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市場調查報告書
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1914286

AI蛋白質設計市場：按蛋白質類型、技術平台、方法、應用、最終用戶和部署類型分類－2026-2032年全球預測

AI Protein Design Market by Protein Type, Technology Platform, Approach, Application, End User, Deployment Mode - Global Forecast 2026-2032
出版日期: | 出版商: 360iResearch | 英文 185 Pages | 商品交期: 最快1-2個工作天內

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預計到 2025 年，人工智慧 (AI) 蛋白質設計市場價值將達到 6.103 億美元，到 2026 年將成長至 7.1897 億美元，到 2032 年將達到 20.133 億美元，複合年成長率為 18.59%。

關鍵市場統計數據
基準年 2025 6.103億美元
預計年份：2026年 7.1897億美元
預測年份 2032 20.133億美元
複合年成長率 (%) 18.59%

權威的介紹將人工智慧驅動的蛋白質工程定位為加速發現、商業化和跨領域創新的策略領域。

人工智慧驅動的蛋白質設計已從學術概念驗證研究發展成為支撐治療、農業、工業生物技術和食品系統等領域產品創新的基礎技術。演算法方法的進步、高通量實驗的檢驗以及雲端運算能力的提升，使得In Silico設計、合成和驗證測試的迭代循環得以以前所未有的速度進行。因此，整合這些能力的機構可以縮短研發週期，豐富候選藥物組合，並探索過去被認為難以實現的治療模式。

對融合的技術進步和不斷演變的經營模式的深刻分析，揭示了這些因素如何重塑蛋白質設計創造商業性價值的方式。

過去幾年，機器學習領域取得了相互融合、相輔相成的進步：更具表現力的機器學習架構、可擴展的結構預測方法以及更高的實驗吞吐量。同時，大量公共和專有資料集湧現，用於模型訓練和檢驗，從而提高了預測精度和泛化能力。此外，雲端原生基礎架構和容器化工作流程降低了採用門檻，使曾經只有大型機構才能使用的功能，如今也惠及小規模團隊。

重點評估 2025 年關稅變化如何改變了蛋白質設計生態系統內的供應韌性、硬體策略和跨境合作。

2025 年關稅調整對人工智慧驅動的蛋白質設計生態系統產生了多方面的影響，波及實驗室耗材、專用設備和計算硬體的流通。試劑、基因合成服務和分析耗材的上游供應商經歷了進出口環境的變化，進而影響了計劃進度和籌資策略。依賴跨境供應鏈的機構需要製定更嚴格的替代採購方案，並加強庫存調整，以應對間歇性延誤。

將應用、蛋白質類型、技術平台、最終用戶、方法和部署模式與策略重點連結的多維度細分整合

應用趨勢凸顯清晰的機會叢集：農業性狀改良優先開發抗性和氣候適應性蛋白，以提高作物產量；藥物研發越來越依賴電腦篩選來加速生物製藥、小分子治療藥物和疫苗設計中的候選藥物篩選；食品飲料創新利用酶和功能性胜肽開發替代蛋白並延長保存期限；工業酶開發持續追求製程最佳化和永續性。這些應用途徑影響著設計交付所需的精確度以及下游檢驗的嚴謹性。

從區域比較觀點，揭示各主要區域的監管態度、基礎建設成熟度及投資重點如何影響商業化進程。

在美洲，強勁的創業投資流入、大規模的生物製藥研發管線以及完善的合約研究和生產基礎設施，為商業化和規模化生產創造了肥沃的環境。主要司法管轄區的法規結構日益重視數據驅動的設計證據，這不僅促進了臨床轉化，也提高了對可重複性和可追溯性的期望。人才的集中和強大的產學合作，支持從發現到早期檢驗的快速迭代。

本文概述了公司的策略和能力叢集，解釋了平台深度、夥伴關係和人才如何塑造蛋白質設計領域的競爭優勢。

主要企業正透過整合高階建模、高通量檢驗和特定領域資料集的整合技術堆疊來脫穎而出。有些公司優先考慮端到端服務模式，涵蓋從設計到功能檢驗和規模化部署的整個流程；而有些公司則專注於平台授權和軟體即服務 (SaaS) 產品。在平台建構者（他們大力投資於可解釋性和模型管治）與專注於速度或針對特定蛋白質類別進行最佳化的利基專家之間，存在著明顯的轉折點。

為建立穩健的流程、有效的模型管治以及加速蛋白質設計領域的轉化成果提供實際有效的建議

優先考慮模組化流程，以便獨立擴展運算和實驗室能力。各組織應透過投資標準化資料格式、自動化檢測平台和可互通的API，將演算法開發和檢驗執行分開。這將縮短迭代時間並提高可重複性。此外，應儘早投資於包含版本控制、追溯和審核追蹤的模型管治框架，以滿足監管機構和合作夥伴的實質審查要求。

採用透明的混合方法調查方法，結合專家訪談、技術文獻綜合和三角驗證技術，確保獲得可靠且可操作的見解。

本研究整合了系統性的一手和二手研究成果。一手研究包括對研發、營運和企業策略等跨職能部門領導者的深入訪談，並輔以對計算生物學家和實驗室主任的技術諮詢。訪談重點在於可重複性實踐、監管證據要件以及從設計到檢驗的流程擴展過程中遇到的營運挑戰。這些第一手資料被用於建構戰略風險和營運槓桿框架。

這份概要清楚地概述了將人工智慧蛋白質設計的潛力轉化為永續成果所需的營運、管治和夥伴關係的優先事項。

人工智慧驅動的蛋白質設計有望徹底改變生物功能的發現、最佳化和商業化方式。然而，只有將其整合到一個系統化的流程中，才能真正發揮這項技術的價值。該流程包括嚴格的模型檢驗、可擴展的實驗方案以及積極的監管溝通。那些能夠將卓越的運算能力與成熟的營運經驗結合的機構，將能夠縮短創新週期、降低技術轉移風險，並更有信心地將差異化產品推向市場。

目錄

第1章：序言

第2章調查方法

  • 研究設計
  • 研究框架
  • 市場規模預測
  • 數據三角測量
  • 調查結果
  • 調查前提
  • 調查限制

第3章執行摘要

  • 首席主管觀點
  • 市場規模和成長趨勢
  • 2025年市佔率分析
  • FPNV定位矩陣，2025
  • 新的商機
  • 下一代經營模式
  • 產業藍圖

第4章 市場概覽

  • 產業生態系與價值鏈分析
  • 波特五力分析
  • PESTEL 分析
  • 市場展望
  • 上市策略

第5章 市場洞察

  • 消費者洞察與終端用戶觀點
  • 消費者體驗基準
  • 機會地圖
  • 分銷通路分析
  • 價格趨勢分析
  • 監理合規和標準框架
  • ESG與永續性分析
  • 中斷和風險情景
  • 投資報酬率和成本效益分析

第6章：美國關稅的累積影響，2025年

第7章：人工智慧的累積影響，2025年

8. 人工智慧蛋白質設計市場（按蛋白質類型分類）

  • 抗體
  • 酵素
    • 水解酵素
    • 氧化還原酶
    • 轉移酶
  • 胜肽

9. 按技術平台分類的人工智慧蛋白質設計市場

  • 計算設計軟體
  • 機器學習模型
    • 深度學習
    • 演化演算法
    • 實體資訊模型
  • 結構生物資訊工具

第10章：人工智慧蛋白質設計市場方法論

  • 全新設計
  • 引導進化
  • 理性設計

第11章：人工智慧蛋白質設計市場（按應用分類）

  • 農業性狀改良
  • 藥物發現
    • 生物製劑
    • 小分子療法
    • 疫苗設計
  • 食品/飲料
  • 工業酵素開發

第12章：人工智慧蛋白質設計市場（按最終用戶分類）

  • 學術研究機構
  • 農藥生產商
  • 生技公司
  • 製藥公司

第13章 人工智慧蛋白質設計市場依部署模式分類

  • 雲
  • 混合
  • 本地部署

第14章 各地區的AI蛋白質設計市場

  • 美洲
    • 北美洲
    • 拉丁美洲
  • 歐洲、中東和非洲
    • 歐洲
    • 中東
    • 非洲
  • 亞太地區

第15章 人工智慧蛋白質設計市場（以群體分類）

  • ASEAN
  • GCC
  • EU
  • BRICS
  • G7
  • NATO

第16章：各國人工智慧蛋白質設計市場

  • 美國
  • 加拿大
  • 墨西哥
  • 巴西
  • 英國
  • 德國
  • 法國
  • 俄羅斯
  • 義大利
  • 西班牙
  • 中國
  • 印度
  • 日本
  • 澳洲
  • 韓國

第17章美國人工智慧蛋白質設計市場

第18章：中國的AI蛋白質設計市場

第19章 競爭情勢

  • 市場集中度分析，2025年
    • 濃度比（CR）
    • 赫芬達爾-赫希曼指數 (HHI)
  • 近期趨勢及影響分析，2025 年
  • 2025年產品系列分析
  • 基準分析，2025 年
  • Absci, Inc.
  • AI Proteins, Inc.
  • Alphabet Inc.
  • Atomwise, Inc.
  • BenevolentAI Ltd.
  • Cradle Bio, Inc.
  • Cyclica Inc.
  • DenovAI Biotech, Inc.
  • Exscientia plc
  • Generate Biomedicines, Inc.
  • Insilico Medicine, Inc.
  • Lantern Pharma, Inc.
  • Recursion Pharmaceuticals, Inc.
  • Relay Therapeutics, Inc.
  • Schrodinger, Inc.
Product Code: MRR-AE420CB13BD8

The AI Protein Design Market was valued at USD 610.30 million in 2025 and is projected to grow to USD 718.97 million in 2026, with a CAGR of 18.59%, reaching USD 2,013.30 million by 2032.

KEY MARKET STATISTICS
Base Year [2025] USD 610.30 million
Estimated Year [2026] USD 718.97 million
Forecast Year [2032] USD 2,013.30 million
CAGR (%) 18.59%

An authoritative introduction positioning AI-enabled protein engineering as a strategic discipline that accelerates discovery, commercialization, and cross-sector innovation

AI-driven protein design has shifted from academic proof-of-concept studies to a foundational technology that underpins product innovation across therapeutics, agriculture, industrial biotechnology, and food systems. Advances in algorithmic approaches, high-throughput experimental validation, and cloud-enabled compute capacity now permit iterative cycles of in silico design, synthesis, and empirical testing at unprecedented speed. Consequently, organizations that integrate these capabilities are able to compress discovery timelines, diversify candidate portfolios, and pursue modalities previously considered intractable.

As translational ambitions grow, so do demands for interdisciplinary coordination between computational scientists, wet-lab biologists, regulatory strategists, and commercialization teams. Investors and corporate R&D leaders are increasingly evaluating not only algorithmic performance but also data governance, reproducibility, and the scalability of experimental pipelines. In this context, the strategic question becomes how to align capability development with product and partnership roadmaps, while maintaining rigorous controls over safety, traceability, and intellectual property.

Taken together, the current landscape rewards organizations that combine domain expertise with engineering discipline, deploy modular technology stacks, and cultivate collaborative ecosystems. This introduction positions AI-enabled protein design as a commercial enabler that requires integrated technical, operational, and regulatory approaches to realize durable competitive advantage

An insightful analysis of converging technological advances and evolving business models that are reshaping how protein design delivers commercial value

The last several years have seen convergent advances that amplify one another: more expressive machine learning architectures, scalable structural prediction methods, and expanded experimental throughput. These technological gains are paralleled by richer public and proprietary datasets that feed model training and validation, improving both predictive accuracy and generalizability. Meanwhile, cloud-native infrastructure and containerized workflows have lowered barriers to adoption, enabling smaller teams to access capabilities that were once the preserve of large institutions.

Concurrently, business models are evolving. Strategic partnerships now combine algorithmic design houses with contract development and manufacturing organizations, bridging gaps between candidate generation and scalable production. Outsourced experimental platforms and shared-foundry models accelerate validation and de-risk early-stage programs. Regulatory engagement is also maturing, with agencies signaling increased familiarity with data-driven design evidence; this reduces ambiguity for developers but heightens the need for transparent, auditable model outputs and traceable laboratory records.

Collectively, these shifts favor organizations that can orchestrate multi-disciplinary teams, operationalize model governance, and integrate design-to-validate pipelines. In the near term, the differentiators will be data quality, cross-functional execution, and the ability to translate computational promise into reproducible biological performance

A focused assessment of how 2025 tariff changes have reshaped supply resilience, hardware strategies, and cross-border collaborations within protein design ecosystems

Tariff adjustments enacted in 2025 have introduced layered effects across the AI-enabled protein design ecosystem, influencing the flow of laboratory inputs, specialized instrumentation, and compute hardware. Upstream suppliers of reagents, gene synthesis services, and analytical consumables have experienced altered import/export dynamics, which in turn affects project timelines and procurement strategies. For organizations reliant on cross-border supply chains, this has necessitated more rigorous sourcing contingency planning and greater inventory coordination to mitigate intermittent delays.

Beyond physical goods, tariffs and associated trade controls have influenced the economics of hardware acquisition, particularly for high-performance compute accelerators and edge devices used in laboratory automation. As a result, some teams have accelerated cloud-first strategies to avoid capital expenditure exposure, while others have pursued regional vendor partnerships to localize critical infrastructure. These adjustments have implications for cost allocation and capital planning, and they tend to increase the value of flexible deployment modes and hybrid architectures.

Moreover, tariff-driven shifts have subtly reshaped collaboration patterns. Cross-border research collaborations and contracted services are now evaluated not only on scientific merit and cost but also on regulatory friction and logistics risk. Consequently, commercial partners and academic collaborators are increasingly expected to articulate resilient supply chain plans. In sum, tariffs in 2025 have made supply chain robustness and deployment flexibility central to strategic planning for entities engaged in AI-driven protein design

A multidimensional segmentation synthesis that links applications, protein types, technology platforms, end users, approaches, and deployment modes to strategic priorities

Application dynamics show distinct opportunity clusters: Agricultural Trait Enhancement is prioritizing resilient, climate-adaptive proteins to support crop performance; Drug Discovery spans biologics, small molecule therapeutics, and vaccine design with increasing reliance on computational triage to accelerate candidate selection; Food & Beverage innovations are leaning on enzymes and functional peptides to enable alternative proteins and shelf-life improvements; and Industrial Enzyme Development continues to pursue process optimization and sustainability gains. These application pathways influence the required fidelity of design outputs and the intensity of downstream validation.

Protein type considerations drive both scientific approach and commercialization strategy. Antibodies remain central for targeted therapeutics and diagnostics, while enzymes-especially hydrolases, oxidoreductases, and transferases-are key to catalysis, bioprocessing, and environmental applications. Peptides offer a middle ground with modularity and manufacturability advantages. Each protein type imposes distinct constraints on stability engineering, manufacturability, and regulatory evidence packages.

Technology platform segmentation highlights how capability stacks are assembled. Computational Design Software provides user-facing workflows; Machine Learning Models-encompassing deep learning, evolutionary algorithms, and physics-informed models-deliver predictive and generative power; and Structural Bioinformatics Tools enable mechanistic interpretation. The interplay among these platforms determines both throughput and interpretability of outputs.

End user diversity matters because needs differ across academic research centers, agrochemical companies, biotechnology firms, and pharmaceutical companies. Academic centers prioritize discovery and method development, agrochemical firms focus on trait robustness and field deployability, biotech companies balance innovation with investor timelines, and pharmaceutical organizations emphasize regulatory readiness and clinical translation.

Approach selection-de novo design, directed evolution, or rational design-reflects trade-offs among novelty, development speed, and experimental burden. Deployment mode decisions between cloud, hybrid, and on-premises solutions further mediate data governance, latency, and capital allocation. Together, these segmentation layers create a multidimensional map that informs both product development priorities and go-to-market positioning

A comparative regional perspective that clarifies how regulatory posture, infrastructure maturity, and investment priorities shape commercialization in each major geography

In the Americas, dynamic venture capital flows, large biopharma pipelines, and a robust contract research and manufacturing infrastructure create fertile conditions for commercialization and scale-up. Regulatory frameworks in major jurisdictions have become more familiar with data-driven design evidence, encouraging clinical translation while also elevating expectations for reproducibility and traceability. Talent concentration and strong academic-industry linkages support rapid iteration from discovery to early-stage validation.

Europe, Middle East & Africa present a heterogeneous landscape where regulatory sophistication varies across the region. European markets emphasize ethical governance, data protection, and sustainability, which shape product positioning and evidence requirements. Several countries possess deep expertise in structural biology and protein engineering, supporting collaborative consortia and public-private partnerships. In parts of the region, industrial biotechnology and agrochemical applications are prioritized as part of circular economy and food security agendas.

Asia-Pacific exhibits high demand elasticity, with significant investment in both computational infrastructure and wet-lab capacity. Governments in multiple jurisdictions are prioritizing biotech sovereignty and local manufacturing, which encourages partnerships that localize production and reduce reliance on cross-border supply chains. Rapid adoption of cloud-native platforms and aggressive talent development programs are accelerating capability buildout and enabling faster commercialization cycles in emerging hubs

Across these regions, strategic decisions about partnerships, localization, and regulatory engagement must be tailored to local incentives, workforce availability, and infrastructure maturity. Consequently, regional nuance is a crucial input to commercialization and operational roadmaps

A distilled view of company strategies and capability clusters that explains how platform depth, partnerships, and talent shape competitive advantage in protein design

Leading organizations differentiate through integrated stacks that combine advanced modeling, high-throughput validation, and domain-specific datasets. Some companies prioritize end-to-end service models that take candidates from design through functional validation and scale-up, while others concentrate on platform licensing or software-as-a-service offerings. A clear inflection point separates platform builders who invest heavily in interpretability and model governance from niche specialists that optimize for speed or for particular protein classes.

Startups and mid-sized firms are often the primary source of radical algorithmic innovation and tend to partner with established contract research organizations to compensate for limited in-house wet-lab capacity. Conversely, large established biopharma and agrochemical firms leverage their downstream regulatory and manufacturing expertise to de-risk late-stage development. Strategic partnerships and licensing agreements are therefore common, enabling each party to focus on core competencies while sharing validation and commercialization risk.

Talent acquisition and retention are consistent differentiators. Firms that successfully hire interdisciplinary teams-combining computational scientists, structural biologists, and process engineers-move faster from prototype to validated candidates. Additionally, firms investing in transparent model validation practices, robust IP strategies, and clear documentation often achieve stronger credibility with regulators and investors. Overall, company strategies reflect trade-offs between platform depth, vertical integration, and partnership-driven scale

Practical, high-impact recommendations for leaders to build resilient pipelines, govern models effectively, and accelerate translational outcomes in protein design

Prioritize modular pipelines that allow independent scaling of computational and laboratory capacity. Organizations should decouple algorithm development from validation execution by investing in standardized data formats, automated assay platforms, and interoperable APIs, thereby reducing time-to-iterate and increasing reproducibility. Additionally, invest early in model governance frameworks that incorporate versioning, provenance, and audit trails to satisfy regulatory and partner due-diligence requirements.

Second, adopt a portfolio approach to risk, combining de novo design projects that offer breakthrough potential with directed evolution or rational design initiatives that provide more predictable pathways to validation. Align each program with a tailored evidence generation plan that explicitly maps computational outputs to experimental assays and regulatory milestones. This alignment shortens feedback loops and clarifies resource allocation.

Third, pursue strategic partnerships that complement internal capabilities rather than attempting to vertically integrate every function. Collaborations with regional labs, foundries, or CROs can accelerate experimental throughput while preserving capital flexibility. Finally, cultivate multidisciplinary talent by establishing joint training programs and by creating career pathways that reward collaboration between computation and bench science. Together, these actions will improve execution velocity and reduce translational risk

A transparent, mixed-methods research approach that combines expert interviews, technical literature synthesis, and triangulation to ensure robust, actionable findings

This study synthesizes findings from a structured program of primary and secondary research. Primary research included in-depth interviews with cross-functional leaders in R&D, operations, and corporate strategy, complemented by technical consultations with practicing computational biologists and laboratory directors. Interviews emphasized reproducibility practices, evidence requirements for regulatory engagement, and operational challenges in scaling design-to-validate pipelines. These direct insights informed the framing of strategic risks and operational levers.

Secondary research entailed a systematic review of peer-reviewed literature, patent filings, public regulatory guidance, and company disclosures to map technological trajectories and validation approaches. Data triangulation was used to cross-check claims and to contextualize anecdotal findings. Wherever possible, technical assertions were validated against experimental protocols and reproducibility criteria rather than solely relying on press releases or marketing materials.

Analytical methods incorporated qualitative coding to extract thematic patterns and a comparative capability assessment to identify differentiators across platforms. Assumptions and limitations are explicitly documented, including potential biases introduced by interview selection and the rapid pace of technological advancement. Where appropriate, scenario analysis was used to illustrate strategic implications under alternative operational conditions. This rigorous, mixed-methods approach ensures that conclusions are evidence-based and actionable for decision-makers

A clear concluding synthesis that highlights the operational, governance, and partnership priorities required to convert AI protein design promise into durable impact

AI-enabled protein design offers a step-change in how biological function can be discovered, optimized, and commercialized. However, the technology's promise is realized only when integrated into disciplined processes that include rigorous model validation, scalable experimental pipelines, and proactive regulatory engagement. Organizations that marry computational excellence with operational maturity will be able to shorten innovation cycles, reduce translational risk, and bring differentiated products to market more reliably.

In closing, strategic success hinges on three interlinked priorities: building interoperable technology stacks that support reproducible workflows, structuring partnerships that accelerate validation while conserving capital, and embedding governance practices that satisfy both regulatory bodies and commercial stakeholders. By focusing on these priorities, leaders can convert scientific potential into practical, defensible, and scalable solutions that deliver value across therapeutic, agricultural, industrial, and food system applications

Table of Contents

1. Preface

  • 1.1. Objectives of the Study
  • 1.2. Market Definition
  • 1.3. Market Segmentation & Coverage
  • 1.4. Years Considered for the Study
  • 1.5. Currency Considered for the Study
  • 1.6. Language Considered for the Study
  • 1.7. Key Stakeholders

2. Research Methodology

  • 2.1. Introduction
  • 2.2. Research Design
    • 2.2.1. Primary Research
    • 2.2.2. Secondary Research
  • 2.3. Research Framework
    • 2.3.1. Qualitative Analysis
    • 2.3.2. Quantitative Analysis
  • 2.4. Market Size Estimation
    • 2.4.1. Top-Down Approach
    • 2.4.2. Bottom-Up Approach
  • 2.5. Data Triangulation
  • 2.6. Research Outcomes
  • 2.7. Research Assumptions
  • 2.8. Research Limitations

3. Executive Summary

  • 3.1. Introduction
  • 3.2. CXO Perspective
  • 3.3. Market Size & Growth Trends
  • 3.4. Market Share Analysis, 2025
  • 3.5. FPNV Positioning Matrix, 2025
  • 3.6. New Revenue Opportunities
  • 3.7. Next-Generation Business Models
  • 3.8. Industry Roadmap

4. Market Overview

  • 4.1. Introduction
  • 4.2. Industry Ecosystem & Value Chain Analysis
    • 4.2.1. Supply-Side Analysis
    • 4.2.2. Demand-Side Analysis
    • 4.2.3. Stakeholder Analysis
  • 4.3. Porter's Five Forces Analysis
  • 4.4. PESTLE Analysis
  • 4.5. Market Outlook
    • 4.5.1. Near-Term Market Outlook (0-2 Years)
    • 4.5.2. Medium-Term Market Outlook (3-5 Years)
    • 4.5.3. Long-Term Market Outlook (5-10 Years)
  • 4.6. Go-to-Market Strategy

5. Market Insights

  • 5.1. Consumer Insights & End-User Perspective
  • 5.2. Consumer Experience Benchmarking
  • 5.3. Opportunity Mapping
  • 5.4. Distribution Channel Analysis
  • 5.5. Pricing Trend Analysis
  • 5.6. Regulatory Compliance & Standards Framework
  • 5.7. ESG & Sustainability Analysis
  • 5.8. Disruption & Risk Scenarios
  • 5.9. Return on Investment & Cost-Benefit Analysis

6. Cumulative Impact of United States Tariffs 2025

7. Cumulative Impact of Artificial Intelligence 2025

8. AI Protein Design Market, by Protein Type

  • 8.1. Antibodies
  • 8.2. Enzymes
    • 8.2.1. Hydrolases
    • 8.2.2. Oxidoreductases
    • 8.2.3. Transferases
  • 8.3. Peptides

9. AI Protein Design Market, by Technology Platform

  • 9.1. Computational Design Software
  • 9.2. Machine Learning Models
    • 9.2.1. Deep Learning
    • 9.2.2. Evolutionary Algorithms
    • 9.2.3. Physics-Informed Models
  • 9.3. Structural Bioinformatics Tools

10. AI Protein Design Market, by Approach

  • 10.1. De Novo Design
  • 10.2. Directed Evolution
  • 10.3. Rational Design

11. AI Protein Design Market, by Application

  • 11.1. Agricultural Trait Enhancement
  • 11.2. Drug Discovery
    • 11.2.1. Biologics
    • 11.2.2. Small Molecule Therapeutics
    • 11.2.3. Vaccine Design
  • 11.3. Food & Beverage
  • 11.4. Industrial Enzyme Development

12. AI Protein Design Market, by End User

  • 12.1. Academic Research Centers
  • 12.2. Agrochemical Companies
  • 12.3. Biotechnology Firms
  • 12.4. Pharmaceutical Companies

13. AI Protein Design Market, by Deployment Mode

  • 13.1. Cloud
  • 13.2. Hybrid
  • 13.3. On-Premises

14. AI Protein Design Market, by Region

  • 14.1. Americas
    • 14.1.1. North America
    • 14.1.2. Latin America
  • 14.2. Europe, Middle East & Africa
    • 14.2.1. Europe
    • 14.2.2. Middle East
    • 14.2.3. Africa
  • 14.3. Asia-Pacific

15. AI Protein Design Market, by Group

  • 15.1. ASEAN
  • 15.2. GCC
  • 15.3. European Union
  • 15.4. BRICS
  • 15.5. G7
  • 15.6. NATO

16. AI Protein Design Market, by Country

  • 16.1. United States
  • 16.2. Canada
  • 16.3. Mexico
  • 16.4. Brazil
  • 16.5. United Kingdom
  • 16.6. Germany
  • 16.7. France
  • 16.8. Russia
  • 16.9. Italy
  • 16.10. Spain
  • 16.11. China
  • 16.12. India
  • 16.13. Japan
  • 16.14. Australia
  • 16.15. South Korea

17. United States AI Protein Design Market

18. China AI Protein Design Market

19. Competitive Landscape

  • 19.1. Market Concentration Analysis, 2025
    • 19.1.1. Concentration Ratio (CR)
    • 19.1.2. Herfindahl Hirschman Index (HHI)
  • 19.2. Recent Developments & Impact Analysis, 2025
  • 19.3. Product Portfolio Analysis, 2025
  • 19.4. Benchmarking Analysis, 2025
  • 19.5. Absci, Inc.
  • 19.6. AI Proteins, Inc.
  • 19.7. Alphabet Inc.
  • 19.8. Atomwise, Inc.
  • 19.9. BenevolentAI Ltd.
  • 19.10. Cradle Bio, Inc.
  • 19.11. Cyclica Inc.
  • 19.12. DenovAI Biotech, Inc.
  • 19.13. Exscientia plc
  • 19.14. Generate Biomedicines, Inc.
  • 19.15. Insilico Medicine, Inc.
  • 19.16. Lantern Pharma, Inc.
  • 19.17. Recursion Pharmaceuticals, Inc.
  • 19.18. Relay Therapeutics, Inc.
  • 19.19. Schrodinger, Inc.

LIST OF FIGURES

  • FIGURE 1. GLOBAL AI PROTEIN DESIGN MARKET SIZE, 2018-2032 (USD MILLION)
  • FIGURE 2. GLOBAL AI PROTEIN DESIGN MARKET SHARE, BY KEY PLAYER, 2025
  • FIGURE 3. GLOBAL AI PROTEIN DESIGN MARKET, FPNV POSITIONING MATRIX, 2025
  • FIGURE 4. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 5. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 6. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 7. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 8. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 9. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 10. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY REGION, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 11. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY GROUP, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 12. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2025 VS 2026 VS 2032 (USD MILLION)
  • FIGURE 13. UNITED STATES AI PROTEIN DESIGN MARKET SIZE, 2018-2032 (USD MILLION)
  • FIGURE 14. CHINA AI PROTEIN DESIGN MARKET SIZE, 2018-2032 (USD MILLION)

LIST OF TABLES

  • TABLE 1. GLOBAL AI PROTEIN DESIGN MARKET SIZE, 2018-2032 (USD MILLION)
  • TABLE 2. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 3. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ANTIBODIES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 4. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ANTIBODIES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 5. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ANTIBODIES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 6. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 7. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 8. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 9. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 10. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY HYDROLASES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 11. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY HYDROLASES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 12. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY HYDROLASES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 13. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY OXIDOREDUCTASES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 14. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY OXIDOREDUCTASES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 15. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY OXIDOREDUCTASES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 16. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY TRANSFERASES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 17. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY TRANSFERASES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 18. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY TRANSFERASES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 19. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY PEPTIDES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 20. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY PEPTIDES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 21. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY PEPTIDES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 22. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 23. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY COMPUTATIONAL DESIGN SOFTWARE, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 24. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY COMPUTATIONAL DESIGN SOFTWARE, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 25. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY COMPUTATIONAL DESIGN SOFTWARE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 26. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 27. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 28. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 29. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 30. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DEEP LEARNING, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 31. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DEEP LEARNING, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 32. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DEEP LEARNING, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 33. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY EVOLUTIONARY ALGORITHMS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 34. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY EVOLUTIONARY ALGORITHMS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 35. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY EVOLUTIONARY ALGORITHMS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 36. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY PHYSICS-INFORMED MODELS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 37. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY PHYSICS-INFORMED MODELS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 38. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY PHYSICS-INFORMED MODELS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 39. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY STRUCTURAL BIOINFORMATICS TOOLS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 40. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY STRUCTURAL BIOINFORMATICS TOOLS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 41. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY STRUCTURAL BIOINFORMATICS TOOLS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 42. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 43. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DE NOVO DESIGN, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 44. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DE NOVO DESIGN, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 45. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DE NOVO DESIGN, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 46. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DIRECTED EVOLUTION, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 47. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DIRECTED EVOLUTION, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 48. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DIRECTED EVOLUTION, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 49. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY RATIONAL DESIGN, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 50. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY RATIONAL DESIGN, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 51. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY RATIONAL DESIGN, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 52. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 53. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY AGRICULTURAL TRAIT ENHANCEMENT, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 54. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY AGRICULTURAL TRAIT ENHANCEMENT, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 55. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY AGRICULTURAL TRAIT ENHANCEMENT, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 56. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 57. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 58. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 59. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 60. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY BIOLOGICS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 61. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY BIOLOGICS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 62. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY BIOLOGICS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 63. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY SMALL MOLECULE THERAPEUTICS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 64. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY SMALL MOLECULE THERAPEUTICS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 65. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY SMALL MOLECULE THERAPEUTICS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 66. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY VACCINE DESIGN, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 67. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY VACCINE DESIGN, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 68. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY VACCINE DESIGN, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 69. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY FOOD & BEVERAGE, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 70. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY FOOD & BEVERAGE, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 71. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY FOOD & BEVERAGE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 72. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY INDUSTRIAL ENZYME DEVELOPMENT, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 73. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY INDUSTRIAL ENZYME DEVELOPMENT, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 74. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY INDUSTRIAL ENZYME DEVELOPMENT, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 75. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 76. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ACADEMIC RESEARCH CENTERS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 77. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ACADEMIC RESEARCH CENTERS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 78. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ACADEMIC RESEARCH CENTERS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 79. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY AGROCHEMICAL COMPANIES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 80. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY AGROCHEMICAL COMPANIES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 81. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY AGROCHEMICAL COMPANIES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 82. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY BIOTECHNOLOGY FIRMS, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 83. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY BIOTECHNOLOGY FIRMS, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 84. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY BIOTECHNOLOGY FIRMS, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 85. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 86. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 87. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY PHARMACEUTICAL COMPANIES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 88. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 89. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY CLOUD, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 90. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY CLOUD, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 91. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY CLOUD, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 92. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY HYBRID, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 93. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY HYBRID, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 94. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY HYBRID, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 95. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ON-PREMISES, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 96. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ON-PREMISES, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 97. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY ON-PREMISES, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 98. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY REGION, 2018-2032 (USD MILLION)
  • TABLE 99. AMERICAS AI PROTEIN DESIGN MARKET SIZE, BY SUBREGION, 2018-2032 (USD MILLION)
  • TABLE 100. AMERICAS AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 101. AMERICAS AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 102. AMERICAS AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 103. AMERICAS AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 104. AMERICAS AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 105. AMERICAS AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 106. AMERICAS AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 107. AMERICAS AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 108. AMERICAS AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 109. NORTH AMERICA AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 110. NORTH AMERICA AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 111. NORTH AMERICA AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 112. NORTH AMERICA AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 113. NORTH AMERICA AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 114. NORTH AMERICA AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 115. NORTH AMERICA AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 116. NORTH AMERICA AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 117. NORTH AMERICA AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 118. NORTH AMERICA AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 119. LATIN AMERICA AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 120. LATIN AMERICA AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 121. LATIN AMERICA AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 122. LATIN AMERICA AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 123. LATIN AMERICA AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 124. LATIN AMERICA AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 125. LATIN AMERICA AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 126. LATIN AMERICA AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 127. LATIN AMERICA AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 128. LATIN AMERICA AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 129. EUROPE, MIDDLE EAST & AFRICA AI PROTEIN DESIGN MARKET SIZE, BY SUBREGION, 2018-2032 (USD MILLION)
  • TABLE 130. EUROPE, MIDDLE EAST & AFRICA AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 131. EUROPE, MIDDLE EAST & AFRICA AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 132. EUROPE, MIDDLE EAST & AFRICA AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 133. EUROPE, MIDDLE EAST & AFRICA AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 134. EUROPE, MIDDLE EAST & AFRICA AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 135. EUROPE, MIDDLE EAST & AFRICA AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 136. EUROPE, MIDDLE EAST & AFRICA AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 137. EUROPE, MIDDLE EAST & AFRICA AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 138. EUROPE, MIDDLE EAST & AFRICA AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 139. EUROPE AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 140. EUROPE AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 141. EUROPE AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 142. EUROPE AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 143. EUROPE AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 144. EUROPE AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 145. EUROPE AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 146. EUROPE AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 147. EUROPE AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 148. EUROPE AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 149. MIDDLE EAST AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 150. MIDDLE EAST AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 151. MIDDLE EAST AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 152. MIDDLE EAST AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 153. MIDDLE EAST AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 154. MIDDLE EAST AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 155. MIDDLE EAST AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 156. MIDDLE EAST AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 157. MIDDLE EAST AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 158. MIDDLE EAST AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 159. AFRICA AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 160. AFRICA AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 161. AFRICA AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 162. AFRICA AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 163. AFRICA AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 164. AFRICA AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 165. AFRICA AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 166. AFRICA AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 167. AFRICA AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 168. AFRICA AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 169. ASIA-PACIFIC AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 170. ASIA-PACIFIC AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 171. ASIA-PACIFIC AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 172. ASIA-PACIFIC AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 173. ASIA-PACIFIC AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 174. ASIA-PACIFIC AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 175. ASIA-PACIFIC AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 176. ASIA-PACIFIC AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 177. ASIA-PACIFIC AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 178. ASIA-PACIFIC AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 179. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY GROUP, 2018-2032 (USD MILLION)
  • TABLE 180. ASEAN AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 181. ASEAN AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 182. ASEAN AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 183. ASEAN AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 184. ASEAN AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 185. ASEAN AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 186. ASEAN AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 187. ASEAN AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 188. ASEAN AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 189. ASEAN AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 190. GCC AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 191. GCC AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 192. GCC AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 193. GCC AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 194. GCC AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 195. GCC AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 196. GCC AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 197. GCC AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 198. GCC AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 199. GCC AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 200. EUROPEAN UNION AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 201. EUROPEAN UNION AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 202. EUROPEAN UNION AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 203. EUROPEAN UNION AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 204. EUROPEAN UNION AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 205. EUROPEAN UNION AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 206. EUROPEAN UNION AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 207. EUROPEAN UNION AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 208. EUROPEAN UNION AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 209. EUROPEAN UNION AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 210. BRICS AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 211. BRICS AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 212. BRICS AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 213. BRICS AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 214. BRICS AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 215. BRICS AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 216. BRICS AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 217. BRICS AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 218. BRICS AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 219. BRICS AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 220. G7 AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 221. G7 AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 222. G7 AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 223. G7 AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 224. G7 AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 225. G7 AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 226. G7 AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 227. G7 AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 228. G7 AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 229. G7 AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 230. NATO AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 231. NATO AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 232. NATO AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 233. NATO AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 234. NATO AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 235. NATO AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 236. NATO AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 237. NATO AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 238. NATO AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 239. NATO AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 240. GLOBAL AI PROTEIN DESIGN MARKET SIZE, BY COUNTRY, 2018-2032 (USD MILLION)
  • TABLE 241. UNITED STATES AI PROTEIN DESIGN MARKET SIZE, 2018-2032 (USD MILLION)
  • TABLE 242. UNITED STATES AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 243. UNITED STATES AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 244. UNITED STATES AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 245. UNITED STATES AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 246. UNITED STATES AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 247. UNITED STATES AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 248. UNITED STATES AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 249. UNITED STATES AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 250. UNITED STATES AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)
  • TABLE 251. CHINA AI PROTEIN DESIGN MARKET SIZE, 2018-2032 (USD MILLION)
  • TABLE 252. CHINA AI PROTEIN DESIGN MARKET SIZE, BY PROTEIN TYPE, 2018-2032 (USD MILLION)
  • TABLE 253. CHINA AI PROTEIN DESIGN MARKET SIZE, BY ENZYMES, 2018-2032 (USD MILLION)
  • TABLE 254. CHINA AI PROTEIN DESIGN MARKET SIZE, BY TECHNOLOGY PLATFORM, 2018-2032 (USD MILLION)
  • TABLE 255. CHINA AI PROTEIN DESIGN MARKET SIZE, BY MACHINE LEARNING MODELS, 2018-2032 (USD MILLION)
  • TABLE 256. CHINA AI PROTEIN DESIGN MARKET SIZE, BY APPROACH, 2018-2032 (USD MILLION)
  • TABLE 257. CHINA AI PROTEIN DESIGN MARKET SIZE, BY APPLICATION, 2018-2032 (USD MILLION)
  • TABLE 258. CHINA AI PROTEIN DESIGN MARKET SIZE, BY DRUG DISCOVERY, 2018-2032 (USD MILLION)
  • TABLE 259. CHINA AI PROTEIN DESIGN MARKET SIZE, BY END USER, 2018-2032 (USD MILLION)
  • TABLE 260. CHINA AI PROTEIN DESIGN MARKET SIZE, BY DEPLOYMENT MODE, 2018-2032 (USD MILLION)