DELRAY BEACH, Fla., Oct. 5, 2026 /PRNewswire/ -- According to MarketsandMarkets™, the 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% during the forecast period.

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AI in Protein Engineering Market Size & Forecast:

• Market Size Available for Years: 2026–2031
• 2026 Market Size: USD 1.44 billion
• 2031 Projected Market Size: USD 3.82 billion
• CAGR (2026–2031): 21.6%

AI in Protein Engineering Market Trends & Insights:

• North America dominates the AI in protein engineering market, with a share of 48.6% in 2025.
• By offering, software/platforms dominate the market with a share of 75.4% in 2025.
• By protein type, the miniproteins segment is expected to register the highest CAGR of 23.7% during the forecast period.

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Market expansion of AI in protein engineering is being propelled by the increasing complexity of protein discovery and optimization, growing AI investment, and the need for faster, more cost-efficient R&D. AI technologies, including machine learning and deep learning, protein language models, generative AI, and structure-based AI, are increasingly applied to protein discovery, de novo design, sequence optimization, structure prediction, and developability assessment. According to a 2025 Benchling survey of ~100 biotech and pharmaceutical organizations, 55% allocated at least 11% of their R&D technology budgets to AI, including 28% allocating 11–20%, 18% allocating 21–35%, and 6% allocating >35%. AI adoption is also expanding, with 48% reporting strategic AI integration and 87% reporting AI agents embedded in workflows, products, or services. AI is also demonstrating productivity gains, with Recursion reporting approximately 330 compounds per program over 17 months, compared with an industry average of 2,500+ compounds over 42 months. Moreover, 88% planned to increase cloud/infrastructure investment, 86% data-platform investment, and 84% AI-platform investment. However, adoption remains constrained by scaling, data, talent, computational, and validation challenges, with only 22% successfully scaling AI and 9% achieving significant returns. These trends are expected to accelerate adoption of AI-powered protein engineering platforms while organizations seek to reduce experimental iterations and improve R&D productivity.

By application, the next-generation biologics segment is expected to register the fastest growth rate during the forecast period.

By application, next-generation biologics is expected to be the fastest-growing segment of the AI in protein engineering market. This segment's growth is driven by increasing demand for novel, more effective protein therapeutics; the rising complexity of biologic molecules; and growing adoption of AI for de novo protein design, antibody discovery and optimization, enzyme engineering, and protein stability and developability optimization. Pharmaceutical and biotechnology companies are increasingly leveraging AI-powered protein engineering platforms to explore large protein sequence spaces, identify promising candidates, and accelerate the design–build–test cycle. Additionally, advances in protein language models and generative AI are enabling the design of novel proteins with desired structural and functional properties, further supporting adoption in next-generation biologics. As investment in innovative biologic modalities continues to increase, demand for AI-enabled protein design and optimization solutions is expected to accelerate, helping organizations reduce experimental iterations, improve candidate selection, and shorten development timelines.

By AI tool, generative AI accounted for the largest share of the AI in protein engineering market in 2025.

By AI tool, generative AI accounted for the largest share of the AI in protein engineering market in 2025. This dominance can be attributed to the growing adoption of generative AI for de novo protein design, antibody generation and optimization, enzyme engineering, and protein sequence generation. Generative AI models enable researchers to explore vast protein sequence spaces and design novel proteins with desired structural and functional properties, reducing reliance on conventional trial-and-error approaches. Furthermore, the growing availability of large-scale protein datasets, advances in foundation models and generative architectures, and integration with high-throughput experimental workflows are improving the scalability and efficiency of AI-driven protein engineering. The growing use of generative AI by pharmaceutical and biotechnology companies to accelerate protein discovery and optimize therapeutic candidates is expected to further strengthen its market leadership.

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The Asia Pacific is expected to be the fastest-growing market during the forecast period.

The AI in protein engineering market is segmented into five major regions: North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa.

Asia Pacific is projected to be the fastest-growing regional market for AI in protein engineering, driven by increasing investments in biotechnology and pharmaceutical R&D, expanding AI and computational biology capabilities, and growing adoption of AI-enabled approaches for protein discovery, design, and optimization. Countries such as China, India, Japan, South Korea, and Australia are strengthening their life sciences and AI ecosystems through government-backed research programs, expanding biopharmaceutical manufacturing, and investments in advanced computing and biotechnology infrastructure. China invested about RMB 3.63 trillion (USD ~500 billion) in R&D in 2024, an 8.9% year-over-year increase, with basic research spending up 10.7%, supporting the development of advanced computational and life sciences capabilities. India's bioeconomy reached USD 165.7 billion in 2024, more than 16-fold higher than in 2014, while the government continues to promote biotechnology innovation through initiatives such as BioE3 and increased support for biotech startups. Japan also recorded JPY 22.05 trillion in total R&D expenditure in FY2023, with pharmaceutical R&D expenditure reaching JPY 1.54 trillion, up 7.6%, highlighting the region's strong pharmaceutical research base. These investments are creating favorable conditions for adopting protein language models, generative AI, structure-based AI, machine learning, and deep learning for applications including de novo protein design, antibody discovery and optimization, enzyme engineering, and protein stability prediction. Furthermore, the growing presence of pharmaceutical companies, biotech startups, CROs, academic research institutes, and AI-protein engineering companies across the region is expected to accelerate the deployment of AI-enabled protein engineering platforms and strengthen Asia Pacific's position as the fastest-growing regional market.

Key Players

Leading players in the AI in Protein Engineering companies include Schrödinger, Inc. (US), XtalPi (China), Dassault Systèmes (France), NVIDIA Corporation (US), Generate: Biomedicines (US), Absci Corp. (US), Insilico Medicine (US), Isomorphic Labs (UK), among others.

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AI in Protein Engineering Market - Investment & funding +Merger & Acquisition

Investment Funding Context

The AI in protein engineering market is witnessing increasing investment activity, supported by growing investor and pharmaceutical interest in generative AI, protein foundation models, and de novo protein design. In 2024, EvolutionaryScale raised USD 142 million to develop AI models that generate novel proteins, while in February 2025, Latent Labs secured USD 50 million to advance generative AI models for protein design. Investment momentum continued in November 2025, when AI Proteins raised USD 41.5 million in Series A financing to advance AI-designed de novo miniprotein therapeutics. The funding environment is further supported by growing pharmaceutical-industry collaboration, with 120 AI-based target and drug discovery deals recorded in 2025, representing 23% of the 513 such deals tracked since 2017; total potential deal value reached USD 29.7 billion, with nearly USD 800 million committed upfront. These investments and strategic collaborations highlight increasing confidence in AI-enabled protein design and are expected to support the development of generative protein design, antibody engineering, enzyme optimization, and next-generation biologics.

Revenue Shift Context

The market is shifting toward AI-driven, computationally enabled platforms that accelerate protein discovery, design, and optimization. The AI in protein engineering market is projected to rise from USD 1.44 billion in 2026 to USD 3.82 billion by 2031, at a CAGR of 21.6%, with much of this growth concentrated in AI-powered protein design, generative protein engineering, protein language models, antibody discovery and optimization, enzyme engineering, and protein stability and developability solutions. Meanwhile, software/platforms accounted for 75.4% of the market in 2025, highlighting strong adoption of AI-enabled platforms, while emerging areas such as generative AI, de novo protein design, protein language models, and AI-driven optimization are capturing a rising share of new spending.

Mergers and Acquisitions

Mergers and acquisitions in the AI in protein engineering market are gaining momentum, with companies increasingly targeting AI-driven protein design, computational biology, and generative AI capabilities. A major transaction was Recursion's 2024 acquisition of Exscientia, combining Recursion's automated drug discovery platform with Exscientia's AI-driven drug design capabilities to strengthen technology-enabled drug discovery. Another notable transaction was Recursion's 2023 acquisition of Cyclica and Valence, expanding its capabilities in AI, machine learning, digital chemistry, and generative molecular design. These transactions highlight growing demand for AI-enabled protein and molecular design platforms, as pharmaceutical and biotechnology companies seek to accelerate discovery, improve candidate selection, and integrate computational design with automated experimental workflows.

AI in Protein Engineering MARKET: MERGERS AND ACQUISITIONS, JANUARY 2023–September 2026

Company Revenue Share Details

The top seven players account for approximately 20–25% of the AI in protein engineering market, reflecting a fragmented and rapidly evolving competitive landscape in which no single company holds a dominant market position. Schrödinger, Inc., XtalPi, Dassault Systèmes, NVIDIA Corporation, Generate:Biomedicines, Absci Corp., and Insilico Medicine benefit from established AI platforms, computational modeling capabilities, generative protein design technologies, and expanding applications across pharmaceutical, biotechnology, and industrial life sciences. The remaining 75–80% of the market is distributed across specialized and emerging players, including Isomorphic Labs, Chai Discovery, EvolutionaryScale, Profluent, BigHat Bio, Cradle, A-Alpha Bio Inc., Latent Labs, Basecamp Research, Nabla Bio Inc., LabGenius Limited, Arzeda Corporation, Evozyne, Menten AI, ProteinQure, Outpace Bio, AI Proteins, MonodBio, Biomatter Inc., and Diffuse Bio. The broad distribution of market share across established technology providers and specialized protein-engineering companies highlights the market's competitive diversity, with companies differentiating through proprietary AI models, biological datasets, generative design capabilities, integrated experimental workflows, strategic partnerships, and expansion into new protein modalities and applications.

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