACOKINETICS PREDICTION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 26. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACOKINETICS PREDICTION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 27. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TOXICITY PREDICTION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 28. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TOXICITY PREDICTION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 29. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TOXICITY PREDICTION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 30. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TOXICITY PREDICTION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 31. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TOXICITY PREDICTION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 32. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TOXICITY PREDICTION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 33. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2024 (USD MILLION)
• TABLE 34. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2025-2032 (USD MILLION)
• TABLE 35. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 36. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 37. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 38. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 39. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 40. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 41. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PATIENT RECRUITMENT, BY REGION, 2018-2024 (USD MILLION)
• TABLE 42. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PATIENT RECRUITMENT, BY REGION, 2025-2032 (USD MILLION)
• TABLE 43. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PATIENT RECRUITMENT, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 44. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PATIENT RECRUITMENT, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 45. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PATIENT RECRUITMENT, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 46. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PATIENT RECRUITMENT, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 47. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TRIAL DESIGN OPTIMIZATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 48. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TRIAL DESIGN OPTIMIZATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 49. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TRIAL DESIGN OPTIMIZATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 50. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TRIAL DESIGN OPTIMIZATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 51. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TRIAL DESIGN OPTIMIZATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 52. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TRIAL DESIGN OPTIMIZATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 53. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2024 (USD MILLION)
• TABLE 54. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2025-2032 (USD MILLION)
• TABLE 55. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 56. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 57. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 58. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 59. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 60. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 61. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIGH THROUGHPUT SCREENING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 62. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIGH THROUGHPUT SCREENING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 63. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIGH THROUGHPUT SCREENING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 64. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIGH THROUGHPUT SCREENING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 65. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIGH THROUGHPUT SCREENING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 66. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIGH THROUGHPUT SCREENING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 67. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY IN SILICO TARGET VALIDATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 68. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY IN SILICO TARGET VALIDATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 69. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY IN SILICO TARGET VALIDATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 70. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY IN SILICO TARGET VALIDATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 71. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY IN SILICO TARGET VALIDATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 72. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY IN SILICO TARGET VALIDATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 73. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY VIRTUAL SCREENING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 74. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY VIRTUAL SCREENING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 75. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY VIRTUAL SCREENING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 76. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY VIRTUAL SCREENING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 77. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY VIRTUAL SCREENING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 78. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY VIRTUAL SCREENING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 79. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2024 (USD MILLION)
• TABLE 80. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2025-2032 (USD MILLION)
• TABLE 81. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 82. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 83. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 84. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 85. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 86. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 87. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DE NOVO DRUG DESIGN, BY REGION, 2018-2024 (USD MILLION)
• TABLE 88. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DE NOVO DRUG DESIGN, BY REGION, 2025-2032 (USD MILLION)
• TABLE 89. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DE NOVO DRUG DESIGN, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 90. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DE NOVO DRUG DESIGN, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 91. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DE NOVO DRUG DESIGN, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 92. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DE NOVO DRUG DESIGN, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 93. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP, BY REGION, 2018-2024 (USD MILLION)
• TABLE 94. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP, BY REGION, 2025-2032 (USD MILLION)
• TABLE 95. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 96. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 97. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 98. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 99. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY STRUCTURE BASED DRUG DESIGN, BY REGION, 2018-2024 (USD MILLION)
• TABLE 100. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY STRUCTURE BASED DRUG DESIGN, BY REGION, 2025-2032 (USD MILLION)
• TABLE 101. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY STRUCTURE BASED DRUG DESIGN, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 102. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY STRUCTURE BASED DRUG DESIGN, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 103. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY STRUCTURE BASED DRUG DESIGN, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 104. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY STRUCTURE BASED DRUG DESIGN, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 105. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2024 (USD MILLION)
• TABLE 106. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2025-2032 (USD MILLION)
• TABLE 107. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 108. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 109. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 110. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 111. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 112. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 113. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY AB INITIO MODELING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 114. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY AB INITIO MODELING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 115. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY AB INITIO MODELING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 116. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY AB INITIO MODELING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 117. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY AB INITIO MODELING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 118. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY AB INITIO MODELING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 119. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HOMOLOGY MODELING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 120. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HOMOLOGY MODELING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 121. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HOMOLOGY MODELING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 122. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HOMOLOGY MODELING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 123. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HOMOLOGY MODELING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 124. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HOMOLOGY MODELING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 125. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MOLECULAR DYNAMICS SIMULATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 126. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MOLECULAR DYNAMICS SIMULATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 127. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MOLECULAR DYNAMICS SIMULATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 128. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MOLECULAR DYNAMICS SIMULATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 129. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MOLECULAR DYNAMICS SIMULATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 130. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MOLECULAR DYNAMICS SIMULATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 131. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2024 (USD MILLION)
• TABLE 132. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2025-2032 (USD MILLION)
• TABLE 133. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COMPUTER VISION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 134. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COMPUTER VISION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 135. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COMPUTER VISION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 136. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COMPUTER VISION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 137. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COMPUTER VISION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 138. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY COMPUTER VISION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 139. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEEP LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 140. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEEP LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 141. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEEP LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 142. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEEP LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 143. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEEP LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 144. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEEP LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 145. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MACHINE LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 146. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MACHINE LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 147. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MACHINE LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 148. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MACHINE LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 149. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MACHINE LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 150. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY MACHINE LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 151. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 152. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 153. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 154. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 155. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 156. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 157. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2024 (USD MILLION)
• TABLE 158. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2025-2032 (USD MILLION)
• TABLE 159. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 160. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 161. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 162. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 163. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 164. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 165. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CENTRAL NERVOUS SYSTEM, BY REGION, 2018-2024 (USD MILLION)
• TABLE 166. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CENTRAL NERVOUS SYSTEM, BY REGION, 2025-2032 (USD MILLION)
• TABLE 167. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CENTRAL NERVOUS SYSTEM, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 168. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CENTRAL NERVOUS SYSTEM, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 169. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CENTRAL NERVOUS SYSTEM, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 170. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CENTRAL NERVOUS SYSTEM, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 171. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY INFECTIOUS DISEASES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 172. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY INFECTIOUS DISEASES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 173. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY INFECTIOUS DISEASES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 174. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY INFECTIOUS DISEASES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 175. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY INFECTIOUS DISEASES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 176. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY INFECTIOUS DISEASES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 177. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ONCOLOGY, BY REGION, 2018-2024 (USD MILLION)
• TABLE 178. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ONCOLOGY, BY REGION, 2025-2032 (USD MILLION)
• TABLE 179. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ONCOLOGY, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 180. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ONCOLOGY, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 181. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ONCOLOGY, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 182. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ONCOLOGY, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 183. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2024 (USD MILLION)
• TABLE 184. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2025-2032 (USD MILLION)
• TABLE 185. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ACADEMIC AND RESEARCH INSTITUTES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 186. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ACADEMIC AND RESEARCH INSTITUTES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 187. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ACADEMIC AND RESEARCH INSTITUTES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 188. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ACADEMIC AND RESEARCH INSTITUTES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 189. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ACADEMIC AND RESEARCH INSTITUTES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 190. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ACADEMIC AND RESEARCH INSTITUTES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 191. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY BIOTECHNOLOGY COMPANIES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 192. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY BIOTECHNOLOGY COMPANIES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 193. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY BIOTECHNOLOGY COMPANIES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 194. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY BIOTECHNOLOGY COMPANIES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 195. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY BIOTECHNOLOGY COMPANIES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 196. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY BIOTECHNOLOGY COMPANIES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 197. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CONTRACT RESEARCH ORGANIZATIONS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 198. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CONTRACT RESEARCH ORGANIZATIONS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 199. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CONTRACT RESEARCH ORGANIZATIONS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 200. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CONTRACT RESEARCH ORGANIZATIONS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 201. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CONTRACT RESEARCH ORGANIZATIONS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 202. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CONTRACT RESEARCH ORGANIZATIONS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 203. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 204. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 205. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 206. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 207. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 208. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 209. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2018-2024 (USD MILLION)
• TABLE 210. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY DEPLOYMENT MODE, 2025-2032 (USD MILLION)
• TABLE 211. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLOUD BASED, BY REGION, 2018-2024 (USD MILLION)
• TABLE 212. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLOUD BASED, BY REGION, 2025-2032 (USD MILLION)
• TABLE 213. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLOUD BASED, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 214. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLOUD BASED, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 215. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLOUD BASED, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 216. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLOUD BASED, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 217. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HYBRID, BY REGION, 2018-2024 (USD MILLION)
• TABLE 218. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HYBRID, BY REGION, 2025-2032 (USD MILLION)
• TABLE 219. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HYBRID, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 220. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HYBRID, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 221. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HYBRID, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 222. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HYBRID, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 223. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ON PREMISES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 224. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ON PREMISES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 225. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ON PREMISES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 226. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ON PREMISES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 227. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ON PREMISES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 228. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ON PREMISES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 229. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY REGION, 2018-2024 (USD MILLION)
• TABLE 230. GLOBAL ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY REGION, 2025-2032 (USD MILLION)
• TABLE 231. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY SUBREGION, 2018-2024 (USD MILLION)
• TABLE 232. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY SUBREGION, 2025-2032 (USD MILLION)
• TABLE 233. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2018-2024 (USD MILLION)
• TABLE 234. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY APPLICATION, 2025-2032 (USD MILLION)
• TABLE 235. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET AND TOXICOLOGY PREDICTION, 2018-2024 (USD MILLION)
• TABLE 236. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY ADMET AND TOXICOLOGY PREDICTION, 2025-2032 (USD MILLION)
• TABLE 237. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2018-2024 (USD MILLION)
• TABLE 238. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY CLINICAL TRIAL OPTIMIZATION, 2025-2032 (USD MILLION)
• TABLE 239. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2018-2024 (USD MILLION)
• TABLE 240. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY HIT IDENTIFICATION, 2025-2032 (USD MILLION)
• TABLE 241. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2018-2024 (USD MILLION)
• TABLE 242. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY LEAD OPTIMIZATION, 2025-2032 (USD MILLION)
• TABLE 243. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2018-2024 (USD MILLION)
• TABLE 244. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY PROTEIN STRUCTURE PREDICTION, 2025-2032 (USD MILLION)
• TABLE 245. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2018-2024 (USD MILLION)
• TABLE 246. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY TECHNOLOGY, 2025-2032 (USD MILLION)
• TABLE 247. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2018-2024 (USD MILLION)
• TABLE 248. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY THERAPEUTIC AREA, 2025-2032 (USD MILLION)
• TABLE 249. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2018-2024 (USD MILLION)
• TABLE 250. AMERICAS ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET SIZE, BY END USER, 2025-2032