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1954421

日本人工智慧药物发现市场规模、市场份额、趋势和预测：按服务提供、应用、治疗领域、最终用户和地区划分（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个工作天内

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简介目录

2025年，日本人工智慧驱动药物研发市场规模达1.3106亿美元。展望未来，IMARC集团预测，到2034年，该市场规模将达到7.9609亿美元，2026年至2034年的复合年增长率（CAGR）为22.20%。这一市场成长要素以下因素：支持製药公司的自主人工智慧基础设施的进步；量子人工智慧混合技术的融合加速了分子生成和药物适用性优化；以及政府主导的数位转型倡议推动了全国人工智慧医疗体系的建设。此外，製药公司与人工智慧技术供应商之间为研发First-in-Class药物而加强的合作，也促进了日本人工智慧在药物研发领域市场份额的不断扩大。

日本人工智慧驱动药物研发市场的发展趋势：

自主人工智慧基础设施的开发及其在製药公司中的应用

在日本，专为药物研发设计的自主人工智慧基础设施的引进，正推动人工智慧驱动的药物发现领域取得变革性进展。Astellas製药、第一三共製药和小野药品工业株式会社等日本领先製药公司正利用先进的高效能运算平台建构复杂的人工智慧模型，用于药物发现。这些公司利用专用平台，使药物研发人员能够开发和部署人工智慧模型，从而从生物分子数据中获取生物学见解。此基础设施支援关键的运算任务，例如蛋白质结构预测、分子对接模拟以及设计针对目标分子结合优化的新型蛋白质结构。可客製化的模组化程式框架和优化的人工智慧推理能力，使药物研究人员能够显着缩短药物发现週期，同时提高发现有前景的候选药物的机率。这些技术的应用标誌着一种策略转变，即利用运算能力和先进演算法来应对传统实验方法难以有效解决的复杂生物学挑战。

量子和人工智慧混合技术将提升药物发现能力

量子运算与人工智慧的融合代表着最尖端科技创新，它正在重塑日本的药物研发方法。主要企业的製药部门正率先应用量子混合计算工作流程，以增强其生成大规模语言模型的能力，这些模型专门用于分子设计和候选药物的筛选。与仅使用传统运算方法产生的分子相比，这些量子增强型人工智慧系统在产生具有更优异类药特性的新型分子结构方面表现出卓越的性能。量子人工智慧混合方法克服了传统计算在探索庞大化学空间方面的根本局限性，而化学空间对于筛选有前景的概念验证研究表明，量子增强型人工智慧具有提升药物研发品质和速度的巨大潜力，这标誌着电脑辅助药物发现发展历程中的一个重要里程碑。

政府主导的医疗保健数位转型和人工智慧医院理念

日本政府正在整个医疗保健领域推行全面的数位转型倡议，大力投资建立人工智慧驱动的医疗基础设施，以应对人口结构挑战和劳动力短缺问题。这些倡议源于迫切需要为迅速老化的日本人口提供高品质的医疗保健服务。在日本，约有30%的人口年龄超过65岁，预计未来将出现数十万名医疗保健专业人员的短缺。政府对医疗保健创新的承诺体现在「社会5.0」理念中，该理念旨在透过整合数位和实体医疗保健领域的技术一体化社会，推动患者照护和医学研究。日本人工智慧药物研发市场的成长得益于公私合营，科技公司、製药公司和学术机构携手开发人工智慧增强型系统，以支援医疗保健服务以及研发的各个方面。这些系统包括人工智慧辅助药物研发平台、用于精准医疗的基因组医学应用、先进的医学影像解决方案以及旨在简化临床工作流程的医疗机器人。建立配备自主系统用于病人管理、诊断支援和治疗优化的人工智慧专科医院，体现了政府在医疗卫生现代化方面采取的综合办法。

本报告解答的主要问题：

日本人工智慧驱动的药物研发市场目前发展状况如何？未来几年预计又将如何发展？
日本人工智慧药物研发市场按服务提供者分類的组成是怎样的？
日本人工智慧药物研发市场按应用领域分類的组成是怎样的？
日本人工智慧驱动药物研发市场的细分情况（按治疗领域划分）是什么？
日本人工智慧药物研发市场按最终用户分類的组成是怎样的？
日本人工智慧驱动的药物研发市场按地区划分情况如何？
请您解释日本人工智慧药物研发市场价值链的各个阶段？
日本人工智慧驱动药物研发市场的主要驱动因素和挑战是什么？
日本人工智慧驱动药物研发市场的架构是怎么样的？主要企业有哪些？
日本人工智慧药物研发市场的竞争程度如何？

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