인공지능(AI) 단백질 설계 시장 : 제공별, 기술별, 용도별, 전개 형태별, 최종 사용자별 - 시장 규모, 업계 역학, 기회 분석 및 예측(2026-2035년)

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