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人工智慧在藥物研發市場的應用、技術、治療領域、最終用戶和部署模式—2025-2032年全球預測

Artificial Intelligence in Drug Discovery Market by Application, Technology, Therapeutic Area, End User, Deployment Mode - Global Forecast 2025-2032
出版日期: | 出版商: 360iResearch | 英文 186 Pages | 商品交期: 最快1-2個工作天內

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預計到 2032 年，人工智慧在藥物發現領域的市場規模將達到 99 億美元，複合年成長率為 28.19%。

關鍵市場統計數據
基準年2024年 13.5億美元
預計年份：2025年 17.4億美元
預測年份 2032 99億美元
複合年成長率 (%) 28.19%

人工智慧在藥物研發領域具有變革性潛力，以及為什麼領導者現在必須重新思考他們的策略

人工智慧已從最初的研究興趣發展成為一項核心能力，它正在重塑治療候選藥物的發現、最佳化和風險評估方式。本文將介紹當前時代，演算法進步、不斷擴展的生物數據以及計算化學的突破正在融合，將生成模型、預測分析和結構模擬引入實用化的工業工作流程中。製藥公司、生物技術新興企業、受託研究機構和學術實驗室等各方相關人員正在將人工智慧整合到藥物發現價值鏈的各個環節，以縮短設計週期、提高轉化準確性並為策略性藥物組合選擇提供資訊。

隨著各組織機構的調整，核心問題正從人工智慧能否創造價值轉變為如何管理、檢驗和擴展人工智慧。如今的關鍵考慮包括：將人工智慧舉措與實驗吞吐量相匹配，為In Silico預測設定切合實際的基準，以及將人工智慧輸出與濕實驗室流程整合，以確保人類專業知識和計算模型相互補充。此外，領導層還必須權衡雲端原生平台（支援快速迭代）和本地配置（滿足嚴格的資料管治要求）之間的營運利弊。簡而言之，人工智慧在藥物研發領域的下一階段將強調嚴謹的整合、可重複的檢驗以及對能夠產生可衡量價值的候選藥物進行策略性優先排序。

根本性的技術變革和跨產業動態如何重塑藥物發現生態系統並加速新能力的產生

藥物發現格局正經歷多重相互交織的變革，而這些變革遠不止於演算法的改進所能涵蓋。首先，蛋白質結構預測的突破降低了標靶表徵的門檻，使得結合口袋和構象動力學的建模速度空前提升，從而助力先導化合物。其次，生成式化學模型的成熟使得新型骨架的構思成為可能，這些骨架可以更快地合成和測試，並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，能夠更全面地展現疾病生物學特徵，進而提升ADMET和毒性預測的表現。

同時，與科學工作流程相契合的MLOps實踐透過強調可重複性和管線管治，提高了企業的準備度。對可解釋人工智慧和可解讀方法的投資，有助於監管機構和安全團隊更有信心地解讀模型輸出。此外，學術團體、生物技術創新者和平台提供者之間日益壯大的夥伴關係生態系統，正在加速知識傳播，並開闢新的商業化路徑。這些轉變不僅提升了個人能力，也改變了藥物研發過程中團隊的組織方式、實驗的整體、風險管理方式。

評估2025年美國關稅環境將如何影響全球供應鏈、創新夥伴關係和運算平台部署選擇。

2025年關稅政策為生物製藥供應鏈以及支援人工智慧主導研發的軟硬體體系帶來了不確定性和實際調整。對於依賴高效能GPU或國際採購實驗室設備等專用硬體的機構而言，關稅政策增加了籌資策略的複雜性，迫使企業重新評估本地部署和雲端方案的整體擁有成本。一些團隊加快了雲端部署的步伐以避免進口瓶頸，而另一些團隊則投資於在地採購和長期供應商協議，以確保關鍵設備的供應。

除了硬體之外，關稅也影響了國際研究合作的結構。為了確保應對不斷變化的貿易壁壘，許可談判和跨境資料傳輸協議都進行了重新審視。因此，合成化合物的海外生產合作變得更加謹慎，並強調採用分散式研發模式，將關鍵能力在地化。同時，監管協調和跨司法管轄區的檢驗工作成為優先事項，以確保多中心臨床計畫和臨床前工作流程的連續性。雖然關稅造成了短期中斷，但也凸顯了靈活的基礎設施、多元化的供應商網路以及能夠吸收政策波動而不阻礙研發勢頭的管治框架的戰略價值。

揭示應用、技術、治療領域、最終用戶和部署模式細分之間的策略交集，以實現最有價值的成果。

解讀細分架構可以揭示人工智慧將在哪些應用、技術、治療領域、最終用戶和部署模式下產生最大影響

全面了解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的進步使研究團隊能夠更早篩選候選化合物，並減少後期研發的失敗率。最佳化臨床試驗受益於最佳化的患者招募策略和研究設計，從而提高試驗效率和代表性。高通量篩檢、電腦模擬標靶驗證和虛擬篩檢能夠更快發現潛在的化合物，從而提升先導化合物的發現流程的價值。先導化合物結構預測，在基於第一原理建模、同源建模和分子動力學模擬的支持下，仍然是目標檢驗和合理設計的基礎。

深度學習和機器學習技術驅動特徵提取和預測建模，而電腦視覺則解讀高內涵成像和表現型分析，將分子擾動與細胞反應聯繫起來。自然語言處理技術組織並挖掘海量的生物醫學文獻、專利和臨床記錄，以揭示領先技術和機制假設。在治療方面，人工智慧的應用已在腫瘤學和感染疾病，分子標靶和高通量檢測加速了學習週期。心血管和中樞神經系統計畫也在利用預測模型，但面臨與生理和臨床終點相關的獨特轉化挑戰。最終用戶包括：致力於拓展方法論前沿的學術和研究機構、將人工智慧與敏捷實驗平台結合的生物技術公司、採用預測工具縮短研發週期的委外研發機構，以及將人工智慧融入其研發工作的製藥公司。部署選項——雲端基礎、混合和本地部署——反映了速度、成本、資料管治和監管問題之間的權衡，要求組織根據其資料敏感度和協作模式來調整其基礎設施策略。

這種細分結構強調，價值在於特定領域模型、高品質資料和協調一致的業務流程的交會點。透過策略性地明確哪些應用、技術和治療終端使用者組合需要優先考慮，組織可以合理安排試點計畫並建立可重複使用的能力，而不是將資源分散到互不相干的實驗中。

決定美洲、歐洲、中東和非洲以及亞太地區招募模式、人才流動、監管參與和夥伴關係模式的區域動態

區域實際情況正在影響人工智慧驅動的藥物發現技術的應用和規模化。在美洲，強大的創業投資生態系統和成熟的生物技術叢集為演算法創新技術的快速商業化提供了支持。該地區與監管機構的對話日益聚焦於模型檢驗、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，研發項目越來越重視可重複性和審核追蹤，以滿足嚴格的合規要求。

歐洲、中東和非洲匯聚了許多傑出的學術機構和公私合作組織，共同推動基礎研究和轉化研究。歐洲各地的法律規範正在不斷改進，以應對人工智慧領域的特定問題，跨境合作也十分普遍，各方都在利用各自在特定治療領域的優勢開展合作。在中東和非洲，數據可用性和標準化臨床資料集方面仍面臨挑戰，但能力建設舉措和對當地基礎設施的投資正逐步推動其參與全球藥物研發網路。

亞太地區在藥物研發領域正迅速應用人工智慧技術，這得益於龐大的患者群體、生命科學領域大量的公共和私人投資以及強大的生產能力。東亞、南亞和大洋洲等中心城市之間的人才流動，支撐著一個充滿活力的生態系統，在這個生態系統中，新興企業和成熟公司都在嘗試雲端原生和混合部署架構。儘管跨國夥伴關係持續推動整個地區的創新，但每個地區的監管差異、人才儲備和基礎設施限制，都會影響藥物研發成果進入臨床開發和商業化計畫的速度。

現有製藥公司、人工智慧新興企業和服務供應商之間的競爭與合作將決定能力的擴散和夥伴關係關係的架構。

競爭格局的特點是角色互補而非純粹的零和博弈。現有製藥公司利用其深厚的領域知識、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，人工智慧新興企業帶來了專業的建模技術、敏捷的工程實踐以及探索新型資料來源的意願，從而為快速迭代和利基創新創造了機會。合約研究組織和服務供應商正在將人工智慧融入其服務產品中，以縮短週期時間，並為尋求外部研發能力的客戶提供差異化的價值提案。

合作開發模式涵蓋策略聯盟、共同開發計劃、技術授權和資料共用聯盟等多種形式。此類合作通常涉及學術團體貢獻基礎科學成果和客製化演算法。雲端和基礎設施供應商則扮演著推動者的角色，提供可擴展的運算資源和平台，用於託管協作工作空間、模型註冊和可重現的流程。在這些互動中，成功的公司憑藉透明的檢驗、清晰的智慧財產權框架以及將計算假設轉化為實驗結果的實際能力脫穎而出。買家和合作夥伴在評估供應商時，不僅關注演算法的複雜程度，還關注其整合成熟度、資料管理實務以及實際應用效果。

為了在管理風險和合規性的同時獲得人工智慧主導的優勢，領導者現在應該採取哪些切實可行的策略舉措和營運干預措施？

與其為了採用工具而採用工具，領導者首先應該將人工智慧舉措與明確的科學和業務目標結合。這首先要選擇數據品質充足且結果可衡量的應用場景，例如迭代式先導藥物最適化或靶點毒性分級，並建立結合預測性能和營運影響的成功指標。接下來，要投資於資料基礎建置：精心維護高品質的內部資料集，利用管理良好的外部資料來源進行擴充，並實施能夠提高模型可解釋性和可重現性的元資料標準。同時，對與生命科學工作流程相契合的機器學習運維（MLOps）進行投資，將加快部署速度，並創建監管機構和安全團隊所需的審核追蹤。

在營運層面，應組成跨學科團隊，將計算科學家與藥物化學家、毒理學家和臨床科學家結合，以確保模型輸出具有實際應用價值。採用分階段驗證方法，即在將模型整合到更廣泛的決策框架之前，先利用有限的試點實驗來驗證其有效性。在採購和基礎設施方面，應權衡雲端部署、混合部署和本地部署與資料保密性、迭代速度和整體擁有成本之間的檢驗。最後，應制定管治，以解決智慧財產權、資料隱私和倫理使用問題，並建立持續學習流程，將實驗中獲得的洞見回饋到模型改進中。透過按順序採取這些措施，組織可以在保持科學嚴謹性的同時，負責任地擴展其人工智慧能力。

對用於產生研究結果的調查方法進行透明的描述，包括資料來源、檢驗技術、專家參與和限制。

本研究整合了多方面的證據，以全面了解人工智慧在藥物研發中的應用。主要研究包括對製藥和生物技術公司、合約研究機構和學術研究中心的專家進行結構化訪談，以及對記錄方法學進展的同行評審文獻和預印本進行技術審查。次要研究包括分析已發布的監管指南、公司關於平台採用情況的披露資訊，以及展示人工智慧與濕實驗室流程成功整合的案例研究。方法論論點的定量檢驗依賴於技術資訊來源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。

我們的分析方法強調三角驗證。我們結合專家觀點、文獻證據和已記錄的案例，以識別穩健的模式，而非依賴單一研究。在條件允許的情況下，我們將資料中引用的專有資料集或供應商聲明與獨立的技術評估和復現結果進行交叉核對。我們承認研究有其局限性，包括難以從私人公司獲取詳細的績效指標、不同機構間資料集標準的差異，以及方法論的快速變化超過了靜態報告的局限性。為了彌補這些局限性，我們的分析重點突出了由多個資訊來源支持的反覆出現的主題，並明確指出了需要進一步開展一手研究和技術基準測試的領域。

提煉出對決策者在人工智慧、生命科學和不斷變化的全球動態融合過程中具有核心意義的啟示

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。要獲得可重複的結果，需要將人工智慧與實驗設計、強大的資料管治以及分階段的檢驗策略進行有意識的整合。專注於高價值應用案例、重視資料管理並組成跨職能團隊的領導者，相比那些只進行孤立的試點研究而缺乏端到端整合的機構，將獲得更大的收益。

此外，地緣政治和政策因素，例如關稅驅動的供應鏈調整和區域監管差異，凸顯了靈活的基礎設施和多元化夥伴關係關係的重要性。成功取決於將卓越的技術與營運規範相結合，例如清晰的指標、透明的檢驗以及涵蓋智慧財產權、倫理和監管要求的治理框架。透過優先考慮這些要素，企業可以將管治的潛力轉化為永續的能力，從而加速治療方法的研發並改善患者的治療效果。

目錄

第1章：序言

第2章調查方法

第3章執行摘要

第4章 市場概覽

第5章 市場洞察

  • 先進的生成式人工智慧模型加速了新型小分子的設計與合成。
  • 將多組體學資料集與深度學習結合，用於腫瘤藥物研發中的精準標靶識別
  • 實施人工智慧驅動的預測性 ADMET 模型以減少後期臨床試驗失敗
  • 引入強化學習演算法以最佳化抗體設計和治療效果。
  • 採用雲端原生人工智慧平台實現可擴展的虛擬篩檢和協作調查工作流程
  • 利用真實世界證據和人工智慧分析快速進行藥物再利用，以應對新出現的健康危機
  • 生物製藥公司與科技巨頭達成策略合作，共同開發人工智慧驅動的藥物發現流程。
  • 應用聯邦學習框架，在分散式專有資料集上安全地訓練人工智慧模型
  • 管理方案和指南正在塑造人工智慧在藥物發現過程中的檢驗和透明度
  • 引入可預測的人工智慧技術，以提高預測結果的可解釋性和監管機構的核准。

第6章：美國關稅的累積影響，2025年

第7章：人工智慧的累積影響，2025年

第8章 按應用程式分類的人工智慧在藥物發現領域的市場

  • ADMET和毒性預測
    • 動態預測
    • 藥物動力學預測
    • 毒性預測
  • 臨床試驗最佳化
    • 病患招募
    • 測試設計最佳化
  • 命中識別
    • 高通量篩檢
    • In Silico靶檢驗
    • 虛擬篩選
  • 先導藥物最適化
    • 從頭開始的藥物設計
    • 定量構效關係
    • 基於結構的藥物設計
  • 蛋白質結構預測
    • 第一原理建模
    • 同源性建模
    • 分子動力學模擬

9. 人工智慧在藥物發現市場的應用（依技術分類）

  • 電腦視覺
  • 深度學習
  • 機器學習
  • 自然語言處理

第10章 人工智慧在藥物發現市場的應用（依治療領域分類）

  • 心血管疾病
  • 中樞神經系統
  • 感染疾病
  • 腫瘤學

第11章 人工智慧在藥物發現市場的應用（依最終用戶分類）

  • 學術研究機構
  • 生技公司
  • 合約研究組織
  • 製藥公司

第12章 人工智慧在藥物發現市場的部署模式

  • 雲端基礎的
  • 混合
  • 本地部署

第13章 各地區藥物研發市場中的人工智慧

  • 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 藥物發現領域人工智慧市場（按集團分類）

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

第15章 各國藥物研發市場中的人工智慧

  • 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 競爭格局

  • 2024年市佔率分析
  • FPNV定位矩陣，2024
  • 競爭分析
    • Schrodinger, Inc.
    • Recursion Pharmaceuticals, Inc.
    • Exscientia plc
    • Valo Health, Inc.
    • Atomwise, Inc.
    • Insilico Medicine, Inc.
    • BenevolentAI Limited
    • Cloud Pharmaceuticals, Inc.
    • Healx Limited
    • Microsoft Corporation
Product Code: MRR-031BF22F953D

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