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2094034

全球人工智慧蛋白質設計市場：按產品、技術、應用、部署和最終用戶分類－市場規模、產業動態、機會分析和預測（2026-2035 年）

Global AI Protein Design Market By Offering, By Technology, By Application, By Deployment, By End User - Market Size, Industry Dynamics, Opportunity Analysis and Forecast For 2026-2035
出版日期: | 出版商: Astute Analytica | 英文 280 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

隨著人工智慧、計算生物學和生物技術的進步不斷改變新蛋白質和治療分子的發現和開發方式，人工智慧蛋白質設計市場正經歷快速成長。預計到2025年，該市場規模將達到約15億美元，並預計在2035年成長至約124億美元。這意味著在2026年至2035年的預測期內，其複合年成長率將達到23.2%。

市場成長的主要驅動力是對個人化和精準醫療日益成長的需求。醫療機構和製藥公司正在尋求更具針對性的方法，透過開發客製化生技藥品、工程蛋白和先進療法來滿足患者的個別需求。人工智慧蛋白質設計平台可以幫助研究人員分析複雜的生物數據，預測分子行為，並創建具有更高活性、特異性和穩定性的最佳化蛋白質候選物。

顯著的市場趨勢

人工智慧蛋白質設計市場由眾多引領運算生物學、生成式人工智慧和分子工程尖端技術的公司組成。谷歌DeepMind及其專注於生物技術的子公司Isomorphic Labs，仍然是人工智慧蛋白質設計領域最具影響力的公司之一。

Biomedicines憑藉其專注於生成式蛋白質設計和新型生物分子創建的「Chroma」平台，已確立了強大的市場地位。 EvolutionaryScale則憑藉其「ESM」系列（包括ESM3），成為將大規模語言建模（LLM）方法應用於生物學領域的先驅。

Xaira Therapeutics專注於將先進的基於人工智慧的蛋白質工程技術商業化，包括源自結構生成人工智慧方法（例如RFdiffusion）的技術。 Profluent融合了人工智慧蛋白質設計和基因組工程。該公司將大規模語言建模技術應用於生物序列，旨在創建自然界中不存在的新型遺傳和分子系統。

主要成長要素

隨著越來越多的機構尋求能夠提高研發流程的準確性、速度和可擴展性的先進生物分子工程能力，市場正在不斷擴大。傳統方法通常依賴大規模實驗檢驗來了解生物分子的行為，這既耗時又昂貴，而且資源消耗巨大。隨著對更高效的藥物發現工作流程的需求不斷成長，人們對能夠預測分子特性、最佳化設計並在大規模實驗室檢驗之前輔助決策的人工智慧驅動平台越來越感興趣。隨著製藥、生物技術、農業和材料科學等行業採用計算方法，對可擴展且精確的生物分子工程解決方案的需求持續成長。

新機會的趨勢

隨著工業企業日益尋求先進的生物解決方案來應對廢棄物管理、塑膠回收再利用和紡織品製造等領域的關鍵永續性挑戰，人工智慧蛋白質設計市場正在不斷擴張。減少環境影響、提高資源利用效率以及向循環生產模式轉型的壓力日益增大，這為人工智慧驅動的蛋白質工程技術創造了新的機會。企業正在探索利用人工設計的酵素來取代高能耗的化學工藝，提高回收效率，並實現更永續的生產方式。這種轉變正在拓展人工智慧蛋白質設計的應用範圍，使其超越傳統的醫療保健和製藥領域，成為工業和環境領域一項極具價值的技術。

最佳化障礙

實用化和規模化生產面臨的障礙構成重大挑戰，可能限制市場成長，尤其是依賴先進蛋白質工程和生物技術平台的產業。儘管計算模型和模擬工具可以識別出具有活性、穩定性、結合特性等理想特性的蛋白質，但這些預測的優勢並非總是能成功轉化為實際的商業產品。即使在受控的實驗室條件下表現良好的蛋白質，在實際應用中也可能面臨意想不到的局限性，例如功能下降、不穩定、分解，或在生產和儲存過程中難以保持性能穩定。

目錄

第1章執行摘要：全球人工智慧蛋白質設計市場

第2章：調查方法與研究框架

  • 研究目標
  • 產品概述
  • 市場區隔
  • 定性研究
    • 一手和二手資訊
  • 量化研究
    • 一手和二手資訊
  • 主要調查受訪者組成：按地區分類
  • 本研究的前提
  • 市場規模估算
  • 數據三角測量

第3章：全球人工智慧蛋白質設計市場概覽

  • 產業價值鏈分析
  • 產業展望
    • 全球人工智慧蛋白質設計和生成生物學產業概覽。
    • 從頭開始設計、結構預測和部署到臨床流程。
    • 生物安全篩檢、數據品質和模型檢驗。
  • PESTLE分析
  • 波特五力分析
  • 市場成長及前景
    • 2020-2035年市場收入估算與預測
    • 價格趨勢分析：透過報價

第4章：全球人工智慧蛋白質設計市場分析

  • 競爭對手儀錶板
    • 市場集中度
    • 企業市場占有率分析，2025 年
    • 競爭對手分析與基準測試

第5章：全球人工智慧蛋白質設計市場分析

  • 市場動態和趨勢
    • 成長要素
    • 抑制因子
    • 機會
    • 主要趨勢
  • 市場規模及預測，2020-2035年
    • 報價
      • 關鍵見解
        • 軟體/平台和模型
        • 服務
    • 透過技術
      • 關鍵見解
        • 生成式人工智慧模型
        • 利用深度學習進行結構預測
        • 強化學習
        • 混合人工智慧-物理
    • 透過使用
      • 關鍵見解
        • 治療
          • 抗體設計
          • 迷你蛋白質/黏合劑設計
        • 酵素工程
        • 細胞和基因治療設計
        • 疫苗抗原設計
        • 工業蛋白
    • 不同的發展
      • 關鍵見解
        • 雲端/API
        • 現場
    • 最終用戶
      • 關鍵見解
        • 生物製藥
        • 生技新創企業
        • 學術和研究
        • CRO
    • 按地區
      • 關鍵見解
        • 北美洲
          • 美國
          • 加拿大
          • 墨西哥
        • 歐洲
          • 西歐
            • 英國
            • 德國
            • 法國
            • 義大利
            • 西班牙
            • 其他西歐國家
          • 東歐
            • 波蘭
            • 俄羅斯
            • 其他東歐國家
        • 亞太地區
          • 中國
          • 印度
          • 日本
          • 澳洲和紐西蘭
          • 韓國
          • ASEAN
          • 其他亞太國家
        • 中東和非洲（MEA）
          • 沙烏地阿拉伯
          • 南非
          • UAE
          • 其他中東和非洲國家
        • 南美洲
          • 阿根廷
          • 巴西
          • 其他南美國家

第6章：北美市場分析

第7章：歐洲市場分析

第8章：亞太市場分析

第9章：中東和非洲市場分析

第10章：南美市場分析

第11章：公司簡介

  • DeepMind Technologies Limited
  • Generate：Biomedicines
  • Arzade Corp.
  • Insilico Medicine
  • Cradle
  • Profluent
  • A-Alpha Bio, inc.
  • Schrodinger, Inc.
  • DenevAI Biotrech
  • Symbio Technologies
  • Other Prominent Players

第12章附錄

簡介目錄
Product Code: AA07261870

The AI protein design market is experiencing rapid expansion as advancements in artificial intelligence, computational biology, and biotechnology continue to transform the way new proteins and therapeutic molecules are discovered and developed. The market is estimated to reach approximately USD 1.5 billion in 2025 and is projected to grow to around USD 12.4 billion by 2035, representing a compound annual growth rate (CAGR) of 23.2% during the forecast period of 2026-2035.

A major factor driving market growth is the increasing demand for personalized medicine and precision therapeutics. Healthcare organizations and pharmaceutical companies are seeking more targeted approaches that can address individual patient needs through the development of customized biologics, engineered proteins, and advanced therapies. AI protein design platforms help researchers analyze complex biological data, predict molecular behavior, and create optimized protein candidates with improved activity, specificity, and stability.

Noteworthy Market Developments

The AI protein design market is being shaped by a group of highly advanced companies that are pushing the boundaries of computational biology, generative AI, and molecular engineering. Google DeepMind, through its biotechnology-focused subsidiary Isomorphic Labs, remains one of the most influential players in the AI protein design landscape.

Biomedicines has established a strong position through its Chroma platform, which focuses on generative protein design and the creation of novel biological molecules. EvolutionaryScale is pioneering the use of large language model approaches for biology through its ESM series, including ESM3.

Xaira Therapeutics is focused on commercializing advanced AI-based protein engineering technologies, including approaches derived from structural generative AI methods such as RFdiffusion. Profluent represents the intersection of AI protein design and genome engineering. The company applies large language model techniques to biological sequences with the goal of creating new genetic and molecular systems that do not exist in nature.

Core Growth Drivers

The market is expanding as organizations increasingly seek advanced biomolecular engineering capabilities that can deliver greater precision, speed, and scalability in research and development processes. Traditional approaches often rely on extensive experimental testing to understand how biological molecules behave, which can be time-consuming, expensive, and resource-intensive. The growing demand for more efficient discovery workflows has accelerated interest in AI-driven platforms that can predict molecular properties, optimize designs, and support decision-making before extensive laboratory validation. As industries such as pharmaceuticals, biotechnology, agriculture, and materials science adopt computational approaches, the need for scalable and accurate biomolecular engineering solutions continues to increase.

Emerging Opportunity Trends

The AI protein design market is expanding as industrial companies increasingly seek advanced biological solutions to address major sustainability challenges across sectors such as waste management, plastics recycling, and textile manufacturing. Growing pressure to reduce environmental impact, improve resource efficiency, and transition toward circular production models is creating new opportunities for AI-driven protein engineering technologies. Companies are exploring the use of artificially designed enzymes to replace energy-intensive chemical processes, improve recycling efficiency, and enable more sustainable manufacturing practices. This shift is broadening the application landscape of AI protein design beyond traditional healthcare and pharmaceutical uses, positioning it as a valuable technology for industrial and environmental applications.

Barriers to Optimization

Translational and scaling roadblocks represent a significant challenge that may limit market growth, particularly in industries relying on advanced protein engineering and biotechnology platforms. Although computational models and simulation tools can identify proteins with highly desirable characteristics, such as improved activity, stability, or binding properties, these predicted advantages do not always translate successfully into practical, commercial products. Proteins that demonstrate excellent performance under controlled laboratory conditions may encounter unexpected limitations when developed into real-world applications, including reduced functionality, instability, degradation, or difficulties in maintaining consistent performance during production and storage.

Detailed Market Segmentation

By technology, deep learning structure prediction technology decisively dominated the global AI protein design market throughout the current year, driven by its critical role in solving complex challenges associated with protein structure analysis and engineering. The ability to accurately predict three-dimensional protein structures has become a fundamental requirement for modern computational biology, as the spatial arrangement of amino acids directly influences protein function, stability, and biological interactions. This capability has positioned deep learning-based structure prediction as one of the most important technological foundations within the AI protein design ecosystem.

By application, antibody design applications secured the largest share of the global AI protein design market during the previous year, driven by the rapidly increasing demand for advanced therapeutic solutions, particularly in targeted oncology treatments. The strong market position of antibody design is supported by the critical role of monoclonal antibodies in modern medicine and the growing need for more precise, effective, and personalized therapies. Pharmaceutical and biotechnology companies are increasingly adopting artificial intelligence-based protein design platforms to accelerate antibody discovery, optimize molecular characteristics, and improve the efficiency of therapeutic development processes.

By deployment, cloud-based deployment models have established a dominant position within the AI protein design market, reflecting the growing reliance on scalable digital infrastructure for advanced biological research and computational discovery. The increasing complexity of protein engineering workflows has made cloud platforms an essential component of modern computational biology operations. Researchers, biotechnology companies, and pharmaceutical organizations are increasingly adopting cloud-based environments to access powerful computing resources, advanced artificial intelligence tools, and large-scale biological datasets without the need to maintain extensive in-house infrastructure.

By end user, biopharmaceutical enterprises generated the highest level of demand within the global AI protein design market, driven by their extensive reliance on advanced computational technologies for accelerating drug discovery and therapeutic development. These organizations are increasingly adopting artificial intelligence-powered protein design platforms to improve research efficiency, reduce development timelines, and enhance their ability to identify promising biological candidates. The integration of AI into protein engineering workflows has become a strategic priority as biopharmaceutical companies seek more precise and scalable approaches for creating next-generation medicines.

Segment Breakdown

By Offering

  • Software/Platforms & Models
  • Services

By Technology

  • Generative AI Models
  • Deep-Learning Structure Prediction
  • Reinforcement Learning
  • Hybrid AI-Physics

By Application

  • Therapeutics
  • Antibody Design
  • Miniprotein/Binder Design
  • Enzyme Engineering
  • Cell & Gene Therapy Design
  • Vaccine Antigen Design
  • Industrial Proteins

By Deployment

  • Cloud/API
  • On-Premises

By End User

  • Biopharma
  • Biotech Startups
  • Academic & Research
  • CROs

By Region

  • North America
  • The U.S.
  • Canada
  • Mexico
  • Europe
  • Western Europe
  • The UK
  • Germany
  • France
  • Italy
  • Spain
  • Rest of Western Europe
  • Eastern Europe
  • Poland
  • Russia
  • Rest of Eastern Europe
  • Asia Pacific
  • China
  • India
  • Japan
  • Australia & New Zealand
  • South Korea
  • ASEAN
  • Rest of Asia Pacific
  • Middle East & Africa (MEA)
  • Saudi Arabia
  • South Africa
  • UAE
  • Rest of MEA
  • South America
  • Argentina
  • Brazil
  • Rest of South America

Geography Breakdown

  • North America currently maintains a leading position in the global computational protein engineering market, supported by a highly developed biotechnology ecosystem, advanced research infrastructure, and strong investments in artificial intelligence-driven life sciences. The region's market leadership is reinforced by the presence of major biotechnology companies, pharmaceutical organizations, technology enterprises, and research institutions that are actively developing computational approaches for designing and optimizing novel proteins.
  • A major factor contributing to regional dominance is the significant availability of venture capital funding directed toward biotechnology and computational life science startups. Large-scale investments are accelerating the commercialization of emerging protein engineering platforms, particularly among biotechnology companies operating in innovation centers such as Silicon Valley and other major research clusters across the United States.

Leading Market Participants

  • DeepMind Technologies Limited
  • Generate: Biomedicines
  • Arzade Corp.
  • Insilico Medicine
  • Cradle
  • Profluent
  • A-Alpha Bio, Inc.
  • Schrodinger, Inc.
  • DenevAI Biotrech
  • Symbio Technologies
  • Other Prominent Players

Table of Content

Chapter 1. Executive Summary: Global AI Protein Design Market

Chapter 2. Research Methodology & Research Framework

  • 2.1. Research Objective
  • 2.2. Product Overview
  • 2.3. Market Segmentation
  • 2.4. Qualitative Research
    • 2.4.1. Primary & Secondary Sources
  • 2.5. Quantitative Research
    • 2.5.1. Primary & Secondary Sources
  • 2.6. Breakdown of Primary Research Respondents, By Region
  • 2.7. Assumption for Study
  • 2.8. Market Size Estimation
  • 2.9. Data Triangulation

Chapter 3. Global AI Protein Design Market Overview

  • 3.1. Industry Value Chain Analysis
    • 3.1.1. Protein-Sequence, Structural & Genomic Data Providers
    • 3.1.2. Foundation Model & Generative-Biology Algorithm Developers
    • 3.1.3. Cloud / GPU Compute & AI Protein-Design Platform Vendors
    • 3.1.4. Wet-Lab Validation, CRO & Synthesis Partners
    • 3.1.5. End Users (Biopharma, Biotech Startups, Academic & Research, CROs)
  • 3.2. Industry Outlook
    • 3.2.1. Overview of the Global AI Protein Design & Generative-Biology Industry
    • 3.2.2. De Novo Design, Structure Prediction & Clinical-Pipeline Translation
    • 3.2.3. Biosecurity Screening, Data Quality & Model-Validation Considerations
  • 3.3. PESTLE Analysis
  • 3.4. Porter's Five Forces Analysis
    • 3.4.1. Bargaining Power of Suppliers
    • 3.4.2. Bargaining Power of Buyers
    • 3.4.3. Threat of Substitutes
    • 3.4.4. Threat of New Entrants
    • 3.4.5. Degree of Competition
  • 3.5. Market Growth and Outlook
    • 3.5.1. Market Revenue Estimates and Forecast (US$ Mn), 2020-2035
    • 3.5.2. Price Trend Analysis, By Offering

Chapter 4. Global AI Protein Design Market Analysis

  • 4.1. Competition Dashboard
    • 4.1.1. Market Concentration Rate
    • 4.1.2. Company Market Share Analysis (Value %), 2025
    • 4.1.3. Competitor Mapping & Benchmarking

Chapter 5. Global AI Protein Design Market Analysis

  • 5.1. Market Dynamics and Trends
    • 5.1.1. Growth Drivers
    • 5.1.2. Restraints
    • 5.1.3. Opportunity
    • 5.1.4. Key Trends
  • 5.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 5.2.1. By Offering
      • 5.2.1.1. Key Insights
        • 5.2.1.1.1. Software/Platforms & Models
        • 5.2.1.1.2. Services
    • 5.2.2. By Technology
      • 5.2.2.1. Key Insights
        • 5.2.2.1.1. Generative AI Models
        • 5.2.2.1.2. Deep-Learning Structure Prediction
        • 5.2.2.1.3. Reinforcement Learning
        • 5.2.2.1.4. Hybrid AI-Physics
    • 5.2.3. By Application
      • 5.2.3.1. Key Insights
        • 5.2.3.1.1. Therapeutics
          • 5.2.3.1.1.1. Antibody Design
          • 5.2.3.1.1.2. Miniprotein/Binder Design
        • 5.2.3.1.2. Enzyme Engineering
        • 5.2.3.1.3. Cell & Gene Therapy Design
        • 5.2.3.1.4. Vaccine Antigen Design
        • 5.2.3.1.5. Industrial Proteins
    • 5.2.4. By Deployment
      • 5.2.4.1. Key Insights
        • 5.2.4.1.1. Cloud/API
        • 5.2.4.1.2. On-Premises
    • 5.2.5. By End User
      • 5.2.5.1. Key Insights
        • 5.2.5.1.1. Biopharma
        • 5.2.5.1.2. Biotech Startups
        • 5.2.5.1.3. Academic & Research
        • 5.2.5.1.4. CROs
    • 5.2.6. By Region
      • 5.2.6.1. Key Insights
        • 5.2.6.1.1. North America
          • 5.2.6.1.1.1. The U.S.
          • 5.2.6.1.1.2. Canada
          • 5.2.6.1.1.3. Mexico
        • 5.2.6.1.2. Europe
          • 5.2.6.1.2.1. Western Europe
            • 5.2.6.1.2.1.1. The UK
            • 5.2.6.1.2.1.2. Germany
            • 5.2.6.1.2.1.3. France
            • 5.2.6.1.2.1.4. Italy
            • 5.2.6.1.2.1.5. Spain
            • 5.2.6.1.2.1.6. Rest of Western Europe
          • 5.2.6.1.2.2. Eastern Europe
            • 5.2.6.1.2.2.1. Poland
            • 5.2.6.1.2.2.2. Russia
            • 5.2.6.1.2.2.3. Rest of Eastern Europe
        • 5.2.6.1.3. Asia Pacific
          • 5.2.6.1.3.1. China
          • 5.2.6.1.3.2. India
          • 5.2.6.1.3.3. Japan
          • 5.2.6.1.3.4. Australia & New Zealand
          • 5.2.6.1.3.5. South Korea
          • 5.2.6.1.3.6. ASEAN
          • 5.2.6.1.3.7. Rest of Asia Pacific
        • 5.2.6.1.4. Middle East & Africa (MEA)
          • 5.2.6.1.4.1. Saudi Arabia
          • 5.2.6.1.4.2. South Africa
          • 5.2.6.1.4.3. UAE
          • 5.2.6.1.4.4. Rest of MEA
        • 5.2.6.1.5. South America
          • 5.2.6.1.5.1. Argentina
          • 5.2.6.1.5.2. Brazil
          • 5.2.6.1.5.3. Rest of South America

Chapter 6. North America Market Analysis

  • 6.1. Market Dynamics and Trends
    • 6.1.1. Growth Drivers
    • 6.1.2. Restraints
    • 6.1.3. Opportunity
    • 6.1.4. Key Trends
  • 6.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 6.2.1. Key Insights
      • 6.2.1.1. By Offering
      • 6.2.1.2. By Technology
      • 6.2.1.3. By Application
      • 6.2.1.4. By Deployment
      • 6.2.1.5. By End User
      • 6.2.1.6. By Country

Chapter 7. Europe Market Analysis

  • 7.1. Market Dynamics and Trends
    • 7.1.1. Growth Drivers
    • 7.1.2. Restraints
    • 7.1.3. Opportunity
    • 7.1.4. Key Trends
  • 7.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 7.2.1. Key Insights
      • 7.2.1.1. By Offering
      • 7.2.1.2. By Technology
      • 7.2.1.3. By Application
      • 7.2.1.4. By Deployment
      • 7.2.1.5. By End User
      • 7.2.1.6. By Country

Chapter 8. Asia Pacific Market Analysis

  • 8.1. Market Dynamics and Trends
    • 8.1.1. Growth Drivers
    • 8.1.2. Restraints
    • 8.1.3. Opportunity
    • 8.1.4. Key Trends
  • 8.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 8.2.1. Key Insights
      • 8.2.1.1. By Offering
      • 8.2.1.2. By Technology
      • 8.2.1.3. By Application
      • 8.2.1.4. By Deployment
      • 8.2.1.5. By End User
      • 8.2.1.6. By Country

Chapter 9. Middle East & Africa Market Analysis

  • 9.1. Market Dynamics and Trends
    • 9.1.1. Growth Drivers
    • 9.1.2. Restraints
    • 9.1.3. Opportunity
    • 9.1.4. Key Trends
  • 9.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 9.2.1. Key Insights
      • 9.2.1.1. By Offering
      • 9.2.1.2. By Technology
      • 9.2.1.3. By Application
      • 9.2.1.4. By Deployment
      • 9.2.1.5. By End User
      • 9.2.1.6. By Country

Chapter 10. South America Market Analysis

  • 10.1. Market Dynamics and Trends
    • 10.1.1. Growth Drivers
    • 10.1.2. Restraints
    • 10.1.3. Opportunity
    • 10.1.4. Key Trends
  • 10.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 10.2.1. Key Insights
      • 10.2.1.1. By Offering
      • 10.2.1.2. By Technology
      • 10.2.1.3. By Application
      • 10.2.1.4. By Deployment
      • 10.2.1.5. By End User
      • 10.2.1.6. By Country

Chapter 11. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, and Business Strategy Outlook)

  • 11.1. DeepMind Technologies Limited
  • 11.2. Generate: Biomedicines
  • 11.3. Arzade Corp.
  • 11.4. Insilico Medicine
  • 11.5. Cradle
  • 11.6. Profluent
  • 11.7. A-Alpha Bio, inc.
  • 11.8. Schrodinger, Inc.
  • 11.9. DenevAI Biotrech
  • 11.10. Symbio Technologies
  • 11.11. Other Prominent Players

Chapter 12. Annexure

  • 12.1. List of Secondary Sources
  • 12.2. Key Country Markets- Macro Economic Outlook/Indicators