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市場調查報告書
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2017580

人工智慧在藥物研發領域的市場：按技術、治療領域、應用、最終用戶和部署方式分類——全球市場預測（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個工作天內

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2025 年，人工智慧 (AI) 藥物發現市場價值為 15.5 億美元，預計到 2026 年將成長至 18.1 億美元，複合年成長率為 17.90%，到 2032 年將達到 49.3 億美元。

主要市場統計數據
基準年 2025 15.5億美元
預計年份：2026年 18.1億美元
預測年份 2032 49.3億美元
複合年成長率 (%) 17.90%

為正在藥物研發流程和營運模式中應用人工智慧的組織定義一個策略轉折點。

人工智慧已從單純的研究興趣發展成為一項核心能力，它重塑了治療候選藥物的發現、最佳化和風險規避方式。本文將人工智慧的現狀置於演算法進步、生物數據擴展和計算化學突破的軌跡中，這些進展促成了生成模型、預測分析和結構模擬在工業工作流程中的實用化。製藥公司、生物技術Start-Ups、合約研究組織 (CRO) 和學術實驗室的相關人員正在將人工智慧整合到整個藥物發現價值鏈中，以縮短設計週期、提高轉化研究的準確性並指南策略性產品組合的選擇。

本書揭示了加速人工智慧主導藥物發現並改變研發決策框架的關鍵技術和組織變革。

藥物發現領域正經歷一系列相互關聯的變革，這些變革遠不止於演算法的改進。首先，蛋白質結構預測的突破性進展降低了標靶表徵的門檻，使研究團隊能夠模擬結合口袋和結構動力學。這使得先導化合物化合物的發現和最佳化速度達到了前所未有的水平。其次，生成式化學模型的成熟使得新型骨架的建構成為可能，這些骨架可以更快地合成和測試，從而將虛擬設計與實驗可行性研究結合。第三，整合基因體學、蛋白質體學、高內涵成像和真實世界臨床證據等多模態數據，能夠更詳細地展現疾病生物學特徵，進而提高ADMET和毒性預測的準確性。

本研究分析了美國在 2025 年徵收的關稅對人工智慧驅動的藥物發現流程和基礎設施決策的多層次運作和戰略影響。

2025年實施的關稅政策為整個生物製藥供應鏈以及支援人工智慧主導藥物研發的軟硬體體系帶來了擔憂和實際調整。對於依賴高效能GPU或實驗室設備等專用硬體且這些硬體通常從海外採購的機構而言，關稅增加了其籌資策略的複雜性，並迫使企業重新評估本地部署和雲端方案的總體擁有成本（TCO）。為此，許多團隊調整了其採用策略。一些團隊加快了雲端部署以規避進口瓶頸，而另一些團隊則投資於在地採購或簽訂長期供應商合約以確保關鍵設備的供應。

我們確定應用、技術、治療領域、最終用戶和部署模式之間的策略交集，以推動能夠帶來最大價值的成果。

全面觀點人工智慧應用有助於明確哪些領域的投資能帶來最直接的科學和營運回報。在ADMET和毒性預測領域，動態、藥物動力學和毒性預測技術的進步使研究團隊能夠更早篩選候選化合物，並降低後期試驗的脫落率。在臨床試驗最佳化方面，最佳化患者招募策略和試驗設計已被證明能夠有效提高試驗效率和代表性。先導化合物篩選流程利用高通量篩檢、In Silico標靶檢驗和虛擬篩檢來更快地識別有前景的化合物。先導化合物最佳化越來越依賴從頭藥物發現、定量構效關係（QSAR）建模和基於結構的藥物發現，這些方法共同迭代地提高分子效力和開發潛力。蛋白質結構預測，在基於第一原理計算、同源建模和分子動力學模擬的支持下，在目標檢驗和合理設計中繼續發揮至關重要的作用。

本文說明了美洲、歐洲、中東和非洲以及亞太地區的生態系統將如何影響技術採納速度、監管重點和合作網路。

每個地區的實際情況決定了人工智慧驅動的藥物發現的實施和規模化方式。在美洲，強大的創業投資系統和成熟的生物技術叢集支援演算法創新快速商業化。同時，接近性大型製藥企業的研發中心也促進了早期應用和產業合作。該地區與監管機構的對話日益側重於模型檢驗、透明度和證據標準，這些標準將計算預測與安全性和有效性評估聯繫起來。因此，研發項目往往優先考慮可重複性和審計追蹤，以滿足嚴格的合規要求。

分析競爭對手和合作模式，以推動成熟製藥公司、人工智慧優先型Start-Ups和服務供應商。

競爭格局的特點是角色互補而非純粹的零和博弈。成熟的製藥公司正利用其深厚的專業知識、廣泛的臨床研發管線和豐富的監管經驗，將人工智慧主導的工作流程擴展到後期研發階段。他們通常優先考慮將人工智慧的輸出結果整合到現有的決策管治中，同時保持嚴格的檢驗標準。同時，人工智慧原生Start-Ups正憑藉其專業的建模技術、敏捷的工程方法以及對新資料來源的探索精神，為快速迭代和利基創新創造機會。受託研究機構（CRO）和服務供應商正在將人工智慧融入其服務產品中，以縮短週期，並為尋求外包藥物研發能力的客戶提供差異化的價值提案。

領導者在負責任地擴大人工智慧在藥物發現領域的應用，同時保持科學誠信和應對力。

領導者應先將人工智慧舉措與明確的科學和業務目標結合，而不是將工具部署本身作為最終目標。這首先要選擇資料品質充足且結果可衡量的應用場景，例如迭代式先導化合物最佳化或靶向毒性分級，並建立結合預測性能和營運影響的成功指標。接下來，要投資數據基礎建設。開發高品質的內部資料集，並輔以管理良好的外部資料來源，同時實施元資料標準，以提高模型的可解釋性和可複現性。同時，投資於針對生命科學工作流程客製化的機器學習運作（MLOps）可以縮短部署時間，並創建監管機構和安全團隊所需的審計追蹤。

概述了一種嚴謹的混合方法研究途徑，該方法結合了專家訪談、文獻整合、可重複性檢查和透明的局限性聲明。

本研究整合了多方面的證據，旨在對人工智慧在藥物研發中的應用呈現平衡的觀點。主要資料來源包括對製藥公司研發部​​門、生物技術公司、受託研究機構和學術研究中心的專家進行的結構化訪談，以及對記錄調查方法調查方法進展的同行評審文獻和預印本的技術審查。次要資料來源包括公開的監管指南、企業關於平台採用情況的資訊披露，以及對成功將人工智慧與實驗室流程相結合的案例研究的分析。方法論主張的定量檢驗是透過技術文獻中報告的可重複性評估以及在有獨立基準資料集的情況下進行的比較評估來實現的。

本報告總結了在不斷變化的政策和營運限制下，將人工智慧主導的能力轉化為永續藥物發現優勢的關鍵戰略意義。

對於那些致力於提升藥物研發速度和轉換準確性的機構而言，人工智慧在藥物研發領域已不再是可選項。然而，這項技術的影響並非一成不變。為了獲得可重複的結果，它需要與實驗設計進行精心整合，並輔以穩健的資料管治和循序漸進的檢驗策略。那些專注於高價值應用案例、重視資料管理並組建跨職能團隊的領導者，將比那些只進行孤立的先導計畫而缺乏端到端整合的領導者獲得更大的收益。

目錄

第1章 引言

第2章 分析方法

  • 分析設計
  • 分析框架
  • 市場規模預測
  • 數據三角測量
  • 分析結果
  • 分析的前提條件
  • 分析的局限性

第3章執行摘要

  • 首席體驗長的觀點
  • 市場規模和成長趨勢
  • 市佔率分析（2025 年）
  • FPNV定位矩陣（2025）
  • 新的商機
  • 下一代經營模式
  • 產業藍圖

第4章 市場概覽

  • 產業生態系與價值鏈分析
  • 波特五力分析
  • PESTEL 分析
  • 市場展望
  • 市場進入（GTM）策略

第5章 市場洞察

  • 消費者分析與最終用戶觀點
  • 消費者體驗基準測試
  • 機會映射
  • 分銷通路分析
  • 價格趨勢分析
  • 監理合規和標準框架
  • ESG與永續性分析
  • 中斷和風險情景
  • 成本效益分析（ROI）和成本效益分析（CBA）

第6章：美國關稅的累積影響（2025 年）

第7章：人工智慧（AI）的累積影響（2025 年）

第8章：人工智慧在藥物發現領域的市場：依技術分類

  • 電腦視覺
  • 深度學習
  • 機器學習
  • 自然語言處理

第9章：人工智慧在藥物研發領域的市場：依治療領域分類

  • 心血管疾病
  • 中樞神經系統
  • 感染疾病
  • 瘤

第10章：人工智慧在藥物發現領域的市場：按應用分類

  • ADMET毒性預測
    • 動態預測
    • 藥物動力學預測
    • 毒性預測
  • 臨床試驗的最佳化
    • 招募受試者
    • 測試設計最佳化
  • 命中識別
    • 高通量篩檢
    • 電腦模擬標靶檢驗
    • 虛擬篩檢
  • 先導藥物最適化
    • 新藥設計
    • QSAP（定量結構-物理相關性）
    • SBDD（基於結構的藥物發現）
  • 蛋白質結構預測
    • 第一原理計算
    • 同源建模
    • 分子動力學模擬

第11章：人工智慧在藥物發現領域的市場：按最終用戶分類

  • 學術和研究機構
  • 生技公司
  • 合約研究機構
  • 製藥公司

第12章：人工智慧在藥物研發領域的市場：以部署方式分類

  • 雲
  • 混合
  • 現場

第13章：人工智慧在藥物研發領域的市場：按地區分類

  • 北美洲和南美洲
    • 北美洲
    • 拉丁美洲
  • 歐洲、中東和非洲
    • 歐洲
    • 中東
    • 非洲
  • 亞太地區

第14章：人工智慧在藥物研發領域的市場：以群體分類

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

第15章：人工智慧在藥物研發領域的市場：依國家分類

  • 美國
  • 加拿大
  • 墨西哥
  • 巴西
  • 英國
  • 德國
  • 法國
  • 俄羅斯
  • 義大利
  • 西班牙
  • 中國
  • 印度
  • 日本
  • 澳洲
  • 韓國

第16章：美國藥物研發領域的人工智慧市場

第17章：中國藥物研發領域的人工智慧市場

第18章 競爭格局

  • 市場集中度分析（2025 年）
    • 濃度比（CR）
    • 赫芬達爾-赫希曼指數 (HHI)
  • 近期趨勢及影響分析（2025 年）
  • 產品系列分析（2025 年）
  • 基準分析（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