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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

1998679

2026 年至 2035 年人工智慧 (AI) 在藥物發現領域的市場機會、成長要素、產業趨勢和預測。

Artificial Intelligence in Drug Discovery Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2026 - 2035

出版日期: 2026年03月05日 | 出版商:

Global Market Insights Inc. | 英文 183 Pages | 商品交期: 2-3個工作天內

價格

簡介目錄

2025 年全球藥物發現領域的人工智慧 (AI) 市場價值為 31 億美元，預計到 2035 年將達到 439 億美元，年複合成長率為 30.5%。

隨著製藥和生物技術公司擴大將先進的計算技術融入其研究方法，人工智慧（AI）在藥物研發行業的應用正在迅速擴展。日益嚴重的複雜慢性健康問題促使各機構加速開發創新療法，推動了人工智慧驅動的藥物研發工具的普及。人工智慧技術透過提高效率、縮短研究週期和最佳化整個藥物研發流程中的決策，正在改變傳統的藥物研發方法。這些平台支援各種研究活動，能夠對廣泛的生物醫學資料集進行高級數據分析和預測建模。人工智慧解決方案被廣泛用於識別生物標靶、最佳化候選化合物、設計新型分子結構以及改進早期測試流程。人們日益關注傳統研發活動的高成本和漫長過程，這也推動了人工智慧平台的整合，以提高效率和準確性。此外，人們對精準醫療和個人化療法的興趣日益濃厚，也使得能夠分析複雜生物資訊的智慧藥物研發解決方案的需求不斷成長。數位醫療基礎設施的擴展和多個地區對生物技術創新投資的增加也促進了市場成長。我們持續進行的研究和開發舉措旨在開發更透明、更可靠的人工智慧模型，這將進一步增強人工智慧市場在全球藥物發現領域的前景。

市場範圍
開始年份	2025
預測期	2026-2035
上市時的市場規模	31億美元
預測金額	439億美元
複合年成長率	30.5%

預計到2025年，軟體領域將佔據67.9%的市場佔有率，並在2026年至2035年間以30.2%的複合年成長率成長。隨著各機構越來越依賴數位化解決方案來管理龐大的生物醫學資料集並進行複雜的預測分析，軟體平台正成為人工智慧主導的藥物發現生態系統的重要組成部分。這些平台透過支援研究人員進行計算模擬、分子建模和進階數據解讀，為藥物發現工作流程的關鍵階段提供支援。隨著人工智慧架構（包括機器學習和深度學習技術）的不斷進步，這些軟體工具的分析能力也不斷提升。先進演算法的整合使研究人員能夠進行大規模化學模擬，並更有效率地識別有前景的候選藥物。

到2025年，機器學習領域將佔據82.6%的市場。機器學習技術憑藉其處理和解讀極其複雜的科學資料集的能力，成為人工智慧藥物研發領域創新發展的主要驅動力。這些演算法能夠分析各種生物和化學資料來源，使研究人員能夠獲得預測性見解，從而改善早期藥物研發決策。機器學習模型使科學家能夠識別基因組資訊、分子庫和實驗資料集中的模式，顯著加快潛在治療候選藥物的篩選。此外，將臨床數據和真實世界數據整合到機器學習模型的趨勢日益成長，正在促進個人化治療策略的開發。雲端運算基礎設施和可擴展資料處理平台的進步進一步推動了機器學習技術在藥物研發領域的廣泛應用。

到2025年，北美人工智慧（AI）藥物研發市場佔有率將達到47.7%。北美人工智慧產業正經歷強勁成長，這主要得益於製藥和生物技術公司對先進數位技術的快速應用。該地區擁有高度發展的創新生態系統，有利於將人工智慧解決方案融入生物醫學研究活動。對生物技術研究和數位醫療基礎設施的大量投資進一步加速了先進的人工智慧藥物研發平台的發展。此外，完善的法規結構也促進了新興技術在醫療保健研究中的安全有效應用，從而推動了市場擴張。

生態系分析
影響產業的因素
- 促進因素
  - 複雜慢性疾病的盛行率不斷上升。
  - 醫療保健領域數據的爆炸性成長數位化
  - 人工智慧演算法和運算能力的進步
  - 擴大科技公司與製藥公司之間的合作
- 產業潛在風險與挑戰
  - 數據品質和整合方面的挑戰
  - 監理和倫理問題
- 市場機遇
  - 個人化醫療和精準醫療的擴展
  - 生成式人工智慧在分子設計領域的興起
成長潛力分析
監理情勢（基於初步調查）
- 北美洲
  - 美國
  - 加拿大
- 歐洲
- 亞太地區
- 拉丁美洲
- 中東和非洲
未來市場趨勢（基於初步研究）
技術格局
- 目前技術
- 新興技術
投資和資金籌措趨勢
人工智慧和生成式人工智慧對市場的影響
波特五力分析
PESTEL 分析

第4章競爭情勢

介紹
企業市佔率分析
- 北美洲
- 歐洲
- 亞太地區
企業矩陣分析
主要市場公司的競爭分析
競爭定位矩陣
主要進展
- 併購
- 夥伴關係與合作
- 新產品發布
- 業務拓展計劃

第5章市場估計與預測：依組件分類，2022-2035年

軟體
- 現場
- 基於雲端的
服務

第6章市場估計與預測：依技術分類，2022-2035年

機器學習
- 深度學習
- 監督式學習
- 無監督學習
- 其他機器學習技術
其他技術

第7章市場估計與預測：依應用領域分類，2022-2035年

分子庫篩檢
目標識別
藥物最佳化與仿單標示外用藥
德諾和製藥
臨床前試驗

第8章市場估計與預測：依治療領域分類，2022-2035年

腫瘤學
神經退化性疾病
發炎性疾病
感染疾病
代謝性疾病
罕見疾病
心血管疾病
其他治療領域

第9章市場估計與預測：依最終用途分類，2022-2035年

製藥和生物技術公司
合約研究組織（CRO）
其他最終用戶

第10章市場估價與預測：依地區分類，2022-2035年

北美洲
- 美國
- 加拿大
歐洲
- 德國
- 英國
- 法國
- 西班牙
- 義大利
- 荷蘭
亞太地區
- 中國
- 日本
- 印度
- 澳洲
- 韓國
拉丁美洲
- 巴西
- 墨西哥
- 阿根廷
中東和非洲
- 南非
- 沙烏地阿拉伯
- 阿拉伯聯合大公國

第11章：公司簡介

Isomorphic Labs（Alphabet）
Microsoft Corporation
NVIDIA Corporation
International Business Machines Corporation
Schrodinger
Recursion Pharmaceuticals
Insilico Medicine
BenevolentAI
Atomwise
Insitro
Deep Genomics
Iktos
Deargen
9Bio Therapeutics
Aureka Biotechnologies
CellCodex Technology Limited
chAIron
DenovAI Biotech
Examol
Helical.AI
Orakl Oncology
Therenia

簡介目錄

Product Code: 6361

The Global Artificial Intelligence in Drug Discovery Market was valued at USD 3.1 billion in 2025 and is estimated to grow at a CAGR of 30.5% to reach USD 43.9 billion by 2035.

Artificial Intelligence in Drug Discovery Market - IMG1

The artificial intelligence in the drug discovery industry is witnessing rapid expansion as pharmaceutical and biotechnology companies increasingly integrate advanced computational technologies into research processes. The growing burden of complex and long-term health conditions is encouraging organizations to accelerate the development of innovative therapeutics, which in turn is driving the adoption of AI-driven discovery tools. Artificial intelligence technologies are transforming traditional drug development methods by improving efficiency, reducing research timelines, and optimizing decision-making across the discovery pipeline. These platforms support various research activities by enabling advanced data analysis and predictive modeling across extensive biomedical datasets. Solutions powered by artificial intelligence are widely used to identify biological targets, optimize candidate compounds, design novel molecular structures, and improve early-stage testing processes. Rising concerns regarding the high cost and lengthy duration associated with conventional research and development activities are also encouraging the integration of AI platforms that enhance productivity and accuracy. Furthermore, increasing interest in precision medicine and personalized therapeutic approaches is creating additional demand for intelligent drug discovery solutions capable of analyzing complex biological information. Expanding digital healthcare infrastructure and growing investments in biotechnology innovation across several regions are also contributing to market growth. Continuous research initiatives aimed at developing more transparent and reliable artificial intelligence models are further strengthening the outlook of the global artificial intelligence in drug discovery market.

Market Scope
Start Year	2025
Forecast Year	2026-2035
Start Value	$3.1 Billion
Forecast Value	$43.9 Billion
CAGR	30.5%

The software segment accounted for 67.9% share in 2025 and is projected to grow at a CAGR of 30.2% throughout 2026-2035. Software platforms have become a fundamental component of the AI-driven drug discovery ecosystem as organizations increasingly rely on digital solutions to manage vast biomedical datasets and conduct complex predictive analyses. These platforms support critical stages of the drug discovery workflow by enabling researchers to perform computational simulations, molecular modeling, and advanced data interpretation. Continuous advancements in artificial intelligence architectures, including machine learning and deep learning techniques, are enhancing the analytical capabilities of these software tools. The integration of sophisticated algorithms allows researchers to perform large-scale chemical simulations and identify promising therapeutic candidates more efficiently.

The machine learning segment held 82.6% share in 2025. Machine learning technologies have become the primary engine driving innovation in AI-based drug discovery because of their ability to process and interpret highly complex scientific datasets. These algorithms analyze diverse biological and chemical data sources, allowing researchers to generate predictive insights that improve early-stage drug development decisions. Machine learning models enable scientists to identify patterns within genomic information, molecular libraries, and experimental datasets, which significantly accelerates the identification of viable therapeutic candidates. In addition, the growing integration of clinical and real-world data into machine learning models is strengthening the development of personalized treatment strategies. Advancements in cloud computing infrastructure and scalable data processing platforms are further supporting the widespread deployment of machine learning technologies within pharmaceutical research environments.

North America Artificial Intelligence in Drug Discovery Market held 47.7% share in 2025. The North America artificial intelligence in drug discovery industry is experiencing strong growth due to the rapid adoption of advanced digital technologies across pharmaceutical and biotechnology organizations. The region benefits from a highly developed innovation ecosystem that encourages the integration of artificial intelligence solutions into biomedical research activities. Strong financial investment in biotechnology research and digital healthcare infrastructure is further accelerating the development of advanced AI-driven drug discovery platforms. Supportive regulatory frameworks also contribute to market expansion by encouraging the safe and effective use of emerging technologies in healthcare research.

Major companies operating in the Global Artificial Intelligence in Drug Discovery Market include Isomorphic Labs (Alphabet), Microsoft Corporation, NVIDIA Corporation, International Business Machines Corporation, Schrodinger, Recursion Pharmaceuticals, Insilico Medicine, BenevolentAI, Atomwise, Insitro, Deep Genomics, Iktos, Deargen, 9Bio Therapeutics, Aureka Biotechnologies, CellCodex Technology Limited, chAIron, DenovAI Biotech, Examol, Helical.AI, Orakl Oncology, and Therenia. Companies operating in the Global Artificial Intelligence in Drug Discovery Market are implementing multiple strategies to strengthen their technological capabilities and expand market influence. One key approach involves investing heavily in research and development to enhance the performance of AI algorithms used in molecular modeling and predictive analytics. Many organizations are also forming strategic collaborations with pharmaceutical firms, biotechnology companies, and research institutions to accelerate the development of innovative therapeutic solutions. Expanding cloud-based computing infrastructure and high-performance data platforms is another major focus area that allows companies to process large biomedical datasets more efficiently. Additionally, firms are prioritizing the integration of advanced analytics, automation tools, and scalable machine learning models to improve drug discovery workflows.

Chapter 1 Methodology and Scope

1.1 Market scope and definition
1.2 Research approach
1.3 Quality commitments
- 1.3.1 GMI AI policy and data integrity commitment
  - 1.3.1.1 Source consistency protocol
1.4 Research trail and confidence scoring
- 1.4.1 Research trail components
- 1.4.2 Scoring components
1.5 Data collection
- 1.5.1 Partial list of primary sources
1.6 Data mining sources
- 1.6.1 Paid sources
  - 1.6.1.1 Sources, by region
1.7 Base estimates and calculations
- 1.7.1 Revenue share analysis
- 1.7.2 Base year calculation
1.8 Forecast model
1.9 Research transparency addendum
- 1.9.1 Source attribution framework
- 1.9.2 Quality assurance metrics
- 1.9.3 Our commitment to trust

Chapter 2 Executive Summary

2.1 Industry 360⁰ synopsis
2.2 Market Trends
- 2.2.1 Business trends
- 2.2.2 Regional trends
- 2.2.3 Component trends
- 2.2.4 Technology trends
- 2.2.5 Application type trends
- 2.2.6 Therapeutic area trends
- 2.2.7 End use trends
2.3 CXO perspectives: Strategic imperatives

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis
3.2 Industry impact forces
- 3.2.1 Growth drivers
  - 3.2.1.1 Increasing prevalence of complex and chronic diseases
  - 3.2.1.2 Data explosion and digitization in healthcare
  - 3.2.1.3 Advancements in AI algorithms and computing power
  - 3.2.1.4 Growing collaboration between tech and pharma companies
- 3.2.2 Industry pitfalls and challenges
  - 3.2.2.1 Data quality and integration issues
  - 3.2.2.2 Regulatory and ethical concerns
- 3.2.3 Market opportunities
  - 3.2.3.1 Expansion of personalized and precision medicine
  - 3.2.3.2 Emergence of generative AI in molecule design
3.3 Growth potential analysis
3.4 Regulatory landscape (Driven by Primary Research)
- 3.4.1 North America
  - 3.4.1.1 U.S.
  - 3.4.1.2 Canada
- 3.4.2 Europe
- 3.4.3 Asia Pacific
- 3.4.4 Latin America
- 3.4.5 Middle East and Africa
3.5 Future market trends (Driven by Primary Research)
3.6 Technological landscape
- 3.6.1 Current technologies
- 3.6.2 Emerging technologies
3.7 Investment and funding landscape
3.8 Impact of AI and generative AI on the market
3.9 Porter's analysis
3.10 PESTEL analysis

Chapter 4 Competitive Landscape, 2025

4.1 Introduction
4.2 Company market share analysis
- 4.2.1 North America
- 4.2.2 Europe
- 4.2.3 Asia Pacific
4.3 Company matrix analysis
4.4 Competitive analysis of major market players
4.5 Competitive positioning matrix
4.6 Key developments
- 4.6.1 Merger and acquisition
- 4.6.2 Partnership and collaboration
- 4.6.3 New product launches
- 4.6.4 Expansion plans

Chapter 5 Market Estimates and Forecast, By Component, 2022 - 2035 ($ Mn)

5.1 Key trends
5.2 Software
- 5.2.1 On-premise
- 5.2.2 Cloud-based
5.3 Services

Chapter 6 Market Estimates and Forecast, By Technology, 2022 - 2035 ($ Mn)

6.1 Key trends
6.2 Machine learning
- 6.2.1 Deep learning
- 6.2.2 Supervised learning
- 6.2.3 Unsupervised learning
- 6.2.4 Other machine learning technologies
6.3 Other technologies

Chapter 7 Market Estimates and Forecast, By Application Type, 2022 - 2035 ($ Mn)

7.1 Key trends
7.2 Molecular library screening
7.3 Target identification
7.4 Drug optimization and repurposing
7.5 De novo drug designing
7.6 Preclinical testing

Chapter 8 Market Estimates and Forecast, By Therapeutic Area, 2022 - 2035 ($ Mn)

8.1 Key trends
8.2 Oncology
8.3 Neurodegenerative diseases
8.4 Inflammatory
8.5 Infectious diseases
8.6 Metabolic diseases
8.7 Rare diseases
8.8 Cardiovascular diseases
8.9 Other therapeutic areas

Chapter 9 Market Estimates and Forecast, By End Use, 2022 - 2035 ($ Mn)

9.1 Key trends
9.2 Pharmaceutical and biotechnology companies
9.3 Contract research organizations (CROs)
9.4 Other end users

Chapter 10 Market Estimates and Forecast, By Region, 2022 - 2035 ($ Mn)

10.1 Key trends
10.2 North America
- 10.2.1 U.S.
- 10.2.2 Canada
10.3 Europe
- 10.3.1 Germany
- 10.3.2 UK
- 10.3.3 France
- 10.3.4 Spain
- 10.3.5 Italy
- 10.3.6 Netherlands
10.4 Asia Pacific
- 10.4.1 China
- 10.4.2 Japan
- 10.4.3 India
- 10.4.4 Australia
- 10.4.5 South Korea
10.5 Latin America
- 10.5.1 Brazil
- 10.5.2 Mexico
- 10.5.3 Argentina
10.6 Middle East and Africa
- 10.6.1 South Africa
- 10.6.2 Saudi Arabia
- 10.6.3 UAE