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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

1954421

日本人工智慧藥物發現市場規模、市場佔有率、趨勢和預測：按服務提供、應用、治療領域、最終用戶和地區分類（2026-2034 年）

Japan AI in Drug Discovery Market Size, Share, Trends and Forecast by Offering, Application, Therapeutic Area, End User, and Region, 2026-2034

出版日期: 2026年02月01日 | 出版商:

IMARC | 英文 147 Pages | 商品交期: 5-7個工作天內

價格

簡介目錄

2025年，日本人工智慧驅動藥物研發市場規模達1.3106億美元。展望未來，IMARC Group預測，到2034年，該市場規模將達到7.9609億美元，2026年至2034年的複合年成長率（CAGR）為22.20%。這一市場成長要素以下因素：支持製藥公司的自主人工智慧基礎設施的進步；量子人工智慧混合技術的融合加速了分子生成和藥物適用性最佳化；以及政府主導的數位轉型舉措推動了全國人工智慧醫療體系的建設。此外，製藥公司與人工智慧技術供應商之間為研發First-in-Class藥物而加強的合作，也促進了日本人工智慧在藥物研發領域市場佔有率的不斷擴大。

日本人工智慧驅動藥物研發市場的發展趨勢：

自主人工智慧基礎設施的開發及其在製藥公司中的應用

在日本，專為藥物研發設計的自主人工智慧基礎設施的引進，正推動人工智慧驅動的藥物發現領域取得變革性進展。Astellas製藥、第一三共製藥和小野藥品工業株式會社等日本領先製藥公司正利用先進的高效能運算平台建構複雜的人工智慧模型，用於藥物發現。這些公司利用專用平台，使藥物研發人員能夠開發和部署人工智慧模型，從而從生物分子數據中獲取生物學見解。此基礎設施支援關鍵的運算任務，例如蛋白質結構預測、分子對接模擬以及設計針對目標分子結合最佳化的新型蛋白質結構。可客製化的模組化程式框架和最佳化的人工智慧推理能力，使藥物研究人員能夠顯著縮短藥物發現週期，同時提高發現有前景的候選藥物的機率。這些技術的應用標誌著一種策略轉變，即利用運算能力和先進演算法來應對傳統實驗方法難以有效解決的複雜生物學挑戰。

量子和人工智慧混合技術將提升藥物發現能力

量子運算與人工智慧的融合代表著最尖端科技創新，它正在重塑日本的藥物研發方法。主要企業的製藥部門正率先應用量子混合計算工作流程，以增強其生成大規模語言模型的能力，這些模型專門用於分子設計和候選藥物的篩選。與僅使用傳統運算方法產生的分子相比，這些量子增強型人工智慧系統在產生具有更優異類藥特性的新型分子結構方面表現出卓越的性能。量子人工智慧混合方法克服了傳統計算在探索龐大化學空間方面的根本局限性，而化學空間對於篩選有前景的概念驗證研究表明，量子增強型人工智慧具有提升藥物研發品質和速度的巨大潛力，這標誌著電腦輔助藥物發現發展歷程中的一個重要里程碑。

政府主導的醫療保健數位轉型和人工智慧醫院理念

日本政府正在整個醫療保健領域推行全面的數位轉型舉措，大力投資建立人工智慧驅動的醫療基礎設施，以應對人口結構挑戰和勞動力短缺問題。這些舉措源於迫切需要為迅速老化的日本人口提供高品質的醫療保健服務。在日本，約有30%的人口年齡超過65歲，預計未來將出現數十萬名醫療保健專業人員的短缺。政府對醫療保健創新的承諾體現在「社會5.0」理念中，該理念旨在透過整合數位和實體醫療保健領域的技術一體化社會，推動患者照護和醫學研究。日本人工智慧藥物研發市場的成長得益於公私合營，科技公司、製藥公司和學術機構攜手開發人工智慧增強型系統，以支援醫療保健服務以及研發的各個方面。這些系統包括人工智慧輔助藥物研發平台、用於精準醫療的基因組醫學應用、先進的醫學影像解決方案以及旨在簡化臨床工作流程的醫療機器人。建立配備自主系統用於病人管理、診斷支援和治療最佳化的人工智慧專科醫院，體現了政府在醫療衛生現代化方面採取的綜合辦法。

本報告解答的主要問題：

日本人工智慧驅動的藥物研發市場目前發展狀況如何？未來幾年預計又將如何發展？
日本人工智慧藥物研發市場按服務提供者分類的組成是怎樣的？
日本人工智慧藥物研發市場按應用領域分類的組成是怎樣的？
日本人工智慧驅動藥物研發市場的詳細情形（按治療領域分類）是什麼？
日本人工智慧藥物研發市場按最終用戶分類的組成是怎樣的？
日本人工智慧驅動的藥物研發市場按地區分類情況如何？
請您解釋日本人工智慧藥物研發市場價值鏈的各個階段？
日本人工智慧驅動藥物研發市場的主要促進因素和挑戰是什麼？
日本人工智慧驅動藥物研發市場的架構是怎麼樣的？主要企業有哪些？
日本人工智慧藥物研發市場的競爭程度如何？

The Japan AI in drug discovery market size reached USD 131.06 Million in 2025. Looking forward, IMARC Group expects the market to reach USD 796.09 Million by 2034, exhibiting a growth rate (CAGR) of 22.20% during 2026-2034. The market is driven by the advancement of sovereign AI infrastructure enabling pharmaceutical companies, the integration of quantum-AI hybrid technologies accelerating molecular generation and drug-likeness optimization, and government-led digital transformation initiatives establishing AI-powered healthcare systems across the country. Additionally, the expansion of the Japan AI in drug discovery market share is supported by increasing collaborations between pharmaceutical companies and AI technology providers for first-in-class drug development.

Japan AI in Drug Discovery Market Trends:

Sovereign AI Infrastructure Development and Pharmaceutical Company Adoption

Japan is witnessing transformative advancements in AI-powered drug discovery driven by the deployment of sovereign AI infrastructure specifically designed for pharmaceutical research. Leading Japanese pharmaceutical companies including Astellas, Daiichi-Sankyo, and Ono Pharmaceutical are harnessing advanced high-performance computing platforms to build sophisticated AI models for drug discovery applications. These companies utilize specialized platforms that enable drug discovery researchers to develop and deploy AI models for generating biological intelligence from biomolecular data. The infrastructure supports critical computational tasks including protein structure prediction, molecular docking simulations, and the design of novel protein structures optimized to bind with target molecules. The availability of customizable, modular programming frameworks and optimized AI inference capabilities allows pharmaceutical researchers to significantly accelerate the drug discovery timeline while improving the probability of identifying viable therapeutic candidates. The adoption of these technologies represents a strategic shift toward leveraging computational power and advanced algorithms to address complex biological challenges that traditional experimental approaches cannot efficiently resolve.

Quantum-AI Hybrid Technologies Advancing Drug Discovery Capabilities

The integration of quantum computing with artificial intelligence represents a frontier technology advancement that is reshaping drug discovery methodologies in Japan. Pharmaceutical divisions of major Japanese corporations are pioneering the application of quantum-hybrid computational workflows to enhance the generative capabilities of large language models specifically for molecular design and drug candidate identification. These quantum-enhanced AI systems demonstrate superior performance in generating novel molecular structures that exhibit improved drug-like properties compared to molecules generated through classical computational methods alone. The quantum-AI hybrid approach addresses fundamental limitations in classical computing when handling the vast chemical space exploration required for identifying promising drug candidates, offering accelerated computation of complex molecular interactions and more accurate predictions of pharmacological properties. This technological convergence enables researchers to explore broader ranges of molecular properties and activities, thereby expanding the discovery space for small-molecule compounds that meet stringent efficacy and safety criteria. The proof-of-concept work in this domain confirms the potential for quantum-enhanced AI to facilitate both the quality and speed of the drug development process, marking an important milestone in the evolution of computational drug discovery.

Government-Led Healthcare Digital Transformation and AI Hospital Initiatives

The Japanese government is implementing comprehensive digital transformation initiatives across the healthcare sector, with substantial investments directed toward establishing AI-powered healthcare infrastructure that addresses demographic challenges and workforce constraints. These initiatives are driven by the urgent need to provide high-quality medical care to Japan's rapidly aging population, approximately thirty percent of whom are 65 years or older, amid an anticipated shortage of hundreds of thousands of healthcare workers. The government's commitment to healthcare innovation is manifested through its Society 5.0 vision, which envisions a technology-integrated society where digital and physical healthcare realms converge to drive progress in patient care and medical research. The Japan AI in drug discovery market growth is propelled by significant public-private partnerships involving technology companies, pharmaceutical firms, and academic institutions working collaboratively to develop AI-augmented systems supporting various aspects of healthcare delivery and research. These systems include AI-assisted drug discovery platforms, genomic medicine applications for precision therapeutics, advanced medical imaging solutions, and healthcare robotics designed to enhance clinical workflows. The establishment of specialized AI hospitals equipped with autonomous systems for patient management, diagnostic support, and treatment optimization demonstrates the government's holistic approach to healthcare modernization.

Japan AI in Drug Discovery Market Segmentation:

Offering Insights:

Software
Services

Application Insights:

Preclinical Testing
Drug Optimization and Repurposing
Target Identification
Candidate Screening
Others

Therapeutic Area Insights:

Oncology
Neurodegenerative Diseases
Cardiovascular Diseases
Metabolic Diseases
Others

End User Insights:

Pharmaceutical and Biotechnology Companies
Contract Research Organizations (CROs)
Research Centers and Academic Institutes

Regional Insights:

Kanto Region
Kansai/Kinki Region
Central/Chubu Region
Kyushu-Okinawa Region
Tohoku Region
Chugoku Region
Hokkaido Region
Shikoku Region
The report has also provided a comprehensive analysis of all the major regional markets, which include Kanto Region, Kansai/Kinki Region, Central/Chubu Region, Kyushu-Okinawa Region, Tohoku Region, Chugoku Region, Hokkaido Region, and Shikoku Region.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

Key Questions Answered in This Report:

How has the Japan AI in drug discovery market performed so far and how will it perform in the coming years?
What is the breakup of the Japan AI in drug discovery market on the basis of offering?
What is the breakup of the Japan AI in drug discovery market on the basis of application?
What is the breakup of the Japan AI in drug discovery market on the basis of therapeutic area?
What is the breakup of the Japan AI in drug discovery market on the basis of end user?
What is the breakup of the Japan AI in drug discovery market on the basis of region?
What are the various stages in the value chain of the Japan AI in drug discovery market?
What are the key driving factors and challenges in the Japan AI in drug discovery market?
What is the structure of the Japan AI in drug discovery market and who are the key players?
What is the degree of competition in the Japan AI in drug discovery market?

1 Preface

2 Scope and Methodology

2.1 Objectives of the Study
2.2 Stakeholders
2.3 Data Sources
- 2.3.1 Primary Sources
- 2.3.2 Secondary Sources
2.4 Market Estimation
- 2.4.1 Bottom-Up Approach
- 2.4.2 Top-Down Approach
2.5 Forecasting Methodology

3 Executive Summary

4 Japan AI in Drug Discovery Market - Introduction

4.1 Overview
4.2 Market Dynamics
4.3 Industry Trends
4.4 Competitive Intelligence

5 Japan AI in Drug Discovery Market Landscape

5.1 Historical and Current Market Trends (2020-2025)
5.2 Market Forecast (2026-2034)

6 Japan AI in Drug Discovery Market - Breakup by Offering

6.1 Software
- 6.1.1 Overview
- 6.1.2 Historical and Current Market Trends (2020-2025)
- 6.1.3 Market Forecast (2026-2034)
6.2 Services
- 6.2.1 Overview
- 6.2.2 Historical and Current Market Trends (2020-2025)
- 6.2.3 Market Forecast (2026-2034)

7 Japan AI in Drug Discovery Market - Breakup by Application

7.1 Preclinical Testing
- 7.1.1 Overview
- 7.1.2 Historical and Current Market Trends (2020-2025)
- 7.1.3 Market Forecast (2026-2034)
7.2 Drug Optimization and Repurposing
- 7.2.1 Overview
- 7.2.2 Historical and Current Market Trends (2020-2025)
- 7.2.3 Market Forecast (2026-2034)
7.3 Target Identification
- 7.3.1 Overview
- 7.3.2 Historical and Current Market Trends (2020-2025)
- 7.3.3 Market Forecast (2026-2034)
7.4 Candidate Screening
- 7.4.1 Overview
- 7.4.2 Historical and Current Market Trends (2020-2025)
- 7.4.3 Market Forecast (2026-2034)
7.5 Others
- 7.5.1 Historical and Current Market Trends (2020-2025)
- 7.5.2 Market Forecast (2026-2034)

8 Japan AI in Drug Discovery Market - Breakup by Therapeutic Area

8.1 Oncology
- 8.1.1 Overview
- 8.1.2 Historical and Current Market Trends (2020-2025)
- 8.1.3 Market Forecast (2026-2034)
8.2 Neurodegenerative Diseases
- 8.2.1 Overview
- 8.2.2 Historical and Current Market Trends (2020-2025)
- 8.2.3 Market Forecast (2026-2034)
8.3 Cardiovascular Diseases
- 8.3.1 Overview
- 8.3.2 Historical and Current Market Trends (2020-2025)
- 8.3.3 Market Forecast (2026-2034)
8.4 Metabolic Diseases
- 8.4.1 Overview
- 8.4.2 Historical and Current Market Trends (2020-2025)
- 8.4.3 Market Forecast (2026-2034)
8.5 Others
- 8.5.1 Historical and Current Market Trends (2020-2025)
- 8.5.2 Market Forecast (2026-2034)

9 Japan AI in Drug Discovery Market - Breakup by End User

9.1 Pharmaceutical and Biotechnology Companies
- 9.1.1 Overview
- 9.1.2 Historical and Current Market Trends (2020-2025)
- 9.1.3 Market Forecast (2026-2034)
9.2 Contract Research Organizations (CROs)
- 9.2.1 Overview
- 9.2.2 Historical and Current Market Trends (2020-2025)
- 9.2.3 Market Forecast (2026-2034)
9.3 Research Centers and Academic Institutes
- 9.3.1 Overview
- 9.3.2 Historical and Current Market Trends (2020-2025)
- 9.3.3 Market Forecast (2026-2034)

10 Japan AI in Drug Discovery Market - Breakup by Region

10.1 Kanto Region
- 10.1.1 Overview
- 10.1.2 Historical and Current Market Trends (2020-2025)
- 10.1.3 Market Breakup by Offering
- 10.1.4 Market Breakup by Application
- 10.1.5 Market Breakup by Therapeutic Area
- 10.1.6 Market Breakup by End User
- 10.1.7 Key Players
- 10.1.8 Market Forecast (2026-2034)
10.2 Kansai/Kinki Region
- 10.2.1 Overview
- 10.2.2 Historical and Current Market Trends (2020-2025)
- 10.2.3 Market Breakup by Offering
- 10.2.4 Market Breakup by Application
- 10.2.5 Market Breakup by Therapeutic Area
- 10.2.6 Market Breakup by End User
- 10.2.7 Key Players
- 10.2.8 Market Forecast (2026-2034)
10.3 Central/Chubu Region
- 10.3.1 Overview
- 10.3.2 Historical and Current Market Trends (2020-2025)
- 10.3.3 Market Breakup by Offering
- 10.3.4 Market Breakup by Application
- 10.3.5 Market Breakup by Therapeutic Area
- 10.3.6 Market Breakup by End User
- 10.3.7 Key Players
- 10.3.8 Market Forecast (2026-2034)
10.4 Kyushu-Okinawa Region
- 10.4.1 Overview
- 10.4.2 Historical and Current Market Trends (2020-2025)
- 10.4.3 Market Breakup by Offering
- 10.4.4 Market Breakup by Application
- 10.4.5 Market Breakup by Therapeutic Area
- 10.4.6 Market Breakup by End User
- 10.4.7 Key Players
- 10.4.8 Market Forecast (2026-2034)
10.5 Tohoku Region
- 10.5.1 Overview
- 10.5.2 Historical and Current Market Trends (2020-2025)
- 10.5.3 Market Breakup by Offering
- 10.5.4 Market Breakup by Application
- 10.5.5 Market Breakup by Therapeutic Area
- 10.5.6 Market Breakup by End User
- 10.5.7 Key Players
- 10.5.8 Market Forecast (2026-2034)
10.6 Chugoku Region
- 10.6.1 Overview
- 10.6.2 Historical and Current Market Trends (2020-2025)
- 10.6.3 Market Breakup by Offering
- 10.6.4 Market Breakup by Application
- 10.6.5 Market Breakup by Therapeutic Area
- 10.6.6 Market Breakup by End User
- 10.6.7 Key Players
- 10.6.8 Market Forecast (2026-2034)
10.7 Hokkaido Region
- 10.7.1 Overview
- 10.7.2 Historical and Current Market Trends (2020-2025)
- 10.7.3 Market Breakup by Offering
- 10.7.4 Market Breakup by Application
- 10.7.5 Market Breakup by Therapeutic Area
- 10.7.6 Market Breakup by End User
- 10.7.7 Key Players
- 10.7.8 Market Forecast (2026-2034)
10.8 Shikoku Region
- 10.8.1 Overview
- 10.8.2 Historical and Current Market Trends (2020-2025)
- 10.8.3 Market Breakup by Offering
- 10.8.4 Market Breakup by Application
- 10.8.5 Market Breakup by Therapeutic Area
- 10.8.6 Market Breakup by End User
- 10.8.7 Key Players
- 10.8.8 Market Forecast (2026-2034)

11 Japan AI in Drug Discovery Market - Competitive Landscape

11.1 Overview
11.2 Market Structure
11.3 Market Player Positioning
11.4 Top Winning Strategies
11.5 Competitive Dashboard
11.6 Company Evaluation Quadrant

12 Profiles of Key Players

12.1 Company A
- 12.1.1 Business Overview
- 12.1.2 Services Offered
- 12.1.3 Business Strategies
- 12.1.4 SWOT Analysis
- 12.1.5 Major News and Events
12.2 Company B
- 12.2.1 Business Overview
- 12.2.2 Services Offered
- 12.2.3 Business Strategies
- 12.2.4 SWOT Analysis
- 12.2.5 Major News and Events
12.3 Company C
- 12.3.1 Business Overview
- 12.3.2 Services Offered
- 12.3.3 Business Strategies
- 12.3.4 SWOT Analysis
- 12.3.5 Major News and Events
12.4 Company D
- 12.4.1 Business Overview
- 12.4.2 Services Offered
- 12.4.3 Business Strategies
- 12.4.4 SWOT Analysis
- 12.4.5 Major News and Events
12.5 Company E
- 12.5.1 Business Overview
- 12.5.2 Services Offered
- 12.5.3 Business Strategies
- 12.5.4 SWOT Analysis
- 12.5.5 Major News and Events

13 Japan AI in Drug Discovery Market - Industry Analysis

13.1 Drivers, Restraints, and Opportunities
- 13.1.1 Overview
- 13.1.2 Drivers
- 13.1.3 Restraints
- 13.1.4 Opportunities
13.2 Porters Five Forces Analysis
- 13.2.1 Overview
- 13.2.2 Bargaining Power of Buyers
- 13.2.3 Bargaining Power of Suppliers
- 13.2.4 Degree of Competition
- 13.2.5 Threat of New Entrants
- 13.2.6 Threat of Substitutes
13.3 Value Chain Analysis