目次
第1章 創薬におけるグローバル人工知能市場 エグゼクティブサマリー
1.1. 市場規模および予測（2022年～2032年）
1.2. 地域別概要
1.3. 分野別概要
1.3.1. 用途別
1.3.2. 治療分野別
1.4. 主要トレンド
1.5. 不況の影響
1.6. アナリストの推奨事項および結論
第2章 グローバル創薬における人工知能市場の定義と調査仮説
2.1. 調査目的
2.2. 市場定義
2.3. 調査仮説
2.3.1. 対象範囲と除外範囲
2.3.2. 制限事項
2.3.3. 供給サイド分析
2.3.3.1. 供給量
2.3.3.2. インフラ
2.3.3.3. 規制環境
2.3.3.4. 市場競争
2.3.3.5. 経済的実現可能性（消費者視点
2.3.4. 需要側分析
2.3.4.1. 規制枠組み
2.3.4.2. 技術的進歩
2.3.4.3. 環境への配慮
2.3.4.4. 消費者意識と受容
2.4. 予測手法
2.5. 調査対象年
2.6. 通貨換算レート
第3章 グローバル創薬における人工知能市場力学
3.1. 市場推進要因
3.1.1. 新規治療に対する需要の高まり
3.1.2. 戦略的提携およびパートナーシップの拡大
3.1.3. コスト効率とより迅速な医薬品開発プロセス
3.2. 市場の課題
3.2.1. 規制順守と倫理的配慮
3.2.2. データ統合と標準化の問題
3.3. 市場機会
3.3.1. 新興市場におけるAIの採用拡大
3.3.2. データマイニングと機械学習アルゴリズムの進歩
第4章 グローバル創薬における人工知能市場の業界分析
4.1. ポーターのファイブフォースモデル
4.1.1. 供給業者の交渉力
4.1.2. 購入業者の交渉力
4.1.3. 新規参入の脅威
4.1.4. 代替品の脅威
4.1.5. 競争の激しさ
4.1.6. ポーターのファイブフォース影響分析
4.2. PESTEL分析
4.2.1. 政治
4.2.2. 経済
4.2.3. 社会
4.2.4. 技術
4.2.5. 環境
4.2.6. 法律
4.3. トップ投資機会
4.4. トップ勝利戦略
4.5. AI駆動型創薬における破壊的トレンド
4.6. アナリストの推奨事項と結論
第5章 用途別世界の創薬における人工知能市場規模および予測（2022年～2032年）
5.1. セグメントダッシュボード
5.2. 用途別収益分析
5.2.1. 薬の最適化と再目的化
5.2.2. 前臨床試験
5.2.3. その他
第6章 治療分野別世界の創薬AI市場規模・予測（2022年～2032年）
6.1. セグメントダッシュボード
6.2. 治療分野別収益分析
6.2.1. 腫瘍学
6.2.2. 神経変性疾患
6.2.3. 心血管疾患
6.2.4. 代謝性疾患
6.2.5. 感染症
6.2.6. その他
第7章 グローバル創薬における人工知能市場規模・予測（2022年～2032年）地域別
7.1. 北米
7.1.1. 米国
7.1.2. カナダ
7.1.3. メキシコ
7.2. ヨーロッパ
7.2.1. 英国
7.2.2. ドイツ
7.2.3. フランス
7.2.4. イタリア
7.2.5. スペイン
7.2.6. デンマーク
7.2.7. スウェーデン
7.2.8. ノルウェー
7.3. アジア太平洋地域
7.3.1. 日本
7.3.2. 中国
7.3.3. インド
7.3.4. 韓国
7.3.5. オーストラリア
7.4. ラテンアメリカ
7.4.1. ブラジル
7.4.2. アルゼンチン
7.5. 中東およびアフリカ
7.5.1. 南アフリカ
7.5.2. サウジアラビア
7.5.3. アラブ首長国連邦
7.5.4. クウェート
第8章 競合情報
8.1. 主要企業のSWOT分析
8.1.1. IBM
8.1.2. Exscientia
8.1.3. Insilico Medicine
8.2. トップ市場戦略
8.3. 企業プロフィール
IBM
Exscientia
Insilico Medicine
Google (DeepMind)
BenevolentAI
Atomwise Inc.
Berg Health (acquired by BPGbio Inc.)
BioSymetrics, Inc.
insitro
GNS Healthcare (rebranded as Aitia)
CYCLICA (acquired by Recursion)
Cloud Pharmaceuticals
BioAge Labs
Merck & Co.
Fujitsu
第9章 研究プロセス
9.1. 研究プロセス
9.1.1. データマイニング
9.1.2. 分析
9.1.3. 市場推定
9.1.4. 検証
9.1.5. 出版
9.2. 研究特性

The Global Artificial Intelligence (AI) in Drug Discovery Market was valued at approximately USD 1.6 billion in 2023 and is projected to expand at a robust CAGR of 29.7% during the forecast period 2024-2032.

This burgeoning growth is attributed to the increasing adoption of AI technologies, including machine learning and deep learning, across various phases of drug discovery, from initial compound screening to clinical trials. The rising need for innovative drug therapies and the integration of advanced analytics in preclinical testing processes drive the market's expansion. Furthermore, a surge in strategic collaborations between AI startups and pharmaceutical companies is reshaping the drug discovery landscape, optimizing processes, and reducing developmental timelines.

The digitalization of biomedical and clinical research is further propelling the implementation of AI-powered solutions. Large datasets generated during molecule screening and preclinical studies demand sophisticated tools for accurate analysis, making AI indispensable for researchers. Advanced machine learning algorithms not only enhance the precision of molecule binding predictions but also reduce errors, fostering significant cost efficiencies. Notably, government initiatives in emerging and developed economies are accelerating the penetration of AI technologies, enabling streamlined regulatory processes and fostering innovation.

Among applications, Drug Optimization and Repurposing leads the market, contributing the highest share of 53.7% in 2023. This dominance underscores the efficiency of AI in refining existing drug candidates and identifying novel therapeutic uses, thereby addressing unmet medical needs while maximizing investment returns. Meanwhile, the Preclinical Testing segment exhibits the fastest growth, with AI’s ability to optimize testing protocols, predict drug toxicity, and model biological interactions significantly enhancing its appeal to pharmaceutical companies.

Regionally, North America commands the largest market share at 57.7%, driven by substantial investments in healthcare technologies and a favorable regulatory landscape. The region’s robust research infrastructure and collaboration between technology giants and pharmaceutical companies amplify the adoption of AI in drug discovery. Simultaneously, Asia Pacific emerges as the fastest-growing region, fueled by advancements in AI applications across countries like China, Japan, and India. These nations prioritize AI integration to improve clinical trial efficiency and address complex healthcare challenges.

The industry is witnessing an influx of mergers, acquisitions, and strategic partnerships aimed at advancing AI capabilities in drug discovery. For instance, BioNTech's acquisition of InstaDeep highlights the industry's focus on leveraging AI for immunotherapy innovations. However, stringent regulations and ethical considerations surrounding AI applications pose challenges, emphasizing the importance of compliance with international standards to sustain market growth.
Major players shaping this market include IBM, Exscientia, Google (DeepMind), and Insilico Medicine, among others. These companies are continually driving innovation, underlining the transformative potential of AI in revolutionizing drug discovery processes.

Key Players Included in This Report:
• IBM
• Exscientia
• Insilico Medicine
• Google (DeepMind)
• BenevolentAI
• Atomwise Inc.
• Berg Health (acquired by BPGbio Inc.)
• BioSymetrics, Inc.
• insitro
• GNS Healthcare (rebranded as Aitia)
• CYCLICA (acquired by Recursion)
• Cloud Pharmaceuticals
• BioAge Labs
• Merck & Co.
• Fujitsu

The detailed segments and sub-segment of the market are explained below:
By Application:
• Drug Optimization and Repurposing
• Preclinical Testing
• Others

By Therapeutic Area:
• Oncology
• Neurodegenerative Diseases
• Cardiovascular Diseases
• Metabolic Diseases
• Infectious Diseases
• Others

By Region:
North America
• U.S.
• Canada
• Mexico
Europe
• U.K.
• Germany
• France
• Italy
• Spain
• Denmark
• Sweden
• Norway
Asia Pacific
• Japan
• China
• India
• South Korea
• Australia
Latin America
• Brazil
• Argentina
Middle East & Africa
• South Africa
• Saudi Arabia
• UAE
• Kuwait

Years considered for the study are as follows:
• Historical year – 2022
• Base year – 2023
• Forecast period – 2024 to 2032

Key Takeaways:
• Market Estimates & Forecast for 10 years from 2022 to 2032.
• Regional and segment-level analysis.
• Comprehensive competitive landscape and key player strategies.
• Supply-side and demand-side analysis of the market.

Table of Contents
Chapter 1. Global Artificial Intelligence in Drug Discovery Market Executive Summary
1.1. Market Size & Forecast (2022-2032)
1.2. Regional Summary
1.3. Segmental Summary
1.3.1. By Application
1.3.2. By Therapeutic Area
1.4. Key Trends
1.5. Recession Impact
1.6. Analyst Recommendations & Conclusion
Chapter 2. Global Artificial Intelligence in Drug Discovery Market Definition and Research Assumptions
2.1. Research Objective
2.2. Market Definition
2.3. Research Assumptions
2.3.1. Inclusion & Exclusion
2.3.2. Limitations
2.3.3. Supply Side Analysis
2.3.3.1. Availability
2.3.3.2. Infrastructure
2.3.3.3. Regulatory Environment
2.3.3.4. Market Competition
2.3.3.5. Economic Viability (Consumer’s Perspective)
2.3.4. Demand Side Analysis
2.3.4.1. Regulatory Frameworks
2.3.4.2. Technological Advancements
2.3.4.3. Environmental Considerations
2.3.4.4. Consumer Awareness & Acceptance
2.4. Estimation Methodology
2.5. Years Considered for the Study
2.6. Currency Conversion Rates
Chapter 3. Global Artificial Intelligence in Drug Discovery Market Dynamics
3.1. Market Drivers
3.1.1. Rising Demand for Novel Therapies
3.1.2. Growing Strategic Collaborations and Partnerships
3.1.3. Cost Efficiency and Faster Drug Development Processes
3.2. Market Challenges
3.2.1. Regulatory Compliance and Ethical Considerations
3.2.2. Data Integration and Standardization Issues
3.3. Market Opportunities
3.3.1. Increasing AI Adoption in Emerging Markets
3.3.2. Advances in Data Mining and Machine Learning Algorithms
Chapter 4. Global Artificial Intelligence in Drug Discovery Market Industry Analysis
4.1. Porter’s Five Forces Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Porter’s Five Forces Impact Analysis
4.2. PESTEL Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.2.5. Environmental
4.2.6. Legal
4.3. Top Investment Opportunities
4.4. Top Winning Strategies
4.5. Disruptive Trends in AI-Powered Drug Discovery
4.6. Analyst Recommendations & Conclusion
Chapter 5. Global Artificial Intelligence in Drug Discovery Market Size & Forecast by Application (2022-2032)
5.1. Segment Dashboard
5.2. Revenue Analysis by Application
5.2.1. Drug Optimization and Repurposing
5.2.2. Preclinical Testing
5.2.3. Others
Chapter 6. Global Artificial Intelligence in Drug Discovery Market Size & Forecast by Therapeutic Area (2022-2032)
6.1. Segment Dashboard
6.2. Revenue Analysis by Therapeutic Area
6.2.1. Oncology
6.2.2. Neurodegenerative Diseases
6.2.3. Cardiovascular Diseases
6.2.4. Metabolic Diseases
6.2.5. Infectious Diseases
6.2.6. Others
Chapter 7. Global Artificial Intelligence in Drug Discovery Market Size & Forecast by Region (2022-2032)
7.1. North America
7.1.1. U.S.
7.1.2. Canada
7.1.3. Mexico
7.2. Europe
7.2.1. U.K.
7.2.2. Germany
7.2.3. France
7.2.4. Italy
7.2.5. Spain
7.2.6. Denmark
7.2.7. Sweden
7.2.8. Norway
7.3. Asia Pacific
7.3.1. Japan
7.3.2. China
7.3.3. India
7.3.4. South Korea
7.3.5. Australia
7.4. Latin America
7.4.1. Brazil
7.4.2. Argentina
7.5. Middle East & Africa
7.5.1. South Africa
7.5.2. Saudi Arabia
7.5.3. UAE
7.5.4. Kuwait
Chapter 8. Competitive Intelligence
8.1. Key Company SWOT Analysis
8.1.1. IBM
8.1.2. Exscientia
8.1.3. Insilico Medicine
8.2. Top Market Strategies
8.3. Company Profiles
8.3.1. IBM
8.3.1.1. Key Information
8.3.1.2. Overview
8.3.1.3. Financial (Subject to Data Availability)
8.3.1.4. Product Summary
8.3.1.5. Market Strategies
8.3.2. Exscientia
8.3.3. Insilico Medicine
Chapter 9. Research Process
9.1. Research Process
9.1.1. Data Mining
9.1.2. Analysis
9.1.3. Market Estimation
9.1.4. Validation
9.1.5. Publishing
9.2. Research Attributes

❖ 掲載企業 ❖

IBM、Exscientia、Insilico Medicine、Google (DeepMind)、BenevolentAI、Atomwise Inc.、Berg Health (acquired by BPGbio Inc.)、BioSymetrics, Inc.、insitro、GNS Healthcare (rebranded as Aitia)、CYCLICA (acquired by Recursion)、Cloud Pharmaceuticals、BioAge Labs、Merck & Co.、Fujitsuなど

❖ 免責事項 ❖
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