欧洲抗体药物发现人工智慧市场：分析与预测（2025-2035年）

欧洲用于抗体药物发现的人工智慧市场预计将从 2025 年的 1.538 亿美元增长到 2035 年的 14.384 亿美元，在预测期（2025-2035 年）内复合年增长率为 25.05%。

传统药物研发方法受限于高成本、耗时和高失败率，而这些正是推动欧洲抗体药物研发人工智慧市场成长的关键因素。深度学习、生成式人工智慧和抗体特异性大规模语言模型（LLM）等人工智慧技术正在革新标靶识别、先导化合物发现和优化流程，显着缩短研发週期并提高成功率。为了在最大限度减少人为干预的情况下实现迭代式设计-测试-优化循环，包括人工智慧技术提供者、製药和生物技术公司、合约研究组织（CRO）以及学术研究机构在内的欧洲生态系统正日益采用自主药物研发平台。基于云端、咨询主导和本地部署的人工智慧解决方案正变得越来越普及，各种规模的公司都能轻鬆使用。同时，生成式人工智慧与多组体学资料的整合也促进了更精准、更个人化的抗体疗法的开发。Start-Ups与大型製药企业倡议的策略合作和区域性资金筹措也在加速平台规模化、临床检验和商业化进程。这些合作有助于促进创新、提高营运效率并维持欧洲市场的成长。

市场概览

欧洲抗体药物研发领域的人工智慧市场正蓬勃发展，成为下一代生物製药开发的关键驱动力。这得归功于该地区强大的医药基础设施、卓越的学术研究以及人工智慧在生命科学领域日益增长的应用。传统的抗体发现方法有研发週期长、成本高、失败率高等问题，因此亟需更有效率、更具预测性的技术。机器学习、深度学习、生成式人工智慧以及抗体特异性大规模语言模型（LLM）等人工智慧技术正在革新治疗性抗体的识别、建构和优化。

欧洲各地的製药和生物技术公司、受託研究机构(CRO) 以及研究机构正越来越多地采用人工智慧系统来提高结合亲和性预测的准确性，优化早期药物发现阶段的可开发性参数，并改善标靶识别。尤其是在肿瘤学、自体免疫疾病和罕见疾病领域，人工智慧与多体学数据、结构生物学和高通量检测的整合，能够实现更精准的候选药物筛选，并开发出精准的客製化抗体疗法。

在包括英国、德国、法国和瑞士在内的欧洲主要市场，公共资助计画、跨境伙伴关係以及完善的创新生态系统正在加速人工智慧的普及应用。同时，本地部署和云端人工智慧技术的日益普及降低了成熟生物技术公司和大型製药公司的进入门槛。这些因素共同作用，使欧洲成为人工智慧驱动抗体药物研发的领先中心，促进市场长期扩张、研发效率提升和持续创新。

本报告调查了欧洲人工智慧在抗体药物发现领域的市场，并总结了关键趋势、市场影响因素分析、法律制度、市场规模趋势和预测、按各个细分市场、地区/主要国家进行的详细分析、竞争格局以及主要企业的概况。

目录

执行摘要

范围和定义

第一章 市场：产业展望

• 市场概览
  • 对下一代生物製药的需求快速成长
  • 利用人工智慧在抗体发现领域实现个人化精准医疗
• 市场趋势
  • 采用抗体特异性大规模语言模型（LLM）
  • 策略联盟和增加投资
• 监管状态/合规性
  • 欧洲联盟
• 定价分析
• 实施策略
  • 人工智慧驱动的生物标记和伴随诊断整合
  • 利用策略伙伴关係
• 市场动态
  • 驱动因素、挑战和机会：评估当前和未来的影响
  • 市场驱动因素
  • 市场挑战
  • 市场机会

第二章 区域

• 区域概况
• 欧洲
  • 区域概览
  • 市场成长驱动因素
  • 市场问题
  • 市场规模及预测
  • 按国家/地区
  • 市场规模及预测
  • 市场规模及预测
  • 市场规模及预测
  • 市场规模及预测
  • 市场规模及预测
  • 市场规模及预测

3. 市场－竞争标竿分析与公司概况

• 主要策略和发展（按公司划分）
  • 资金筹措活动
  • 伙伴关係、合作与业务拓展
• 公司简介
  • LabGenius Therapeutics
  • Antiverse
  • EVQLV Inc.
  • MAbSilico
  • Cradle Bio BV

第四章调查方法

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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