歐洲抗體藥物發現人工智慧市場：分析與預測（2025-2035年）

歐洲用於抗體藥物發現的人工智慧市場預計將從 2025 年的 1.538 億美元成長到 2035 年的 14.384 億美元，在預測期（2025-2035 年）內複合年成長率為 25.05%。

傳統藥物研發方法受限於高成本、耗時和高失敗率，而這些正是推動歐洲抗體藥物研發人工智慧市場成長的關鍵因素。深度學習、生成式人工智慧和抗體特異性大規模語言模型（LLM）等人工智慧技術正在革新標靶識別、先導化合物發現和最佳化流程，顯著縮短研發週期並提高成功率。為了在最大限度減少人為干預的情況下實現迭代式設計-測試-最佳化循環，包括人工智慧技術提供者、製藥和生物技術公司、合約研究組織（CRO）以及學術研究機構在內的歐洲生態系統正日益採用自主藥物研發平台。基於雲端、諮詢主導和本地部署的人工智慧解決方案正變得越來越普及，各種規模的公司都能輕鬆使用。同時，生成式人工智慧與多組體學資料的整合也促進了更精準、更個人化的抗體療法的開發。Start-Ups與大型製藥企業舉措的策略合作和區域性資金籌措也在加速平台規模化、臨床檢驗和商業化進程。這些合作有助於促進創新、提高營運效率並維持歐洲市場的成長。

市場概覽

歐洲抗體藥物研發領域的人工智慧市場正蓬勃發展，成為下一代生物製藥開發的關鍵驅動力。這得歸功於該地區強大的醫藥基礎設施、卓越的學術研究以及人工智慧在生命科學領域日益成長的應用。傳統的抗體發現方法有研發週期長、成本高、失敗率高等問題，因此亟需更有效率、更具預測性的技術。機器學習、深度學習、生成式人工智慧以及抗體特異性大規模語言模型（LLM）等人工智慧技術正在革新治療性抗體的識別、建構和最佳化。

歐洲各地的製藥和生物技術公司、受託研究機構(CRO) 以及研究機構正擴大採用人工智慧系統來提高結合親和性預測的準確性，最佳化早期藥物發現階段的可開發性參數，並改善標靶識別。尤其是在腫瘤學、自體免疫疾病和罕見疾病領域，人工智慧與多體學數據、結構生物學和高通量檢測的整合，能夠實現更精準的候選藥物篩選，並開發出精準的客製化抗體療法。

在包括英國、德國、法國和瑞士在內的歐洲主要市場，公共資助計畫、跨境夥伴關係以及完善的創新生態系統正在加速人工智慧的普及應用。同時，本地部署和雲端人工智慧技術的日益普及降低了成熟生物技術公司和大型製藥公司的進入門檻。這些因素共同作用，使歐洲成為人工智慧驅動抗體藥物研發的領先中心，促進市場長期擴張、研發效率提升和持續創新。

本報告調查了歐洲人工智慧在抗體藥物發現領域的市場，並總結了關鍵趨勢、市場影響因素分析、法律制度、市場規模趨勢和預測、按各個細分市場、地區/主要國家進行的詳細分析、競爭格局以及主要企業的概況。

目錄

執行摘要

範圍和定義

第1章 市場：產業展望

• 市場概覽
  • 對下一代生物製藥的需求快速成長
  • 利用人工智慧在抗體發現領域實現個人化精準醫療
• 市場趨勢
  • 採用抗體特異性大規模語言模型（LLM）
  • 策略聯盟和增加投資
• 監管狀態/合規性
  • 歐洲聯盟
• 定價分析
• 實施策略
  • 人工智慧驅動的生物標記和伴隨診斷整合
  • 利用策略夥伴關係
• 市場動態
  • 促進因素、挑戰和機會：評估當前和未來的影響
  • 市場促進因素
  • 市場挑戰
  • 市場機遇

第2章 區域

• 區域概況
• 歐洲
  • 區域概覽
  • 市場成長促進因素
  • 市場問題
  • 市場規模及預測
  • 按國家/地區
  • 市場規模及預測
  • 市場規模及預測
  • 市場規模及預測
  • 市場規模及預測
  • 市場規模及預測
  • 市場規模及預測

3. 市場－競爭標竿分析與公司概況

• 主要策略和發展（按公司分類）
  • 資金籌措活動
  • 夥伴關係、合作與業務拓展
• 公司簡介
  • LabGenius Therapeutics
  • Antiverse
  • EVQLV Inc.
  • MAbSilico
  • Cradle Bio BV

第4章調查方法

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Introduction to Europe AI in Antibody Discovery Market

The Europe AI in antibody discovery market is projected to reach $1,438.4 million by 2035 from $153.8 million in 2025, growing at a CAGR of 25.05% during the forecast period 2025-2035. The constraints of traditional discovery methods, which are expensive, time-consuming, and marked by high failure rates, are the main factor driving growth in the European AI in antibody discovery market. By drastically cutting down on development times and increasing success rates, AI-enabled technologies like deep learning, generative AI, and antibody-specific large language models (LLMs) are revolutionizing target identification, lead discovery, and optimization. In order to facilitate iterative design-test-optimize cycles with little human interaction, the European ecosystem-which includes AI technology providers, pharmaceutical and biotechnology businesses, CROs, and academic research institutions-is progressively using autonomous discovery platforms. While cloud-based, consulting-led, and on-premise AI solutions are increasing accessibility across enterprises of different sizes, generative AI integration with multi-omics data is facilitating the creation of more accurate and customized antibody therapies. Platform scale-up, clinical validation, and commercialization are being accelerated by strategic partnerships and regional funding initiatives between AI startups and well-established pharmaceutical companies. Together, these partnerships are fostering innovation, enhancing operational efficiency, and sustaining market growth in Europe.

Market Introduction

The Europe AI in antibody discovery market is developing as a major enabler of next-generation biologics development, owing to the region's strong pharmaceutical foundation, superior academic research, and growing incorporation of artificial intelligence into life science. There is a great need for more effective and predictive techniques because traditional antibody discovery methods are frequently limited by lengthy development durations, expensive costs, and high attrition rates. The identification, creation, and optimization of therapeutic antibodies are being revolutionized by AI technologies such as machine learning, deep learning, generative AI, and antibody-specific large language models (LLMs).

AI-powered systems are being adopted by pharmaceutical and biotechnology businesses, contract research organizations (CROs), and research institutes around Europe in order to improve binding affinity prediction, optimize developability parameters early in the discovery phase, and improve target identification. More precise candidate selection and the advancement of precision and customized antibody therapeutics are made possible by the integration of AI with multi-omics data, structural biology, and high-throughput testing, especially in oncology, autoimmune, and uncommon illnesses.

Public financing programs, cross-border partnerships, and supportive innovation ecosystems are speeding up the adoption of AI in important European markets like the UK, Germany, France, and Switzerland. Simultaneously, the availability of on-premise and cloud-based AI technologies is lowering entry hurdles for both established biotech enterprises and major pharmaceutical companies. Together, these elements are establishing Europe as a key center for AI-driven antibody discovery, promoting long-term market expansion, increased R&D productivity, and continuous innovation.

Europe AI in Antibody discovery Market Trends, Drivers and Challenges

Market Trends

Growing adoption of AI-led discovery platforms

• Faster early-stage lead identification using machine learning and computational antibody design.
• Increased use of predictive models for binding, developability and immunogenicity to shorten discovery cycles.
• Hybrid workflows combining in-silico design with automated wet-lab validation.

Cross-sector collaboration & ecosystem building

• Startups, pharma, and academic labs forming partnerships and licensing agreements.
• Regional clusters and consortia enabling shared tools, pilot programs, and talent exchange.
• Rising contract research and platform partnerships that accelerate commercialisation.

Expansion of personalized & precision therapies

• AI used to design antibodies tailored to specific targets, patient subgroups, and complex epitope profiles.
• Growing focus on oncology, autoimmune, and rare-disease biologics that benefit from rapid candidate optimization.
• Increased interest in bispecifics, antibody-drug conjugates and other engineered modalities supported by computational design.

Key Market Drivers

Strong biopharma R&D infrastructure

• Established pharma and biotech hubs provide scientific depth and ready adoption pathways for AI tools.
• Presence of advanced lab facilities and translational pipelines expedites moving in-silico hits to experiments.

Supportive funding and innovation programs

• Public and private funding initiatives targeting biotech and health-tech innovation.
• Grants and collaborative research programs that de-risk early AI-biotech projects.

Demand for faster, cost-effective discovery

• Need to reduce long timelines and high attrition in traditional antibody discovery.
• Cost pressures and competitive pipelines push companies to integrate AI for efficiency gains.

Major Challenges

Regulatory & compliance complexity

• Strict data-privacy and emerging AI regulations raise compliance overhead.
• Difficulty validating AI predictions to meet drug-development regulatory expectations.

Data limitations & quality barriers

• Scarcity of large, standardized, high-quality labeled datasets across targets and modalities.
• Proprietary, fragmented data and inconsistent annotations reduce model generalizability.

Investment & commercialization gaps

• Relatively cautious investment climate for deep computational biotech compared with other regions.
• Challenges scaling academic prototypes into robust, enterprise-grade platforms.

Talent & infrastructure constraints

• Shortage of professionals who combine AI, structural biology, and immunology expertise.
• High capital and operational costs for compute infrastructure (HPC/cloud) limit uptake by smaller players.

How can this report add value to an organization?

Product/Innovation: This report enables organizations to identify high-value opportunities in Europe AI in antibody discovery market, including generative AI, autonomous platforms, and antibody-specific LLMs. It guides R&D investment decisions, pipeline optimization, and technology adoption, helping companies prioritize initiatives that accelerate lead identification and antibody optimization. The report provides actionable insights on platform scalability, wet lab integration, and predictive modelling accuracy, allowing stakeholders to reduce development costs, improve success rates, and maintain a competitive advantage in the rapidly evolving antibody discovery market.

Growth/Marketing: The report delivers in-depth insights into regional adoption trends, emerging markets, and partnership opportunities, supporting strategic market entry and commercialization planning. It enables companies to identify growth potential across technology, solution, application, and end-user segments. By understanding regional R&D investments, regulatory frameworks, and technology adoption rates, organizations can refine marketing, licensing, and collaboration strategies, maximize visibility, and increase return on investment in a competitive European landscape.

Competitive: This report provides comprehensive company profiling, competitive benchmarking, highlighting strategic collaborations, funding activities, mergers, acquisitions, and technology adoption trends. Stakeholders gain a clear understanding of competitor focus areas, R&D priorities, and market positioning. This intelligence allows organizations to identify gaps, anticipate market shifts, and formulate strategies to differentiate themselves, optimize market entry, and maintain leadership in the Europe AI-driven antibody discovery ecosystem.

Key Market Players and Competitive Landscape

The Europe AI in antibody discovery market is characterized by a highly competitive and evolving landscape, with participation from innovative biotechnology startups, established pharmaceutical companies, and AI technology providers. Key players include:

• LabGenius Therapeutics
• Antiverse
• EVQLV, Inc.
• MAbsillco
• Cradle Bio B.V.

Table of Contents

Executive Summary

Scope and Definition

1 Market: Industry Outlook

• 1.1 Market Overview
  • 1.1.1 Surging Demand for Next-Generation Biologics
  • 1.1.2 Leveraging AI for Personalized Precision Medicine in Antibody Discovery
• 1.2 Market Trends
  • 1.2.1 Adoption of Antibody-Specific Large Language Models (LLMs)
  • 1.2.2 Increasing Strategic Collaborations and Investments
• 1.3 Regulatory Landscape / Compliance
  • 1.3.1 E.U.
    • 1.3.1.1 France
    • 1.3.1.2 Italy
• 1.4 Pricing Analysis
• 1.5 Implementation Strategies
  • 1.5.1 AI-Driven Biomarker and Companion Diagnostic Integration
  • 1.5.2 Leveraging Strategic Partnerships
• 1.6 Market Dynamics
  • 1.6.1 Drivers, Challenges, and Opportunities: Current and Future Impact Assessment, 2024-2035
  • 1.6.2 Market Drivers
    • 1.6.2.1 High Attrition Rates and Costs Associated with Traditional Antibody Discovery Methods
    • 1.6.2.2 AI Integration with Wet Labs Accelerating Antibody Discovery
  • 1.6.3 Market Challenges
    • 1.6.3.1 Data Bottlenecks Hindering Innovation in AI-Enabled Antibody Discovery
    • 1.6.3.2 Validation Gap in AI-Driven Antibody Discovery
  • 1.6.4 Market Opportunities
    • 1.6.4.1 Generative AI and Deep Learning for Novel Antibody Design
    • 1.6.4.2 Autonomous Discovery Platforms and AI Agents
    • 1.6.4.3 Establishing Antibody Data Foundries and Collaborative Networks

2 Region

• 2.1 Regional Summary
• 2.2 Europe
  • 2.2.1 Regional Overview
  • 2.2.2 Driving Factors for Market Growth
  • 2.2.3 Factors Challenging the Market
  • 2.2.4 Market Sizing and Forecast
  • 2.2.5 By Country
    • 2.2.5.1 U.K.
  • 2.2.6 Market Sizing and Forecast
    • 2.2.6.1 Germany
  • 2.2.7 Market Sizing and Forecast
    • 2.2.7.1 France
  • 2.2.8 Market Sizing and Forecast
    • 2.2.8.1 Italy
  • 2.2.9 Market Sizing and Forecast
    • 2.2.9.1 Spain
  • 2.2.10 Market Sizing and Forecast
    • 2.2.10.1 Rest-of-Europe
  • 2.2.11 Market Sizing and Forecast

3 Markets - Competitive Benchmarking & Company Profiles

• 3.1 Key Strategies and Developments (by Company)
  • 3.1.1 Funding Activities
  • 3.1.2 Partnerships, Collaborations, and Business Expansions
• 3.2 Company Profiles
  • 3.2.1 LabGenius Therapeutics
    • 3.2.1.1 Overview
    • 3.2.1.2 Top Products/Product Portfolio
    • 3.2.1.3 Top Competitors
    • 3.2.1.4 Target Customers
    • 3.2.1.5 Key Personal
    • 3.2.1.6 Analyst View
  • 3.2.2 Antiverse
    • 3.2.2.1 Overview
    • 3.2.2.2 Top Products/Product Portfolio
    • 3.2.2.3 Top Competitors
    • 3.2.2.4 Target Customers
    • 3.2.2.5 Key Personal
    • 3.2.2.6 Analyst View
  • 3.2.3 EVQLV Inc.
    • 3.2.3.1 Overview
    • 3.2.3.2 Top Products/Product Portfolio
    • 3.2.3.3 Top Competitors
    • 3.2.3.4 Target Customers
    • 3.2.3.5 Key Personal
    • 3.2.3.6 Analyst View
  • 3.2.4 MAbSilico
    • 3.2.4.1 Overview
    • 3.2.4.2 Top Products/Product Portfolio
    • 3.2.4.3 Top Competitors
    • 3.2.4.4 Target Customers
    • 3.2.4.5 Key Personal
    • 3.2.4.6 Analyst View
  • 3.2.5 Cradle Bio B.V.
    • 3.2.5.1 Overview
    • 3.2.5.2 Top Products/Product Portfolio
    • 3.2.5.3 Top Competitors
    • 3.2.5.4 Target Customers
    • 3.2.5.5 Key Personal
    • 3.2.5.6 Analyst View

4 Research Methodolgy

• 4.1 Data Sources
  • 4.1.1 Primary Data Sources
  • 4.1.2 Secondary Data Sources
  • 4.1.3 Data Triangulation
• 4.2 Market Estimation and Forecast

List of Figures

• Figure 1: Europe AI in Antibody Discovery Market (by Scenario), $Million, 2024, 2030, and 2035
• Figure 2: Market Snapshot, 2024
• Figure 3: AI in Antibody Discovery Market, $Million, 2024 and 2035
• Figure 4: AI application across the Antibody Discovery Workflow
• Figure 5: Advanced Antibody Design and Optimization
• Figure 6: Europe AI in Antibody Discovery Market, $Million, 2024-2035
• Figure 7: U.K. AI in Antibody Discovery Market, $Million, 2024-2035
• Figure 8: Germany AI in Antibody Discovery Market, $Million, 2024-2035
• Figure 9: France AI in Antibody Discovery Market, $Million, 2024-2035
• Figure 10: Italy AI in Antibody Discovery Market, $Million, 2024-2035
• Figure 11: Spain AI in Antibody Discovery Market, $Million, 2024-2035
• Figure 12: Rest-of-Europe AI in Antibody Discovery Market, $Million, 2024-2035
• Figure 13: Data Triangulation
• Figure 14: Top-Down and Bottom-Up Approach
• Figure 15: Assumptions and Limitations

List of Tables

• Table 1: Market Snapshot
• Table 2: Competitive Landscape Analysis
• Table 3: Companies Involved in Funding and Collaboration
• Table 4: Leading Platforms and their Pricing Model
• Table 5: AI in Antibody Discovery Market (by Region), $Million, 2024-2035