항체 발견용 AI 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the aI in antibody discovery market size is expected to grow from USD 0.5 billion in 2025 to USD 0.62 billion in 2026 and is forecast to reach USD 1.88 billion by 2031 at 24.61% CAGR over 2026-2031.

This report is Segmented by Offering (Software Platforms, and More), Technology (Structure Prediction, and More), Application (De Novo Antibody Design, and More), Antibody Modality (Monoclonal Antibodies, and More), Therapeutic Area (Oncology, and More), End User (Pharmaceutical Companies, and More), and Geography (North America, and More). The Market Forecasts are Provided in Terms of Value (USD).

Global AI In Antibody Discovery Market Trends and Insights

Conventional Discovery Economics Force a Platform Pivot

The AI in antibody discovery market is gaining force because the cost burden of conventional antibody discovery has become difficult to justify in a tighter capital environment. Absci reported that AI-guided programs can lower per-program investment from more than USD 50 million to nearly USD 15 million and reduce development time from 4-6 years to roughly 2 years. Preclinical attrition has historically exceeded 90%, which means most early discovery spending has not translated into clinical return. The pressure is even stronger in early target classes such as GPCRs, ion channels, and multi-pass membrane proteins, where cryo-EM throughput still limits the structural information available for conventional studies. In the AI in antibody discovery market, that creates a practical first-mover window for platforms that can close difficult programs before traditional competitors fully characterize the antigen. It also explains why buyers are moving from narrow software use toward broader platform relationships that can carry more discovery risk and more program ownership.

Rising Demand for Next-Generation Biologics and Precision Medicine Expands AI's Design Surface

The market is also being lifted by a clear pipeline shift toward more complex biologics and toward precision medicine use cases in oncology and immunology. By early 2025, more than 12 bispecific antibodies had received regulatory approval worldwide, and more than 200 bispecifics were in active clinical trials across oncology and immunology. That pipeline mix expands the design problem beyond what traditional screening can manage efficiently, because multiple binding arms must be optimized at the same time for affinity, pharmacokinetics, and manufacturability. In the AI in antibody discovery market, precision medicine demand is reinforcing that shift because newer programs are targeting more selective cytokine receptors, co-stimulatory pathways, and immunology adjacencies that older methods handled poorly. Platforms that lack native multispecific design capability are therefore at risk of being excluded from the fastest-growing parts of future deal flow.

Data Scarcity Structurally Limits Model Generalization

The market still faces a basic data problem, because public structural coverage remains too thin for broad generalization across target classes. Frontiers in Immunology noted that the Protein Data Bank contained fewer than 10,000 publicly available antibody structures and close to 2,000 VHH complexes across nearly 800 unique antigens as of May 2025. That scarcity is uneven, because common oncology targets have richer annotations while GPCR, ion-channel, and rare-disease antigens remain underrepresented. A November 2025 benchmark from the University of Tokyo showed that AlphaFold3 achieved only an 11% success rate at DockQ >= 0.80 for antibody-antigen complexes, which underlines how difficult heterodimer geometry still is for public models. In the AI in antibody discovery market, this makes proprietary dataset generation through wet-lab campaigns more valuable than simple model scaling. It also raises the cost of compute, increases the premium for cross-disciplinary talent, and makes patent enablement harder when physical validation trails digital design.

Other drivers and restraints analyzed in the detailed report include:

1. AI Opens GPCR and Ion-Channel Antibody Programs
2. Antibody-Specific Foundation Models Improve Hit Quality and Pull More Collaboration Spend
3. Wet-Lab Triage Bottleneck Constrains Throughput Gains

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software Platforms accounted for 38.31% of the AI in antibody discovery market share in 2025, while Discovery Services is projected to expand at a 26.38% CAGR through 2031. That mix shows how the market first commercialized through tools and interfaces, then began moving toward contracts that tie payment more closely to program delivery. Discovery Services is benefiting from buyers who prefer to shift discovery risk onto platform partners rather than build large internal stacks. The AI in antibody discovery market is therefore moving away from simple tool access and toward fuller outsourcing models that include target work, sequence generation, and validation support. Bayer said Cradle was being deployed across 6 of the top 25 global pharma companies and more than 50 active R&D programs as of January 2026, which supports the view that deeper service relationships are becoming more durable than license-only arrangements.

Integrated Platform Partnerships and Data and Model Licensing remain smaller today, but both carry strategic weight because they change how value is captured. The first model spreads upside through milestone-linked co-development structures, which can align platform incentives more closely with partner outcomes. The second model monetizes curated datasets and model weights as standalone intellectual property, which gives the AI in antibody discovery market a revenue layer that traditional CRO models did not offer. As foundation model leadership becomes harder to displace, data and model licensing may emerge as the highest-margin part of the commercial stack.

Structure Prediction held 33.24% of the AI in antibody discovery market size in 2025, while Generative AI and Protein Language Models is set to grow at a 28.52% CAGR through 2031. That revenue split reflects the fact that structure prediction remains the base layer for nearly every downstream design and optimization task. It is still the most established technical entry point, especially for teams that are adding AI into existing discovery workflows rather than rebuilding those workflows from the start. The AI in antibody discovery market is now shifting beyond prediction, though, because the main question is no longer just what structure exists but what sequence should be created. Chai-2's disclosed 20% hit rate versus a conventional baseline near 0.1% shows why generative approaches are resetting performance expectations.

Machine Learning and Deep Learning still hold meaningful commercial value in lead optimization, developability scoring, and ranking tasks where buyers want measured gains without changing the full process. Natural Language Processing and Knowledge Graphs are also building a narrower but useful role in target relationship mapping and literature synthesis. Closed-loop AI with Lab Automation remains the most disruptive long-run technology layer because it couples design and assay cycles into one system rather than treating them as separate steps. In the AI in antibody discovery market, that changes the cost structure by shifting the bottleneck from human analysis toward reagent flow, robotic throughput, and lab orchestration.

Geography Analysis

North America held 43.44% of the AI in antibody discovery market share in 2025, which kept it in the lead across regional demand. The region benefits from a dense group of AI-native biotechs, experienced capital allocators, and public science funding that supports protein engineering and machine learning work. That combination gives the AI in antibody discovery market a strong commercial center in North America because buyers, funders, and technical partners are already concentrated in the same ecosystem. It also supports multi-round partnership behavior, where pharma companies commit to longer co-development structures instead of testing AI through small pilot contracts only. North America therefore remains the main proving ground for benchmark differentiation, patent strategy, and milestone-based revenue models.

Asia-Pacific is projected to grow at a 26.22% CAGR through 2031, making it the fastest-growing regional cluster. China and Japan are driving that rise through different models, with China leaning on policy-backed AI biopharmaceutics investment and larger domestic compute build-out, while Japan is leaning more on corporate-academic collaboration. The AI in antibody discovery market is expanding in Asia-Pacific because local players are building stronger antibody datasets, stronger compute access, and more region-specific development infrastructure. Data sovereignty is also becoming a practical advantage, since regulated pharmaceutical collaborations are more likely to move forward when proprietary sequence data can remain under controlled domestic environments. The region's research ecosystems are now moving beyond follower status, and their benchmark progress suggests that competition with U.S. platforms will become more direct over the forecast period.

Europe remains a meaningful mid-tier region, supported mainly by Germany and the United Kingdom, where large pharma groups are putting AI discovery into core R&D instead of isolating it as an experimental add-on. Bayer's multi-year partnership with Cradle in January 2026 showed that established pharma companies in Europe are committing operating budgets to AI-assisted protein and antibody engineering rather than limiting spend to small exploratory pilots. The AI in antibody discovery market also benefits in Europe from early work in cell-free systems and closed-loop wet-lab integration, which fits the region's strength in process discipline and translational science. South America and the Middle East and Africa remain early-stage areas, with activity centered more on academic institutes and contract manufacturing expansion than on independent platform creation. Even so, the presence of global CROs offering AI discovery services is starting to build local demand that can turn into measurable regional share after 2027.

1. AbCellera
2. Absci
3. Alloy Therapeutics
4. Antiverse
5. BigHat Biosciences
6. Chai Discovery
7. Cradle
8. Etcembly
9. EVQLV
10. Generate:Biomedicines
11. Harbour BioMed
12. Infinimmune
13. Insilico Medicine
14. LabGenius Therapeutics
15. MAbSilico
16. MOLCURE
17. Nabla Bio
18. Nona Biosciences

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

• 1.1 Study Assumptions & Market Definition
• 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Conventional Discovery Cost and Attrition Pressure
  • 4.2.2 Rising Demand for Next-Generation Biologics
  • 4.2.3 Precision Medicine Demand in Oncology and Immunology
  • 4.2.4 Expanding Pharma-AI Collaboration Budgets
  • 4.2.5 AI Opening GPCR and Ion-Channel Antibody Programs
  • 4.2.6 Antibody-Specific Foundation Models Improving Hit Quality
• 4.3 Market Restraints
  • 4.3.1 Scarcity of Labeled Antibody Data
  • 4.3.2 High Compute and Interdisciplinary Talent Cost
  • 4.3.3 Wet-Lab Triage Bottleneck for AI-Generated Libraries
  • 4.3.4 Patentability and Enablement Pressure on AI-Generated Antibodies
• 4.4 Value Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 4.7 Porter's Five Forces
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Bargaining Power of Buyers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Intensity of Rivalry

5 Market Size & Growth Forecasts (Value, USD)

• 5.1 By Offering
  • 5.1.1 Software Platforms
  • 5.1.2 Discovery Services
  • 5.1.3 Integrated Platform Partnerships
  • 5.1.4 Data and Model Licensing
• 5.2 By Technology
  • 5.2.1 Structure Prediction
  • 5.2.2 Generative AI and Protein Language Models
  • 5.2.3 Machine Learning and Deep Learning
  • 5.2.4 Natural Language Processing and Knowledge Graphs
  • 5.2.5 Closed-loop AI with Lab Automation
• 5.3 By Application
  • 5.3.1 Target Identification and Validation
  • 5.3.2 Epitope Mapping and Binder Screening
  • 5.3.3 De novo Antibody Design
  • 5.3.4 Lead Optimization and Engineering
  • 5.3.5 Developability and Manufacturability Prediction
• 5.4 By Antibody Modality
  • 5.4.1 Monoclonal Antibodies
  • 5.4.2 Bispecific Antibodies
  • 5.4.3 Multispecific Antibodies
  • 5.4.4 Antibody-Drug Conjugates
  • 5.4.5 Nanobodies and Single-domain Antibodies
• 5.5 By Therapeutic Area
  • 5.5.1 Oncology
  • 5.5.2 Autoimmune and Inflammatory Diseases
  • 5.5.3 Infectious Diseases
  • 5.5.4 Neurology
  • 5.5.5 Metabolic Diseases
  • 5.5.6 Rare Diseases
• 5.6 By End User
  • 5.6.1 Pharmaceutical Companies
  • 5.6.2 Biotechnology and Platform Companies
  • 5.6.3 CROs and CDMOs
  • 5.6.4 Academic and Research Institutes
• 5.7 By Geography
  • 5.7.1 North America
    • 5.7.1.1 United States
    • 5.7.1.2 Canada
    • 5.7.1.3 Mexico
  • 5.7.2 Europe
    • 5.7.2.1 Germany
    • 5.7.2.2 United Kingdom
    • 5.7.2.3 France
    • 5.7.2.4 Italy
    • 5.7.2.5 Spain
    • 5.7.2.6 Rest of Europe
  • 5.7.3 Asia-Pacific
    • 5.7.3.1 China
    • 5.7.3.2 India
    • 5.7.3.3 Japan
    • 5.7.3.4 South Korea
    • 5.7.3.5 Australia
    • 5.7.3.6 Rest of Asia-Pacific
  • 5.7.4 Middle East and Africa
    • 5.7.4.1 GCC
    • 5.7.4.2 South Africa
    • 5.7.4.3 Rest of Middle East and Africa
  • 5.7.5 South America
    • 5.7.5.1 Brazil
    • 5.7.5.2 Argentina
    • 5.7.5.3 Rest of South America

6 Competitive Landscape

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles {(includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share for key companies, Products & Services, and Recent Developments)}
  • 6.3.1 AbCellera
  • 6.3.2 Absci
  • 6.3.3 Alloy Therapeutics
  • 6.3.4 Antiverse
  • 6.3.5 BigHat Biosciences
  • 6.3.6 Chai Discovery
  • 6.3.7 Cradle
  • 6.3.8 Etcembly
  • 6.3.9 EVQLV
  • 6.3.10 Generate:Biomedicines
  • 6.3.11 Harbour BioMed
  • 6.3.12 Infinimmune
  • 6.3.13 Insilico Medicine
  • 6.3.14 LabGenius Therapeutics
  • 6.3.15 MAbSilico
  • 6.3.16 MOLCURE
  • 6.3.17 Nabla Bio
  • 6.3.18 Nona Biosciences

7 Market Opportunities & Future Outlook

• 7.1 White-space & unmet-need assessment