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創薬および薬剤開発におけるAIの世界市場：2025年～2033年

Global AI in Drug Discovery and Development Market - 2025-2033

出版日
ページ情報
英文 180 Pages
納期
即日から翌営業日
カスタマイズ可能
適宜更新あり
価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=143.57円
創薬および薬剤開発におけるAIの世界市場：2025年～2033年
出版日: 2025年04月22日
発行: DataM Intelligence
ページ情報: 英文 180 Pages
納期: 即日から翌営業日
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概要

世界の創薬および薬剤開発におけるAIの市場規模は、2024年に62億4,000万米ドルとなりました。同市場は、2032年には340億5,000万米ドルに達し、2025年～2033年の予測期間中のCAGRは18.5％となると予測されています。

創薬および薬剤開発におけるAIは、機械学習、ディープラーニング、自然言語処理、データ分析などの技術を利用して、新薬の発見、設計、開発のプロセスをスピードアップします。AIは、ゲノミクス、プロテオミクス、臨床試験から得られる大規模なデータセットを分析することで、潜在的な標的の特定、分子間相互作用の予測、化合物選択の最適化、転帰の予測を従来の手法よりも効率的に支援し、製薬業界を変革して治療法の発見をより迅速かつ正確にします。

世界の創薬および薬剤開発におけるAI市場は、複雑な生物学的データの解析、創薬標的の特定、化合物の有効性と毒性の予測などの能力により、勢いを増しています。この技術は、従来の医薬品開発における時間とコストを削減します。製薬企業や研究機関は、機械学習アルゴリズムやディープラーニングツールを使用して候補化合物のスクリーニングを合理化し、臨床試験を最適化し、意思決定を強化することで、新薬の市場投入までの時間を短縮しています。

さらにAIは、治療結果の予測、臨床試験のデザイン、薬剤のリポジショニングを可能にすることで、臨床試験の効率を向上させる。しかし、強固なデータ共有の仕組みやアルゴリズムの包括的な知的財産保護などが課題となっています。AIを活用する製薬企業は、生物科学とアルゴリズムを効果的に統合し、ウェットラボとドライラボの実験の融合を成功させなければなりません。

世界の創薬および薬剤開発におけるAI市場は、FDAや市場情勢などの機関がAI駆動ツールのガイドラインを策定するなど、規制状況が進化していることによる課題に直面しています。データの取り扱い、モデルの検証、アルゴリズムの透明性に関する標準化されたプロトコルがないため、さらなるコンプライアンスの負担が生じる。データのプライバシー、倫理的配慮、AIの意思決定における説明可能性などに関する懸念も、こうした不確実性に拍車をかけています。こうした不確実性は、製品の発売を遅らせたり、中小企業がAI技術を採用する意欲を削いだりして、市場の成長を制限する可能性があります。

当レポートでは、世界の創薬および薬剤開発におけるAI市場について調査し、市場の概要とともに、技術別、用途別、地域別動向、競合情勢、および市場に参入する企業のプロファイルなどを提供しています。

目次

第1章 市場のイントロダクションと範囲

第2章 経営者の洞察と重要なポイント

第3章 市場力学

  • 影響要因
    • 促進要因
    • 抑制要因
    • 機会
    • 影響分析

第4章 戦略的洞察と業界展望

  • 規制分析
  • 市場のリーダーとパイオニア
  • CXOの視点
  • 最新の開発とブレークスルー
  • 規制と償還の情勢
  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 特許分析
  • SWOT分析
  • アンメットニーズとギャップ
  • 市場参入と拡大のための推奨戦略
  • シナリオ分析ベストケース、ベースケース、ワーストケースの予測
  • 価格分析と価格動向
  • キーオピニオンリーダー

第5章 創薬開発市場におけるAI（技術別）

  • 機械学習
  • 自然言語処理
  • 生成AI
  • その他

第6章 医薬品の発見と開発市場におけるAI（用途別）

  • ターゲット同定と検証
  • ヒット同定とバーチャルスクリーニング
  • ヒット・トゥ・リード
  • リード最適化
  • 前臨床試験
  • 臨床試験
  • その他

第7章 創薬開発市場におけるAI（地域別）

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • スペイン
    • イタリア
    • その他
  • 南米
    • ブラジル
    • アルゼンチン
    • その他
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • 韓国
    • その他
  • 中東・アフリカ

第8章 競合情勢と市場ポジショニング

  • 競合状況の概要と主要市場参入企業
  • 市場シェア分析とポジショニングマトリックス
  • 戦略的パートナーシップ、合併・買収
  • 製品ポートフォリオとイノベーションにおける主な発展
  • 企業ベンチマーク

第9章 企業プロファイル

  • Alphabet（Google DeepMind）
    • 会社概要
    • 製品ポートフォリオ
    • 財務概要
    • 主な発展
    • SWOT分析
  • Atomwise Inc.
  • BenevolentAI
  • BioMap
  • BioSymetrics
  • DEEP GENOMICS.
  • Euretos.
  • Exscientia
  • IBM
  • Iktos.

第10章 付録

目次
Product Code: HCIT9508

The global AI in drug discovery and development market reached US$ 6.24 billion in 2024 and is expected to reach US$ 34.05 billion by 2032, growing at a CAGR of 18.5% during the forecast period 2025-2033.

AI in drug discovery and development uses technologies like machine learning, deep learning, natural language processing, and data analytics to speed up the process of discovering, designing, and developing new drugs. By analyzing large datasets from genomics, proteomics, and clinical trials, AI helps identify potential targets, predict molecular interactions, optimize compound selection, and forecast outcomes more efficiently than traditional methods, transforming the pharmaceutical industry and making therapy discovery faster and more precise.

Market Dynamics: Drivers & Restraints

Increasing Adoption of Artificial Intelligence for Faster Drug Development

The global AI in drug discovery and development market is gaining momentum due to its ability to analyze complex biological data, identify drug targets, and predict compound efficacy and toxicity. This technology reduces time and cost in traditional drug development. Pharmaceutical companies and research institutions are using machine learning algorithms and deep learning tools to streamline candidate screening, optimize clinical trials, and enhance decision-making, leading to faster time-to-market for new drugs.

Additionally, AI improves clinical trial efficiency by predicting outcomes, designing trials, and enabling drug repositioning. However, challenges include robust data-sharing mechanisms and comprehensive intellectual property protections for algorithms. AI-driven pharmaceutical companies must integrate biological sciences and algorithms effectively, ensuring the successful fusion of wet and dry laboratory experiments.

Regulatory Challenges in AI Integration

The global AI in drug discovery and development market faces challenges due to the evolving regulatory landscape, with bodies like the FDA and EMA developing guidelines for AI-driven tools. The lack of standardized protocols for data handling, model validation, and algorithm transparency creates additional compliance burdens. Concerns around data privacy, ethical considerations, and explainability in AI decisions also add to these uncertainties. These uncertainties can delay product launches and discourage smaller players from adopting AI technologies, limiting market growth.

Segment Analysis

The global AI in drug discovery and development market is segmented based on technology, application, and region.

Technology

Machine learning in the technology segment is expected to grow with the highest CAGR in the forecast period.

Machine learning is a subfield of artificial intelligence that enables machines to imitate intelligent human behavior. AI systems are used to perform complex tasks similar to human problem-solving. The goal of AI is to create computer models that exhibit "intelligent behaviors" like humans, such as recognizing visual scenes, understanding natural language, or performing physical actions. Boris Katz, a principal research scientist at CSAIL, emphasizes this goal.

Machine learning is a key driver of global AI in the drug discovery and development market. It enables faster and more accurate analysis of complex biological data, reducing time and cost. It helps identify and validate drug targets by recognizing patterns in large datasets, aiding in early drug development stages. ML also streamlines compound screening and lead optimization by predicting drug efficacy and toxicity, improving success rates. It also supports efficient clinical trial design through patient stratification and real-time data analysis.

For instance, in April 2024, Aurigene Pharmaceutical Services Limited, a Dr. Reddy's Laboratories company, introduced Aurigene.AI, an AI and ML-assisted platform designed to expedite drug discovery projects from hit identification to candidate nomination.

Geographical Analysis

Asia-Pacific is expected to hold a significant position in the AI drug discovery and development market with the highest market share

The market growth in the Asia-Pacific region is contributed to by various factors such as rising pharmaceutical innovations, increasing investments by pharmaceutical and biopharmaceutical companies in drug discovery and development activities, etc.

For instance, in the Asia-Pacific region, Japan has the strongest pharmaceutical industry, supported by constant and advanced R&D activities, novel innovations, etc. Many pharmaceutical companies operate globally and stand among industry giants. These companies invest heavily in drug discovery and development activities and are forming strategic alliances with AI technology leaders. These collaborative initiatives are the major market drivers of the Japanese market.

For instance, in February 2024, Ono Pharmaceutical Co., Ltd. announced a research collaboration with InveniAI LLC to identify novel therapeutic targets by leveraging InveniAI's cutting-edge artificial intelligence (AI) and machine learning (ML) platforms AlphaMeld and ChatAlphaMeld.

Moreover, in February 2024, Atinary Technologies Inc. announced a partnership with Takeda, one of the largest pharmaceutical manufacturers in Japan and globally. Through this partnership, Takeda will leverage Atinary's leading AI Self-Driving Labs technology, combined with its expertise in R&D and drug discovery.

In addition, several leading technology leaders are establishing their footprint in the Asia-Pacific region, especially in Japan, which will create opportunities for Japanese manufacturers to leverage these advanced AI technologies in their drug discovery activities.

Competitive Landscape

The major global players in the AI in drug discovery market are Alphabet (Google DeepMind), Atomwise Inc., BenevolentAI, BioMap, BioSymetrics, DEEP Genomics, Euretos, Exscientia, IBM, and Iktos. Among others.

Key Developments

  • In January 2025, InveniAI LLC, has announced its recent key milestones as a part of its commitment to revolutionize the drug development process. InveniAI has launched its fully owned subsidiary AlphaMeld Corporation, which specializes in artificial intelligence (AI), generative AI, and machine learning technologies for the development of novel therapies.
  • In July 2024, Exscientia plc announced the expansion of its collaboration with Amazon Web Services (AWS) to utilize the cloud provider's artificial intelligence (AI) and machine learning (ML) services for powering Exscientia's platform for end-to-end drug discovery and automation. The platform uses generative AI models and AWS's scalability and robotic lab automation to design drug candidates quickly and at low cost.

Why Purchase the Report?

  • Pipeline & Innovations: Reviews ongoing clinical trials, product pipelines, and forecasts upcoming pharmaceutical advancements.
  • Technology Performance & Market Positioning: Analyzes product performance, market positioning, and growth potential to optimize strategies.
  • Real-World Evidence: Integrates patient feedback and data into product development for improved outcomes.
  • Physician Preferences & Health System Impact: Examines healthcare provider behaviors and the impact of health system mergers on adoption strategies.
  • Market Updates & Industry Changes: Covers recent regulatory changes, new policies, and emerging technologies.
  • Competitive Strategies: Analyzes competitor strategies, market share, and emerging players.
  • Pricing & Market Access: Reviews pricing models, reimbursement trends, and market access strategies.
  • Market Entry & Expansion: Identifies optimal strategies for entering new markets and partnerships.
  • Regional Growth & Investment: Highlights high-growth regions and investment opportunities.
  • Supply Chain Optimization: Assesses supply chain risks and distribution strategies for efficient Technology delivery.
  • Sustainability & Regulatory Impact: Focuses on eco-friendly practices and evolving regulations in healthcare.
  • Post-market Surveillance: Uses post-market data to enhance product safety and access.
  • Pharmacoeconomics & Value-Based Pricing: Analyzes the shift to value-based pricing and data-driven decision-making in R&D.

The global AI in drug discovery and development market report would provide approximately 54 tables, 47 figures, and 180 pages.

Technology Audience 2023

  • Manufacturers: Pharmaceutical, Biotech Companies, Contract Manufacturers, Distributors, Hospitals.
  • Regulatory & Policy: Compliance Officers, Government, Health Economists, Market Access Specialists.
  • Technology & Innovation: R&D Professionals, Clinical Trial Managers, Pharmacovigilance Experts.
  • Investors: Healthcare Investors, Venture Fund Investors, Pharma Marketing & Sales.
  • Consulting & Advisory: Healthcare Consultants, Industry Associations, Analysts.
  • Supply Chain: Distribution and Supply Chain Managers.
  • Consumers & Advocacy: Patients, Advocacy Groups, Insurance Companies.
  • Academic & Research: Academic Institutions.

Table of Contents

1. Market Introduction and Scope

  • 1.1. Objectives of the Report
  • 1.2. Report Coverage & Definitions
  • 1.3. Report Scope

2. Executive Insights and Key Takeaways

  • 2.1. Market Highlights and Strategic Takeaways
  • 2.2. Key Trends and Future Projections
  • 2.3. Snippet by Technology
  • 2.4. Snippet by Application
  • 2.5. Snippet by Region

3. Dynamics

  • 3.1. Impacting Factors
    • 3.1.1. Drivers
      • 3.1.1.1. Increasing Adoption of Artificial Intelligence for Faster Drug Development
      • 3.1.1.2. Rise in Technological Advancements
    • 3.1.2. Restraints
      • 3.1.2.1. Regulatory Challenges
      • 3.1.2.2. High Cost associated with AI integration
    • 3.1.3. Opportunities
      • 3.1.3.1. Expansion in Emerging Markets
    • 3.1.4. Impact Analysis

4. Strategic Insights and Industry Outlook

  • 4.1. Regulatory Analysis
  • 4.2. Market Leaders and Pioneers
    • 4.2.1. Emerging Pioneers and Prominent Players
    • 4.2.2. Established leaders with the largest-selling Brand
    • 4.2.3. Market leaders with established Product
  • 4.3. CXO Perspectives
  • 4.4. Latest Developments and Breakthroughs
  • 4.5. Regulatory and Reimbursement Landscape
    • 4.5.1. North America
    • 4.5.2. Europe
    • 4.5.3. Asia Pacific
    • 4.5.4. South America
    • 4.5.5. Middle East & Africa
  • 4.6. Porter's Five Forces Analysis
  • 4.7. Supply Chain Analysis
  • 4.8. Patent Analysis
  • 4.9. SWOT Analysis
  • 4.10. Unmet Needs and Gaps
  • 4.11. Recommended Strategies for Market Entry and Expansion
  • 4.12. Scenario Analysis: Best-Case, Base-Case, and Worst-Case Forecasts
  • 4.13. Pricing Analysis and Price Dynamics
  • 4.14. Key Opinion Leaders

5. AI in Drug Discovery and Development Market, By Technology

  • 5.1. Introduction
    • 5.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 5.1.2. Market Attractiveness Index, By Technology
  • 5.2. Machine Learning*
    • 5.2.1. Introduction
    • 5.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 5.3. Natural Language Processing
  • 5.4. Generative AI
  • 5.5. Others

6. AI in Drug Discovery and Development Market, By Application

  • 6.1. Introduction
    • 6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 6.1.2. Market Attractiveness Index, By Application
  • 6.2. Target Discovery & Validation*
    • 6.2.1. Introduction
    • 6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 6.3. Hit Discovery & Virtual Screening
  • 6.4. Hit-to-Lead
  • 6.5. Lead Optimization
  • 6.6. Pre-Clinical Testing
  • 6.7. Clinical Trials
  • 6.8. Others

7. AI in Drug Discovery and Development Market, By Region

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 7.1.2. Market Attractiveness Index, By Region
  • 7.2. North America
    • 7.2.1. Introduction
    • 7.2.2. Key Region-Specific Dynamics
    • 7.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 7.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 7.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 7.2.5.1. U.S.
      • 7.2.5.2. Canada
      • 7.2.5.3. Mexico
  • 7.3. Europe
    • 7.3.1. Introduction
    • 7.3.2. Key Region-Specific Dynamics
    • 7.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 7.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 7.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 7.3.5.1. Germany
      • 7.3.5.2. U.K.
      • 7.3.5.3. France
      • 7.3.5.4. Spain
      • 7.3.5.5. Italy
      • 7.3.5.6. Rest of Europe
  • 7.4. South America
    • 7.4.1. Introduction
    • 7.4.2. Key Region-Specific Dynamics
    • 7.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 7.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 7.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 7.4.5.1. Brazil
      • 7.4.5.2. Argentina
      • 7.4.5.3. Rest of South America
  • 7.5. Asia-Pacific
    • 7.5.1. Introduction
    • 7.5.2. Key Region-Specific Dynamics
    • 7.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 7.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 7.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 7.5.5.1. China
      • 7.5.5.2. India
      • 7.5.5.3. Japan
      • 7.5.5.4. South Korea
      • 7.5.5.5. Rest of Asia-Pacific
  • 7.6. Middle East and Africa
    • 7.6.1. Introduction
    • 7.6.2. Key Region-Specific Dynamics
    • 7.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
    • 7.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application

8. Competitive Landscape and Market Positioning

  • 8.1. Competitive Overview and Key Market Players
  • 8.2. Market Share Analysis and Positioning Matrix
  • 8.3. Strategic Partnerships, Mergers & Acquisitions
  • 8.4. Key Developments in Product Portfolios and Innovations
  • 8.5. Company Benchmarking

9. Company Profiles

  • 9.1. Alphabet (Google DeepMind) *
    • 9.1.1. Company Overview
    • 9.1.2. Product Portfolio
      • 9.1.2.1. Product Description
      • 9.1.2.2. Product Key Performance Indicators (KPIs)
      • 9.1.2.3. Historic and Forecasted Product Sales
      • 9.1.2.4. Product Sales Volume
    • 9.1.3. Financial Overview
      • 9.1.3.1. Company Revenue
      • 9.1.3.2. Geographical Revenue Shares
      • 9.1.3.3. Revenue Forecasts
    • 9.1.4. Key Developments
      • 9.1.4.1. Mergers & Acquisitions
      • 9.1.4.2. Key Product Development Activities
      • 9.1.4.3. Regulatory Approvals, etc.
    • 9.1.5. SWOT Analysis
  • 9.2. Atomwise Inc.
  • 9.3. BenevolentAI
  • 9.4. BioMap
  • 9.5. BioSymetrics
  • 9.6. DEEP GENOMICS.
  • 9.7. Euretos.
  • 9.8. Exscientia
  • 9.9. IBM
  • 9.10. Iktos.

LIST NOT EXHAUSTIVE

10. Appendix

  • 10.1. About Us and Services
  • 10.2. Contact Us