한글목차

유럽의 항체 발견용 AI 시장 규모는 2025년 1억 5,380만 달러에서 2035년까지 14억 3,840만 달러에 이를 것으로 예측되며, 2025-2035년 예측 기간 중 CAGR 25.05%로 성장할 전망입니다.

기존의 신약개발 방법은 비용과 시간이 많이 소요되고 실패율이 높다는 제약이 있으며, 이는 유럽 항체 신약개발을 위한 AI 시장의 성장을 가속하는 주요 요인으로 작용하고 있습니다. 딥러닝, 생성형 AI, 항체 특이적 대규모 언어 모델(LLM) 등의 AI 기술은 개발 기간을 크게 단축하고 성공률을 높여 표적 식별, 리드 탐색, 최적화 과정을 혁신하고 있습니다. AI 기술 제공업체, 제약 및 바이오테크놀러지 기업, CRO, 학술 연구기관을 포함한 유럽 생태계에서는 사람의 개입을 최소화한 반복적인 설계, 시험, 최적화 사이클을 실현하기 위해 자율적 신약개발 플랫폼의 채택이 확대되고 있습니다. 클라우드 기반, 컨설팅 주도형, On-Premise형 AI 솔루션은 기업 규모에 관계없이 활용도를 높이고, 멀티오믹스 데이터와 생성형 AI를 통합하여 보다 정확하고 개인화된 항체 치료제 개발을 촉진하고 있습니다. AI 스타트업과 대형 제약사 간의 전략적 제휴와 지역 자금 조달 이니셔티브를 통해 플랫폼의 확장, 임상 검증 및 상용화를 가속화하고 있습니다. 이러한 협력은 유럽의 혁신 촉진, 업무 효율성 향상, 시장 성장 지속에 기여하고 있습니다.

시장 개요

유럽의 항체 신약개발을 위한 AI 시장은 탄탄한 제약 기반, 우수한 학술 연구, 생명과학 분야에서의 AI 활용 확대에 힘입어 차세대 바이오의약품 개발의 주요 동력으로 발전하고 있습니다. 기존의 항체 생성 방식은 개발 기간의 장기화, 고비용, 높은 실패율 등의 문제점이 있어 보다 효과적이고 예측 가능한 기술에 대한 요구가 높아지고 있습니다. 머신러닝, 딥러닝, 생성형 AI, 항체 특화 대규모 언어 모델(LLM) 등 AI 기술로 인해 치료용 항체의 식별, 생성, 최적화는 혁신적으로 변화하고 있습니다.

유럽 전역의 제약-바이오 기업, CRO(위탁연구기관), 연구기관에서는 결합 친화도 예측 정확도 향상, 신약개발 초기 단계에서의 개발 가능성 파라미터 최적화, 표적 분자 식별 정확도 향상을 목적으로 AI 탑재 시스템 도입이 추진되고 있습니다. 특히 종양학, 자가면역질환, 희귀질환에서 AI와 멀티오믹스 데이터, 구조생물학, 하이스루풋 테스트의 통합을 통해 보다 정확한 후보물질 선택과 정밀한 맞춤형 항체 치료제 개발이 가능해졌습니다.

영국, 독일, 프랑스, 스위스 등 유럽의 주요 시장에서는 공공 자금 프로그램, 국경 간 파트너십, 지원적인 혁신 생태계가 AI 도입을 가속화하고 있습니다. 동시에 On-Premise 및 클라우드 기반 AI 기술의 가용성이 높아짐에 따라 기존 생명공학 기업과 대형 제약사 모두 진입 장벽이 낮아지고 있습니다. 이러한 요소들이 결합되어 유럽은 AI 기반 항체 신약 개발의 주요 거점으로 자리매김하고 있으며, 장기적인 시장 확대, R&D 생산성 향상, 지속적인 혁신을 촉진하고 있습니다.

유럽의 항체 신약개발용 AI 시장을 조사했으며, 주요 동향, 시장 영향요인 분석, 법 및 규제 환경, 시장 규모 추이 및 예측, 각종 부문별/지역별/주요 국가별 상세 분석, 경쟁 구도, 주요 기업 개요 등의 정보를 정리하여 전해드립니다.

목차

주요 요약

제1장 시장 : 업계 전망

제2장 지역

제3장 시장 - 경쟁 벤치마킹과 기업 개요

제4장 조사 방법

영문목차

This report can be delivered in 2 working days.

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