AI in Protein Engineering Market worth $3.82 billion by 2031
The report "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", is projected to grow from USD 1.44 billion in 2026 and to reach USD 3.82 billion by 2031, at a Compound Annual Growth Rate (CAGR) of 21.6% during the forecast period.
Browse 350 market data Tables and 50 Figures spread through 450 Pages and in-depth TOC on "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"
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Drug development is becoming increasingly complex, creating demand for AI-enabled protein engineering to accelerate protein discovery, design, and optimization while reducing costly experimental iterations. AI platforms enable researchers to design novel proteins, predict structures and functions, optimize sequences, and prioritize candidates before wet-lab validation. The expanding scale and capabilities of AI are supporting this adoption; AlphaFold2 achieved a median accuracy of 92.4 GDT for protein structures in the CASP14 assessment, representing a major advancement in computational protein structure prediction. Furthermore, RFdiffusion generated functional protein binders with a 19% overall experimental success rate, demonstrating generative AI's potential to produce experimentally viable protein designs. AI-based protein engineering is also improving experimental efficiency, with computational filtering approaches reported to achieve 50–150% higher experimental success rates for AI-generated enzymes. Continued advances in protein language models, generative AI, and high-throughput experimentation are expected to further improve design accuracy and scalability. However, market growth remains constrained by limited high-quality experimental data, model validation and interpretability challenges, computational requirements, specialized talent shortages, and the continued need for costly wet-lab validation.
“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.
“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 Market Players:
The key players functioning in the AI in protein engineering market 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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