首頁 > 市場調查報告書 > 製藥

藥物開發人工智能人工智能藥物研發

市場調查報告書

商品編碼

2007854

人工智慧藥物發現平台市場預測至2034年—按平台類型、部署模式、技術、應用、最終用戶和地區分類的全球分析

AI Drug Discovery Platforms Market Forecasts to 2034 - Global Analysis By Platform Type, Deployment Mode, Technology, Application, End User and By Geography

出版日期: 2026年04月06日 | 出版商:

Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的數據，全球 AI 藥物發現平台市場預計將在 2026 年達到 48 億美元，並在預測期內以 21.3% 的複合年成長率成長，到 2034 年達到 226 億美元。

人工智慧藥物發現平台是指利用機器學習、深度學習和預測分析等技術，透過軟體主導的運算系統，加速候選藥物的辨識、篩檢和最佳化。這些平台整合基因體學、蛋白質體學和臨床數據，繪製生物標靶圖譜，模擬分子間相互作用，並預測治療效果和毒性特徵。這些平台能夠幫助製藥和生物技術公司識別癌症治療標靶、最佳化先導化合物研發流程、進行藥物重定位研究以及設計適應性臨床試驗。

簡化藥物研發流程

隨著製藥公司面臨研發成本飆升和傳統藥物研發流程盈利下滑的困境，精簡藥物研發流程成為關鍵促進因素。人工智慧驅動的平台透過計算篩選數十億個分子結構，並與檢驗的目標進行比對，將候選化合物的篩檢時間從數年縮短至數週。人工智慧專家與大型製藥企業之間的策略合作日益增多，不僅能夠產生基於里程碑的合作收益，還能加強對平台在癌症和罕見疾病適應症方面的商業性檢驗。

對資料隱私和智慧財產權的擔憂

對資料隱私和智慧財產權的擔憂阻礙了人工智慧藥物發現平台的普及應用。這一點在成熟的製藥公司中尤其明顯，它們不願與第三方人工智慧供應商共用其專有的基因組資料集和化合物庫。關於人工智慧生成分子的智慧財產權歸屬，監管方面的模糊性為平台開發商和製藥合作夥伴帶來了法律上的不確定性。這些障礙會延遲企業採用人工智慧藥物發現平台的決策，延長銷售週期，並限制資料共用協議對於訓練高性能人工智慧藥物發現模型至關重要。

擴展應用範圍，適用於罕見疾病

將人工智慧技術應用於罕見疾病領域蘊藏著巨大的機會。即使在患者群體較小、傳統臨床經濟學難以奏效的疾病領域，人工智慧平台也能幫助識別出具有成本效益的候選藥物。包括美國食品藥物管理局（FDA）在內的監管機構正在為罕見疾病治療藥物提供快速核准流程，降低上市風險。慈善機構和患者權益倡導組織對罕見疾病研究投入的不斷增加，正在推動人工智慧驅動的藥物發現能力在目前佔據主導地位的腫瘤市場之外，持續發展。

無法成功過渡到臨床應用的風險

人工智慧驅動的藥物發現平台信譽面臨的結構性威脅之一是臨床試驗失敗的風險。人工智慧預測的候選化合物仍需成功通過臨床前和臨床檢驗階段。人工智慧識別的化合物在第二期和第三期臨床試驗中的高脫落率會削弱製藥合作夥伴的信心，並延緩平台的推廣應用。監管機構對人工智慧產生的證據包的審查以及缺乏統一的人工智慧藥物申請指南，進一步加劇了臨床試驗推廣應用的不確定性。

新冠疫情的影響：

新冠疫情大大加速了人工智慧藥物發現平台的應用，因為製藥公司迫切需要快速識別抗病毒候選化合物。疫情期間，人工智慧與生物製藥公司的合作使得多個符合FDA審查條件的候選化合物在更短的時間內湧現。自疫情爆發以來，對人工智慧藥物發現基礎設施的結構性投資持續進行，各機構已將平台功能整合到標準的早期藥物發現工作流程中。

在預測期內，臨床試驗設計平台細分市場預計將成為最大的細分市場。

預計在預測期內，臨床試驗設計平台細分市場將佔據最大的市場佔有率，這主要得益於製藥公司面臨的降低臨床開發成本和提高患者招募效率的日益成長的壓力。人工智慧驅動的試驗設計工具透過最佳化通訊協定參數、識別最佳生物標記定義患者群體以及預測脫落率，顯著降低了營運成本。在關鍵市場，基於人工智慧的自適應試驗設計獲得監管部門的核准不斷擴大，進一步推動了該平台的應用。

在預測期內，基於雲端的細分市場預計將呈現最高的複合年成長率。

在預測期內，受大規模多組體學資料集在人工智慧模型訓練中對可擴展性的需求，以及跨地域協作存取共用藥物研發基礎設施的需求驅動，基於雲端的細分市場預計將呈現最高的成長率。採用雲端技術無需對本地運算硬體進行資本投資，並支援新興生技公司青睞的靈活訂閱模式。超大規模資料中心業者大規模雲端服務商對生命科學領域雲端基礎設施的投資正在加速提升雲端藥物研發工作負載的效能標準。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率，這得益於其集中了主要企業的製藥和生物技術公司、對人工智慧醫療創新領域的大量創業投資投資，以及支持人工智慧藥物研發的完善法規結構。美國擁有大多數平台開發商和積極採用人工智慧藥物研發解決方案的製藥合作夥伴。此外，美國國立衛生研究院 (NIH) 和生物醫學高級研究與發展局 (BARDA) 的資助計畫正在津貼人工智慧藥物研發研究，從而深化創新生態系統。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、日本和韓國生物技術行業的快速擴張、政府主導的基因組數據基礎設施投資以及國內製藥行業日益成長的雄心。中國的國家人工智慧發展策略已明確將應用領域鎖定在製藥業，國家資助的人工智慧藥物研發聯盟正在加速平台升級。該地區的生物技術投資穩步成長，吸引了來自世界各地的人工智慧藥物研發平台供應商與其建立夥伴關係。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

企業概況
- 對其他市場參與者（最多 3 家公司）進行全面分析
- 對主要企業進行SWOT分析（最多3家公司）
區域細分
- 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
競爭性標竿分析
- 根據產品系列、地理覆蓋範圍和策略聯盟對主要企業進行基準分析。

北美洲
- 美國
- 加拿大
- 墨西哥
歐洲
- 英國
- 德國
- 法國
- 義大利
- 西班牙
- 荷蘭
- 比利時
- 瑞典
- 瑞士
- 波蘭
- 其他歐洲國家
亞太地區
- 中國
- 日本
- 印度
- 韓國
- 澳洲
- 印尼
- 泰國
- 馬來西亞
- 新加坡
- 越南
- 其他亞太國家
南美洲
- 巴西
- 阿根廷
- 哥倫比亞
- 智利
- 秘魯
- 其他南美國家
世界其他地區（RoW）
- 中東
  - 沙烏地阿拉伯
  - 阿拉伯聯合大公國
  - 卡達
  - 以色列
  - 其他中東國家
- 非洲
  - 南非
  - 埃及
  - 摩洛哥
  - 其他非洲國家

第11章策略市場資訊

工業價值網路和供應鏈評估
空白區域和機會地圖
產品演進與市場生命週期分析
通路、經銷商和打入市場策略的評估

第12章產業趨勢與策略舉措

併購
夥伴關係、聯盟和合資企業
新產品發布和認證
擴大生產能力和投資
其他策略舉措

第13章：公司簡介

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

簡介目錄圖表

Product Code: SMRC34760

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

Market Dynamics:

Driver:

Accelerated Drug Pipeline Efficiency

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

Restraint:

Data Privacy and IP Concerns

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

Opportunity:

Rare Disease Application Expansion

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

Threat:

Clinical Translation Failure Risk

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

Covid-19 Impact:

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

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

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

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

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

Region with largest share:

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

Region with highest CAGR:

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

Key players in the market

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

Key Developments:

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

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

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

Platform Types Covered:

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

Deployment Modes Covered:

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

Technologies Covered:

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

Applications Covered:

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

End Users Covered:

Pharmaceutical Companies
Biotechnology Firms
CROs
Academic Institutes
Healthcare Organizations
Government Research Bodies
Other End Users

Regions Covered:

North America
- United States
- Canada
- Mexico
Europe
- United Kingdom
- Germany
- France
- Italy
- Spain
- Netherlands
- Belgium
- Sweden
- Switzerland
- Poland
- Rest of Europe
Asia Pacific
- China
- Japan
- India
- South Korea
- Australia
- Indonesia
- Thailand
- Malaysia
- Singapore
- Vietnam
- Rest of Asia Pacific
South America
- Brazil
- Argentina
- Colombia
- Chile
- Peru
- Rest of South America
Rest of the World (RoW)
- Middle East
  - Saudi Arabia
  - United Arab Emirates
  - Qatar
  - Israel
  - Rest of Middle East
- Africa
  - South Africa
  - Egypt
  - Morocco
  - Rest of Africa

What our report offers:

Market share assessments for the regional and country-level segments
Strategic recommendations for the new entrants
Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 3032 and 2034
Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
Strategic recommendations in key business segments based on the market estimations
Competitive landscaping mapping the key common trends
Company profiling with detailed strategies, financials, and recent developments
Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

Company Profiling
- Comprehensive profiling of additional market players (up to 3)
- SWOT Analysis of key players (up to 3)
Regional Segmentation
- Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
Competitive Benchmarking
- Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

1 Executive Summary

1.1 Market Snapshot and Key Highlights
1.2 Growth Drivers, Challenges, and Opportunities
1.3 Competitive Landscape Overview
1.4 Strategic Insights and Recommendations

2 Research Framework

2.1 Study Objectives and Scope
2.2 Stakeholder Analysis
2.3 Research Assumptions and Limitations
2.4 Research Methodology
- 2.4.1 Data Collection (Primary and Secondary)
- 2.4.2 Data Modeling and Estimation Techniques
- 2.4.3 Data Validation and Triangulation
- 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

3.1 Market Definition and Structure
3.2 Key Market Drivers
3.3 Market Restraints and Challenges
3.4 Growth Opportunities and Investment Hotspots
3.5 Industry Threats and Risk Assessment
3.6 Technology and Innovation Landscape
3.7 Emerging and High-Growth Markets
3.8 Regulatory and Policy Environment
3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

4.1 Porter's Five Forces Analysis
- 4.1.1 Supplier Bargaining Power
- 4.1.2 Buyer Bargaining Power
- 4.1.3 Threat of Substitutes
- 4.1.4 Threat of New Entrants
- 4.1.5 Competitive Rivalry
4.2 Market Share Analysis of Key Players
4.3 Product Benchmarking and Performance Comparison

5 Global AI Drug Discovery Platforms Market, By Platform Type

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

6 Global AI Drug Discovery Platforms Market, By Deployment Mode

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

7 Global AI Drug Discovery Platforms Market, By Technology

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

8 Global AI Drug Discovery Platforms Market, By Application

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

9 Global AI Drug Discovery Platforms Market, By End User

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

10 Global AI Drug Discovery Platforms Market, By Geography

10.1 North America
- 10.1.1 United States
- 10.1.2 Canada
- 10.1.3 Mexico
10.2 Europe
- 10.2.1 United Kingdom
- 10.2.2 Germany
- 10.2.3 France
- 10.2.4 Italy
- 10.2.5 Spain
- 10.2.6 Netherlands
- 10.2.7 Belgium
- 10.2.8 Sweden
- 10.2.9 Switzerland
- 10.2.10 Poland
- 10.2.11 Rest of Europe
10.3 Asia Pacific
- 10.3.1 China
- 10.3.2 Japan
- 10.3.3 India
- 10.3.4 South Korea
- 10.3.5 Australia
- 10.3.6 Indonesia
- 10.3.7 Thailand
- 10.3.8 Malaysia
- 10.3.9 Singapore
- 10.3.10 Vietnam
- 10.3.11 Rest of Asia Pacific
10.4 South America
- 10.4.1 Brazil
- 10.4.2 Argentina
- 10.4.3 Colombia
- 10.4.4 Chile
- 10.4.5 Peru
- 10.4.6 Rest of South America
10.5 Rest of the World (RoW)
- 10.5.1 Middle East
  - 10.5.1.1 Saudi Arabia
  - 10.5.1.2 United Arab Emirates
  - 10.5.1.3 Qatar
  - 10.5.1.4 Israel
  - 10.5.1.5 Rest of Middle East
- 10.5.2 Africa
  - 10.5.2.1 South Africa
  - 10.5.2.2 Egypt
  - 10.5.2.3 Morocco
  - 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

11.1 Industry Value Network and Supply Chain Assessment
11.2 White-Space and Opportunity Mapping
11.3 Product Evolution and Market Life Cycle Analysis
11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

12.1 Mergers and Acquisitions
12.2 Partnerships, Alliances, and Joint Ventures
12.3 New Product Launches and Certifications
12.4 Capacity Expansion and Investments
12.5 Other Strategic Initiatives

13 Company Profiles

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

簡介目錄圖表

List of Tables

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