세계의 항체 탐색용 AI 시장 규모는 2024년 약 4억 1,040만 달러로 평가되었고, 2035년까지 약 48억 4,310만 달러로 성장할 전망이며, CAGR 약 24.76%로 성장이 예측됩니다.
이 급성장의 배경에는 기존의 항체 창약 수법이 안고 있는 고비용, 긴 개발 기간, 높은 실패율 등의 한계가 있습니다. 딥러닝, 생성형 AI, 항체 특이적인 대규모 언어 모델(LLM) 등의 AI 기술은 표적 식별, 리드 발견, 최적화 프로세스를 변화시키고 개발 기간을 대폭 단축하며 성공률을 향상시키고 있습니다.
| 주요 시장 통계 | |
|---|---|
| 예측 기간 | 2025-2035년 |
| 평가(2025년) | 5억 2,990만 달러 |
| 예측(2035년) | 48억 4,310만 달러 |
| CAGR | 24.76% |
세계의 항체 탐색용 AI 시장은 AI 기술 공급자, 제약 및 바이오테크놀러지 기업, 수탁연구기관(CRO), 학술기관 및 연구기관으로 구성되어 있습니다. 각 조직에서는 자율형 창약 플랫폼의 활용이 진행되어, 인적 개입을 최소한으로 억제한 반복적인 설계, 시험 및 최적화의 사이클을 실현하고 있습니다. 생성형 AI와 멀티오믹스 통합은 개별화 항체 치료제의 개발을 촉진하고, 클라우드 기반 솔루션, 컨설팅 서비스, 온프레미스 AI 도구는 접근성을 확대하고 있습니다. AI 스타트업과 기존 제약기업 간의 전략적 제휴 및 자금 제공은 플랫폼의 스케일 업, 임상 검증, 상업화를 가속화하고 있습니다. 이러한 요인들이 함께 급속한 혁신을 추진하고, 업무 효율성을 향상시키며, 시장을 지속적인 성장으로 자리매김하고 있습니다.
기술별로는 구조 예측 부문이 항체 접힘, 3D 구조, 안정성을 정확하게 모델링하는 데 있어서 매우 중요한 역할을 담당하기 때문에 시장을 견인할 것으로 예상되고 있습니다. 정밀한 구조 예측을 가능하게 함으로써, 제약 회사 및 생명 공학 기업은 실험 사이클을 단축하고, 결합 친화성을 향상시키며, 효과적인 항체 발견을 가속화할 수 있습니다. 에피토프 매핑 및 데노보 설계와 같은 다른 기술은 후보 선택을 정교하게 하고 치료 효과의 잠재력을 최적화하여 구조 예측을 보완합니다.
솔루션별로, AI 소프트웨어 플랫폼 부서는 예측 모델링, 생성형 AI, 습식 실험실과의 호환성을 통합한 항체 발견을 위한 종합적인 프레임워크를 제공하기 때문에 시장을 선도할 것으로 예측됩니다. 그 확장성, 범용성, 연구 워크플로우를 가속화하는 능력은 세계 제약회사와 생명공학 기업에 최적의 선택이 되고 있습니다. 클라우드 기반, 온프레미스 도구 및 컨설팅 서비스는 다양한 연구 환경에서의 도입을 지원합니다.
용도별로는 표적 식별 부서가 치료와 관련된 항원을 선택하기 위한 기초가 되기 때문에 시장을 견인할 것으로 예측됩니다. AI를 활용한 표적 예측은 정밀도를 향상시키고, 발견까지의 시간을 단축하며, 하류의 항체 개발 효율을 높입니다. 리드 발견 및 최적화는 후보 물질을 더욱 정교하게 하여 효능, 안정성 및 면역원성을 향상시킵니다.
최종 사용자별로는 제약, 생명공학, 플랫폼 개발 기업 부문이 연구개발 가속화, 파이프라인 최적화, 신규 항체 시장 투입을 목적으로 한 AI 대응 플랫폼의 도입률이 높기 때문에 시장을 견인할 것으로 예측됩니다. CRO와 학술 기관은 특히 초기 단계의 창약과 검증에 크게 기여하고 있습니다.
지역별로 북미는 확립된 의료 인프라, 엄청난 연구 개발 투자, 강력한 규제 지원, 항체 탐색용 AI 플랫폼의 조기 도입으로 시장을 선도하고 있습니다. 유럽은 견고한 연계 및 임상 도입이 진행된 성숙 시장입니다. 아시아태평양에서는 생명공학 분야의 연구개발 증가, 신흥 AI 기업의 상승, 의료 접근성 개선 등 급속한 성장이 나타납니다. 라틴아메리카, 중동 및 아프리카를 포함한 세계 기타 지역에서는 의료 및 생명 공학 분야에 대한 투자 증가로 추가 성장 기회가 제공됩니다.
본 보고서에서는 세계의 항체 탐색용 AI 시장을 조사했으며, 주요 동향, 시장 영향요인 분석, 법규제 환경, 파이프라인 분석, 시장 규모 추이 및 예측, 각종 구분, 지역 및 주요 국가별 상세 분석, 경쟁 구도, 주요 기업 프로파일 등을 정리했습니다.
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The global AI in antibody discovery market was valued at approximately $410.4 million in 2024 and is projected to grow $4,843.1 million by 2035, at a CAGR of around 24.76%. Growth is driven by the limitations of conventional antibody discovery methods, which are costly, time-intensive, and prone to high failure rates. AI-enabled technologies, including deep learning, generative AI, and antibody-specific large language models (LLMs), are transforming the process of target identification, lead discovery, and optimization, significantly reducing development timelines and improving success rates.
| KEY MARKET STATISTICS | |
|---|---|
| Forecast Period | 2025 - 2035 |
| 2025 Evaluation | $529.9 Million |
| 2035 Forecast | $4,843.1 Million |
| CAGR | 24.76% |
The global AI in antibody discovery market encompasses AI technology providers, pharmaceutical and biotechnology companies, contract research organizations (CROs), and academic and research institutions. Organizations are increasingly leveraging autonomous discovery platforms to enable iterative design-test-optimize cycles with minimal human intervention. Generative AI and multi-omics integration are facilitating personalized antibody therapeutics, while cloud-based solutions, consulting services, and on-premise AI tools expand accessibility. Strategic collaborations and funding between AI startups and established pharmaceutical companies accelerate platform scale-up, clinical validation, and commercialization. Collectively, these factors are driving rapid innovation, improving operational efficiency, and positioning the market for sustained growth.
Global AI in Antibody Discovery Market Lifecycle Stage
The global AI in antibody discovery market is in the expansion phase, characterized by rapid adoption, substantial investment, and strategic collaborations between innovative startups and established pharmaceutical companies. High attrition rates and the escalating costs associated with traditional antibody discovery have catalysed the transition toward AI-enabled platforms. Integration of AI with wet lab experimentation enhances efficiency, reduces failure rates, and supports iterative optimization cycles. The use of antibody-specific LLMs improves predictive accuracy for structure, binding affinity, and immunogenicity, increasing the probability of successful antibody candidates.
Despite these supporting factors, several challenges remain, including data bottlenecks from limited high-quality datasets and validation gaps requiring experimental confirmation. However, the market presents substantial growth opportunities through generative AI, autonomous discovery platforms, and multi-omics integration, enabling the design of novel antibodies and personalized therapeutics. Expansion into emerging regions with increasing healthcare investments, supportive regulatory frameworks, and growing R&D initiatives further positions the market for sustained growth. Stakeholders are increasingly focusing on technological advancement, strategic alliances, and scalable solutions to optimize antibody discovery efficiency and maintain a competitive advantage.
Market Segmentation:
Segmentation 1: By Technology
Structure Prediction is expected to lead the market due to its critical role in accurately modelling antibody folding, 3D structures, and stability. By enabling precise structural predictions, pharmaceutical and biotechnology companies can reduce experimental cycles, improve binding affinity, and accelerate the discovery of effective antibodies. Other technologies, such as epitope mapping and de novo design, complement structure prediction by refining candidate selection and optimizing therapeutic potential.
Segmentation 2: By Solution
AI Software Platforms are projected to lead the market as they provide comprehensive frameworks for antibody discovery, integrating predictive modelling, generative AI, and wet lab compatibility. Their scalability, versatility, and ability to accelerate research workflows make them the preferred choice for pharmaceutical and biotechnology companies globally. Cloud-based and on-premise tools, along with consulting services, support adoption across diverse research settings.
Segmentation 3: By Application
Target Identification is expected to lead the application segment because it is fundamental for selecting therapeutically relevant antigens. AI-driven target prediction improves precision, reduces discovery timelines, and enhances downstream antibody development efficiency. Lead discovery and optimization further refine candidates to improve efficacy, stability, and immunogenicity.
Segmentation 4: By End User
Pharmaceutical, biotechnology, and platform developing companies are expected to lead the market due to their high adoption of AI-enabled platforms to accelerate R&D, optimize pipelines, and bring novel antibodies to market. CROs and academic institutes also contribute significantly, particularly in early-stage discovery and validation.
Segmentation 5: By Region
North America leads the market with a well-established healthcare infrastructure, significant R&D investment, strong regulatory support, and early adoption of AI platforms in antibody discovery. Europe represents a mature market with robust collaborations and clinical adoption. The Asia-Pacific region is witnessing rapid growth driven by increasing biotech R&D, emerging AI companies, and improving healthcare access. Rest-of-the-world regions, including Latin America, the Middle East, and Africa, offer additional growth opportunities due to rising investments in healthcare and biotechnology.
Demand - Drivers and Limitations
Demand drivers for the global AI in antibody discovery market:
Limitations for the global AI in antibody discovery market:
How can this report add value to an organization?
Product/Innovation: This report enables organizations to identify high-value opportunities in global 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 global 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 AI-driven antibody discovery ecosystem.
Key Market Players and Competitive Landscape
The global 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:
Competitive benchmarking highlights mergers and acquisitions, funding activities, licensing deals, and collaborations with pharmaceutical organizations, reflecting the growing emphasis on innovation and speed-to-market. Companies are evaluated based on market presence, technological capabilities, strategic initiatives, and product portfolios. Detailed company profiles cover target end users, technological focus areas, and expert analyst perspectives, providing stakeholders with actionable insights into investment opportunities, market positioning, and strategic growth pathways. This intelligence enables organizations to effectively align their R&D and commercialization strategies with emerging market dynamics of the global AI in antibody discovery market.
Scope and Definition