AI in Protein Engineering Market
AI in Protein Engineering Market, AI Tools (Generative, Protein Language, Structure-Based), Application (Binder, De Novo, Enzyme Engineering), Protein Type (Antibody, Peptide), Process (Discovery, Design), End User (CRO, Pharma) – Global Forecast to 2031
OVERVIEW
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
The global AI in protein engineering market is projected to reach USD 3.82 billion by 2031 from USD 1.44 billion in 2026, at a CAGR of 21.6% from 2026 to 2031. The market is driven by the increasing adoption of AI-powered protein design and optimization technologies to accelerate protein discovery, reduce experimental iterations, and improve engineering outcomes. The growing use of generative AI, protein language models, machine learning, and structure-based AI, along with the need to optimize protein sequences, enhance stability and affinity, and identify promising candidates, is accelerating the adoption of AI-enabled protein engineering platforms.
KEY TAKEAWAYS
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BY REGIONNorth America dominates the AI in protein engineering market, with a share of 48.6% in 2025.
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BY OFERINGBy offering, software/platforms dominate the market with a share of 75.4% in 2025.
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BY AI TOOLProtein language models are expected to register the highest CAGR during the forecast period.
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BY APPLICATIONBy application, de novo protein design segment dominated the AI in protein engineering market in 2025.
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BY PROTEIN TYPEBy protein type, the miniproteins segment is expected to register the highest CAGR of 23.7% during the forecast period.
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COMPETITIVE LANDSCAPESchrodinger, Inc., XtalPi, and Dassault Systèmes were identified as some of the star players in the AI in protein engineering market (global), given their strong market share and product footprint.
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COMPETITIVE LANDSCAPEArzeda Corporation and Evozyne, among others, have distinguished themselves among startups and SMEs by securing strong footholds in specialized niche areas, underscoring their potential as emerging market leaders.
The AI in protein engineering market is fueled by the demand for faster, more precise, and scalable protein discovery and optimization through advanced computational approaches. The growing adoption of generative AI, protein language models, machine learning, and structure-based AI, combined with the increasing complexity of biologics and next-generation protein therapeutics, is encouraging pharmaceutical and biotechnology companies, CROs, and research organizations to implement AI-enabled protein engineering tools to reduce experimental iterations, accelerate candidate development, and improve the probability of identifying high-performing proteins.
TRENDS & DISRUPTIONS IMPACTING CUSTOMERS' CUSTOMERS
The AI in protein engineering market has undergone a significant transformation, shifting from traditional computational protein modeling and sequence-analysis tools to AI-driven, generative, and integrated protein engineering platforms. Vendors are moving beyond conventional structure prediction and molecular modeling toward advanced capabilities such as protein language models, generative AI, de novo protein design, AI-based antibody and enzyme optimization, and automated design-build-test-learn workflows. As demand for faster protein discovery grows among pharmaceutical and biotechnology companies, CROs, academic researchers, and industrial biotechnology organizations, these technologies are reshaping protein engineering workflows. As protein development becomes more complex, particularly for next-generation biologics, multispecific proteins, engineered enzymes, and novel protein therapeutics, AI-enabled platforms are increasingly important for exploring large sequence spaces, improving candidate quality, reducing experimental iterations, and accelerating the transition from computational design to experimental validation.
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
MARKET DYNAMICS
Level
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Increasing adoption of generative AI and protein foundation models
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Rising demand for next-generation biologics and engineered proteins
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Limited availability of high-quality, experimentally validated protein data
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High cost and complexity of experimental validation
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Expansion of AI-designed de novo proteins
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Growth of AI-enabled antibody discovery and optimization
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Biosecurity and biosafety concerns
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Lack of standardized benchmarking across AI protein-engineering platforms
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
Driver: Increasing adoption of generative AI and protein foundation models
The growing adoption of generative AI and protein foundation models is driving the AI in protein engineering market by enabling the generation, prediction, and optimization of protein sequences at scales beyond conventional computational approaches. These models can learn from large protein sequence and structural datasets to identify sequence-function relationships and generate novel proteins with desired characteristics. Their growing application in de novo protein design, antibody engineering, enzyme optimization, and protein stability prediction is reducing reliance on trial-and-error approaches and accelerating the design-to-validation cycle.
Restraint: Limited availability of high-quality, experimentally validated protein data
The limited availability of high-quality, standardized, and experimentally validated protein sequence-function data restrains market growth by affecting the training and reliability of AI models. Although large protein sequence databases are expanding rapidly, much of the available data lacks detailed experimental measurements for properties such as activity, stability, binding affinity, and specificity. Differences in experimental protocols and incomplete annotations further reduce data consistency, making it difficult to train models that reliably generalize across proteins and biological contexts.
Opportunity: Expansion of AI-designed de novo proteins
The expansion of AI-designed de novo proteins presents a significant opportunity as generative models enable researchers to create proteins that do not exist naturally and tailor them for specific therapeutic, industrial, and research applications. AI can explore vast sequence and structural spaces to design proteins with targeted binding, catalytic activity, stability, and specificity, opening opportunities across next-generation biologics, enzymes, protein binders, and synthetic biology. Increasing integration of AI design with automated synthesis and high-throughput screening is further creating opportunities for scalable protein engineering.
Challenge: Biosecurity and biosafety concerns
Biosecurity and biosafety concerns represent a growing challenge as AI systems become capable of designing novel proteins with potentially unintended biological properties. The ability to generate or modify proteins at scale raises concerns regarding the misuse of AI-designed sequences, including the potential creation of harmful biological agents or proteins with undesirable functions. These concerns are increasing the need for sequence screening, responsible AI frameworks, access controls, experimental safeguards, and regulatory oversight, which may add complexity to the development and deployment of AI-enabled protein engineering platforms.
AI IN PROTEIN ENGINEERING MARKET: COMMERCIAL USE CASES ACROSS INDUSTRIES
| COMPANY | USE CASE DESCRIPTION | BENEFITS |
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AI/physics-based protein design and optimization | Faster protein optimization, reduced experimental screening, improved candidate quality, and lower protein engineering costs |
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AI-driven protein/peptide design and high-throughput screening | Faster candidate discovery; improved screening efficiency |
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AI-driven protein structure prediction and molecular simulation | Accelerates protein structure analysis and enables computational evaluation of protein behavior and interactions |
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AI for protein binder and antibody design | Supports computational generation and optimization of protein binders and antibody candidates with desired properties |
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Generative AI, de novo protein design & design-build-test workflows | Faster protein discovery; fewer experimental iterations |
Logos and trademarks shown above are the property of their respective owners. Their use here is for informational and illustrative purposes only.
MARKET ECOSYSTEM
The AI in protein engineering market ecosystem consists of leading providers such as Schrödinger, Inc., XtalPi, Dassault Systèmes, NVIDIA Corporation, Generate: Biomedicines, Absci Corp., and Insilico Medicine, which offer technologies including generative AI, protein language models, de novo protein design, structure-based modeling, antibody engineering, and protein optimization solutions. Emerging companies such as Chai Discovery, EvolutionaryScale, Profluent, BigHat Bio, Cradle, and Latent Labs are advancing AI-driven protein design, sequence generation, and optimization capabilities. Cloud and computational infrastructure providers such as AWS, Microsoft Azure, and Google Cloud support the high-performance computing and scalable environments required for AI-based protein engineering. End users, including pharmaceutical and biotechnology companies, CROs, academic research institutes, and industrial biotechnology companies, leverage these platforms to accelerate protein discovery, optimize candidates, reduce experimental iterations, and improve R&D productivity.
Logos and trademarks shown above are the property of their respective owners. Their use here is for informational and illustrative purposes only.
MARKET SEGMENTS
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
AI in Protein Engineering Market, By Offering
In 2025, the software/platforms segment accounted for the largest share of the AI in protein engineering market, as AI-powered platforms provide the core infrastructure for protein discovery, design, optimization, and characterization. These solutions enable researchers to analyze protein sequences and structures, generate novel proteins, predict functional properties, and prioritize candidates before experimental validation. The growing adoption of generative AI, protein language models, machine learning, and structure-based AI, along with increasing demand for de novo protein design and AI-driven antibody and enzyme engineering, has further strengthened platform adoption. Additionally, integration with high-throughput experimentation, automated design-build-test workflows, cloud computing, and large-scale protein datasets is driving organizations to invest in advanced AI protein engineering platforms to accelerate discovery, reduce experimental iterations, and improve R&D productivity.
AI in Protein Engineering Market, By AI Tool
By AI tool, the Generative AI segment accounted for the largest share of the AI in protein engineering market in 2025, securing the highest share of market revenue compared with other AI tools. Generative AI's leading position is primarily driven by its growing use in de novo protein design, protein sequence generation, antibody engineering, enzyme optimization, and the development of next-generation biologics. Generative AI enables researchers to explore vast protein sequence spaces and design novel proteins with desired structural and functional properties, reducing dependence on conventional trial-and-error approaches. Increasing availability of large protein datasets, advances in foundation models, and integration with automated high-throughput experimentation are further strengthening adoption of generative AI for faster protein discovery, optimization, and validation.
AI in Protein Engineering Market, By Application
By application, the antibody discovery and optimization segment accounted for the largest share of the AI in protein engineering market in 2025, securing the highest share of market revenue compared with other applications. This segment's leading position is primarily driven by the widespread use of AI in antibody discovery, affinity optimization, specificity and cross-reactivity assessment, developability optimization, and bispecific and multispecific antibody engineering. AI technologies enable researchers to screen large antibody sequence spaces, predict antibody–antigen interactions, identify promising candidates, and optimize key properties before experimental testing. The increasing development of next-generation antibodies and targeted biologics, coupled with growing adoption of generative AI and protein language models, is further strengthening demand for AI-enabled antibody engineering solutions.
AI in Protein Engineering Market, By Protein Type
In 2025, the antibodies segment accounted for the largest share of the AI in protein engineering market by protein type. This leading position stems from the extensive use of AI across antibody development, including sequence generation, candidate discovery, affinity and specificity optimization, and developability assessment. The expanding pipeline of antibody-based therapeutics, particularly bispecific and multispecific antibodies, is increasing demand for AI-enabled approaches that can efficiently evaluate and optimize large numbers of protein variants. Furthermore, advances in generative AI and protein language models enable faster, more precise antibody engineering, improving candidate quality and accelerating experimental validation.
AI in Protein Engineering Market, By Protein Engineering Process
In 2025, the protein design segment held the largest share of the AI in protein engineering market by process. The dominance of this segment can be attributed to the increasing application of AI for designing novel protein sequences and structures with predefined functional characteristics. AI-enabled design tools allow researchers to evaluate extensive sequence and structural possibilities, identify promising candidates, and optimize desired properties before laboratory testing. The growing focus on de novo protein design, therapeutic proteins, enzymes, and other next-generation biologics is further expanding the use of AI during the design stage. In addition, advancements in generative AI, protein language models, and structure-based approaches are improving the efficiency and precision of protein design, supporting faster progression toward experimental validation.
AI in Protein Engineering Market, By End User
The pharmaceutical & biotechnology companies segment held the largest share of the AI in protein engineering market by end user in 2025, owing to the extensive adoption of AI technologies across protein discovery and development activities. These companies are increasingly using AI-powered platforms for de novo protein design, antibody discovery and optimization, enzyme engineering, protein stability assessment, and developability optimization to improve R&D efficiency. The growing complexity of biologic pipelines and the need to accelerate candidate identification are further driving investments in AI-enabled protein engineering solutions. In addition, integrating AI with high-throughput screening, automated experimentation, and large-scale protein datasets enables pharmaceutical and biotechnology companies to reduce experimental iterations and accelerate the development of novel protein therapeutics.
REGION
Asia Pacific to register the highest CAGR during the forecast period
The Asia Pacific region is the fastest-growing market for AI in protein engineering, supported by increasing pharmaceutical and biotechnology R&D activities, rising adoption of AI-driven protein design and optimization technologies, and growing investments in life sciences innovation. Furthermore, expanding capabilities in biopharmaceutical development, computational biology, and high-throughput experimentation, along with supportive government initiatives and improving research infrastructure, are driving the adoption of AI-enabled protein engineering platforms across the region. The increasing focus on accelerating protein discovery, developing next-generation biologics, and reducing experimental iterations is further contributing to market growth.
AI IN PROTEIN ENGINEERING MARKET: COMPANY EVALUATION MATRIX
Schrödinger, Inc. (Star) stands out with strong market positioning and a comprehensive AI-driven protein engineering portfolio, supported by its physics-based computational platform and AI-enabled solutions for protein structure prediction, molecular modeling, protein design, and biologics optimization. Its broad capabilities across computational protein engineering, integrated modeling workflows, and collaborations with pharmaceutical and biotechnology companies strengthen its position across the protein discovery and development lifecycle. Isomorphic Labs (Emerging Leader) has gained recognition through its AI-driven approach to protein structure and drug design, supported by technology originating from DeepMind's AlphaFold research and strategic collaborations with major pharmaceutical companies, including Eli Lilly and Novartis. While Schrodinger, Inc. leads through its established computational platform, broad protein engineering capabilities, integrated AI and physics-based workflows, and commercial partnerships, Isomorphic Labs demonstrates strong potential to move toward the leaders' quadrant as demand for AI-powered protein design, structure-based modeling, and accelerated biologics discovery continues to grow.
Source: Secondary Research, Interviews with Experts, MarketsandMarkets Analysis
KEY MARKET PLAYERS
- Schrödinger, Inc.
- XtalPi
- Dassault Systèmes
- NVIDIA Corporation
- Generate:Biomedicines
- Absci Corp.
- Insilico Medicine
- Isomorphic Labs
- Chai Discovery
- EvolutionaryScale
- Profluent
- BigHat Bio
- Cradle
- A-Alpha Bio Inc.
- Latent Labs
- Basecamp Research
- Nabla Bio Inc.
- Evozyne
- LabGenius Limited
MARKET SCOPE
| REPORT METRIC | DETAILS |
|---|---|
| Market Size in 2025 (Value) | USD 1.21 BN |
| Market Forecast in 2026 (Value) | USD 1.44 BN |
| Market Forecast in 2031 (Value) | USD 3.82 BN |
| Growth Rate | CAGR of 21.6% from 2026–2031 |
| Years Considered | 2024–2031 |
| Base Year | 2025 |
| Forecast Period | 2026–2031 |
| Units Considered | Value (USD Million/Billion) |
| Report Coverage | Revenue forecast, company ranking, competitive landscape, growth factors, and trends |
| Segments Covered |
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| Regions Covered | North America, Europe, Asia Pacific, Latin America, Middle East & Africa |
WHAT IS IN IT FOR YOU: AI IN PROTEIN ENGINEERING MARKET REPORT CONTENT GUIDE
DELIVERED CUSTOMIZATIONS
We have successfully delivered the following deep-dive customizations:
| CLIENT REQUEST | CUSTOMIZATION DELIVERED | VALUE ADDS |
|---|---|---|
| Local Competitive Landscape | Profiling of leading AI protein engineering players covering AI capabilities, applications, platforms, protein types, partnerships, and offerings. | Enables competitive benchmarking, gap identification, and differentiation. |
| Regional Market Entry Strategy | Assessment of AI adoption, pharma/biotech R&D, research infrastructure, talent, funding, and startup ecosystems across key regions. | Supports market prioritization, localization, and investment decisions. |
| Local Risk & Opportunity Assessment | Evaluation of data availability, validation requirements, talent gaps, AI adoption barriers, biosecurity considerations, and opportunities across key protein engineering applications. | Supports risk mitigation and identification of growth opportunities. |
| Technology Adoption | Mapping adoption of generative AI, protein language models, ML/DL, structure-based AI, de novo protein design, antibody engineering, enzyme engineering, and automated workflows. | Guides technology strategy, product development, and alignment with customer needs. |
RECENT DEVELOPMENTS
- June 2026 : Cradle partnered with Lundbeck to deploy its generative AI protein design platform for two antibody programs targeting CNS diseases, enabling an end-to-end, AI-guided protein engineering workflow that improves antibody candidates and reduces wet-lab iterations.
- December 2025 : XtalPi partnered with Gan & Lee Pharmaceuticals to advance AI-driven peptide drug discovery for metabolic diseases, leveraging its PepiX platform for generative design, stability optimization, developability prediction, and high-throughput screening
- December 2024 : AI Proteins partnered with Bristol Myers Squibb to discover and develop novel AI-designed miniprotein therapeutics, leveraging its AI-driven platform for de novo protein design and optimization. The collaboration covers two undisclosed targets, with potential development, regulatory, and commercial milestone payments of up to USD 400 million, plus royalties.
Table of Contents
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Methodology
This research study used extensive primary and secondary sources. It involved the analysis of various factors affecting the industry to identify the segmentation types, industry trends, key players, the competitive landscape of market players, and key market dynamics such as drivers, opportunities, challenges, restraints, and key player strategies.
Secondary Research
This research study extensively utilized secondary sources, including directories, databases such as Dun & Bradstreet, Bloomberg Businessweek, and Factiva, as well as white papers, annual reports, and Companies House documents. The secondary research aimed to gather and analyze information for a comprehensive, commercially focused study of the AI in protein engineering market, covering technical aspects and market dynamics. It also helped identify key players, market segments, industry trends, geographic markets, and significant market developments. Additionally, secondary research compiled a database of prominent industry leaders.
Primary Research
In the primary research process, various supply-side and demand-side sources were interviewed to obtain qualitative and quantitative information for this report. Primary sources from the supply side included industry experts such as CEOs, vice presidents, marketing and sales directors, technology & innovation directors, engineers, and related key executives from various companies and organizations operating in the AI in protein engineering market. Primary sources from the demand side included personnel from pharmaceutical & biotechnology companies, contract research organizations, academic and research institutes, and industrial biotechnology companies.
A breakdown of the primary respondents is provided below:
*Others include sales managers, marketing managers, and product managers.
Note: Tiers are defined based on a company’s total revenue, as of 2025: Tier 1 = >USD 1 billion, Tier 2 = USD 500 million to USD 1 billion, and Tier 3 = <USD 500 million.
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation
The total size of the AI in protein engineering market was determined by triangulating data using the two approaches mentioned below. After the completion of each approach, the weighted average of these approaches was taken based on the level of assumptions used in each approach.
A2L Refrigerant Market: Top-Down and Bottom-Up Approach
Data Triangulation
The size of the AI in protein engineering market was estimated through segmental extrapolation using the bottom-up approach. The methodology used is as given below:
- Revenues for individual companies were gathered from public sources and databases.
- Shares of leading players in the AI in protein engineering market were gathered from secondary sources to the extent available. In some cases, shares of AI in protein engineering businesses were estimated after a detailed analysis of various parameters, including product portfolios, market positioning, selling price, and geographic reach & strength.
- Individual shares or revenue estimates were validated through interviews with experts.
- The total revenue in the AI in protein engineering market was determined by extrapolating the market share data of major companies.
Market Definition
The AI in protein engineering market comprises AI-powered software, platforms, and services used to discover, design, optimize, characterize, and validate proteins with desired structural and functional properties. It includes machine learning, deep learning, protein language models, generative AI, and structure-based AI applied to antibody discovery, enzyme engineering, de novo protein design, protein stability, next-generation biologics, and other protein-engineering workflows. The market serves pharmaceutical and biotechnology companies, CROs, academic institutions, and industrial biotechnology organizations.
Key Stakeholders
- AI Protein Engineering Platform & Software Providers
- Pharmaceutical & Biopharmaceutical Companies
- Biotechnology Companies & Protein Engineering Startups
- Contract Research Organizations (CROs)
- Academic & Research Institutions/Universities
- Protein Engineering & Medical Research Laboratories
- Computational Biology & Bioinformatics Companies
- AI/ML Technology & Cloud Computing Providers
- Contract Development & Manufacturing Organizations (CDMOs)
- Research & Development (R&D) Organizations
- Regulatory Agencies & Government Bodies
- Clinical & Translational Research Organizations
- Investors & Venture Capital Firms
- Business Research & Consulting Service Providers
Report Objectives
- To define, describe, and forecast the AI in protein engineering market based on offering, AI tool, application, protein type, protein engineering process, end user, and region
- To provide detailed information regarding the major factors influencing the market growth (such as drivers, restraints, opportunities, and challenges)
- To analyze the micromarkets with respect to individual growth trends, prospects, and contributions to the overall AI in protein engineering market
- To analyze the opportunities for stakeholders and provide details of the competitive landscape for market leaders
- To forecast the size of the market segments with respect to five main regions, namely, North America, Europe, the Asia Pacific, Latin America, and the Middle East & Africa
- To profile the key players and analyze their market shares and core competencies
- To track and analyze competitive developments such as product launches & approvals, partnerships, agreements, and collaborations in the overall AI in protein engineering market
- To benchmark players within the market using the proprietary "Competitive Leadership Mapping" framework, which analyzes market players on various parameters within the broad categories of business and product strategy.
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Geographic Analysis
- Further breakdown of the Rest of Asia Pacific AI in protein engineering market into Taiwan, New Zealand, Thailand, Singapore, Malaysia, and other countries
- Further breakdown of the Rest of Europe AI in protein engineering market into Russia, Austria, Finland, Sweden, Turkey, Norway, Poland, Portugal, Romania, Denmark, and other countries
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Growth opportunities and latent adjacency in AI in Protein Engineering Market, AI Tools (Generative, Protein Language, Structure-Based), Application (Binder, De Novo, Enzyme Engineering), Protein Type (Antibody, Peptide), Process (Discovery, Design), End User (CRO, Pharma)