AI in Protein Engineering Market

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 increasing complexity of protein discovery and engineering, rising demand for proteins with improved efficacy, stability, specificity, and functionality, and the need to accelerate design–build–test cycles are driving AI adoption across pharmaceutical and biotechnology companies, academic institutions, and industrial biotechnology organizations. Machine learning, protein language models, generative AI, and structure-based AI are increasingly being used to predict protein structures and functions, optimize sequences, and design novel proteins. The AlphaFold Protein Structure Database contains more than 200 million predicted protein structures and has been accessed by more than 3 million users across 190+ countries, highlighting the expanding use of AI-enabled protein resources. ProGen, a protein language model trained on 280 million protein sequences across more than 19,000 protein families, generated functional proteins with as little as 31.4% sequence identity to known natural proteins, demonstrating AI's potential for de novo protein design. Similarly, RFdiffusion achieved a 19% experimental binding success rate for AI-designed protein binders across five tested targets. Regulatory acceptance of AI is also increasing, with the US FDA reporting more than 500 submissions containing AI components between 2016 and 2023 and issuing guidance on AI use in drug and biological-product regulatory decision-making. Furthermore, AI-based computational filtering has demonstrated 50–150% improvements in experimental success rates for AI-generated enzymes, supporting the potential to reduce costly laboratory iterations. These developments are creating demand for AI-powered protein engineering platforms that integrate sequence, structural, functional, and experimental data. However, adoption may be constrained by limited high-quality training data, challenges in model benchmarking and validation, computational requirements, interpretability limitations, proprietary datasets, and the continued need to experimentally validate AI-generated proteins.

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Prominent players 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. These companies adopted strategies such as product launches, expansions, agreements, partnerships, collaborations, and acquisitions to strengthen their market presence in the AI in protein engineering market.

In March 2026, OpenProtein.AI expanded its strategic partnership with Boehringer Ingelheim to co-develop AI-powered antibody discovery and optimization workflows. The collaboration integrates OpenProtein.AI’s foundation models and cloud platform into Boehringer Ingelheim’s therapeutic development processes, enabling sequence analysis, binding prediction, generative AI-based variant design, and custom model training using proprietary assay data.

In January 2026, Isomorphic Labs entered a cross-modality, multi-target research collaboration with Johnson & Johnson, combining AI-driven in-silico prediction and design with experimental validation to advance small molecules, antibodies, peptides, and molecular-glue programs.

Schrödinger, Inc. has established a strong position in the AI-driven protein engineering and computational drug discovery market through its physics-based computational platform, which integrates molecular modeling with machine learning to accelerate the design and optimization of therapeutic molecules and biologics. The company supports pharmaceutical and biotechnology organizations in protein and antibody modeling, molecular design, and optimization across multiple therapeutic modalities. In January 2026, Schrödinger entered into a multi-year strategic agreement with Manas AI, providing the company with ultra-large-scale access to Schrödinger’s computational molecular discovery platform and priority technical support. The collaboration integrates Schrödinger’s physics-based modeling capabilities with Manas AI’s machine-learning algorithms to improve predictive accuracy and discovery speed, including identifying novel binders such as nanobodies. The agreement further strengthens Schrödinger’s position in AI-enabled protein and binder discovery by showing how its physics-plus-AI platform supports large-scale computational workflows.

XtalPi leverages artificial intelligence, physics-based modeling, and automated experimentation to support the design and optimization of proteins and other biological molecules across pharmaceutical and non-pharmaceutical applications. Its technology portfolio enables computational protein design, molecular generation, and experimental testing, allowing customers to address complex discovery and engineering challenges. In February 2025, XtalPi partnered with CyberplantX to develop an AI-powered “Intelligent Evolution Engine” for crop improvement. The initiative combines XtalPi’s AI-based protein design capabilities with CyberplantX’s plant genomic variation and phenotype data to identify and engineer proteins associated with plant stress resistance, regeneration, and environmental adaptability. By extending its computational protein engineering capabilities into agricultural biotechnology, the collaboration highlights XtalPi’s ability to apply AI-driven biological design beyond therapeutic discovery and address applications such as climate-resilient and high-yield crop development.

Dassault Systèmes is expanding the use of AI and computational modeling in protein-related applications through its BIOVIA platform, which provides tools for molecular simulation, formulation development, and biological data analysis. The company is applying these capabilities across life sciences, food, and consumer products. In June 2025, BIOVIA collaborated with Shiru to combine its formulation design technologies with Shiru’s Flourish AI platform for protein discovery. The initiative uses Shiru’s extensive protein sequence database and AI models to identify proteins with desirable functional properties, while BIOVIA technologies help evaluate their suitability within formulations. This collaboration broadens Dassault Systèmes’ protein engineering applications beyond traditional drug discovery, particularly toward the development of novel protein ingredients for food, beverage, beauty, and personal care products.

Market Ranking

The AI in protein engineering market includes diversified AI and computational science providers alongside specialized protein design and engineering companies that enable protein discovery, design, optimization, and validation. Schrödinger, Inc., XtalPi, Dassault Systèmes, NVIDIA Corporation, and Generate:Biomedicines are positioned among the leading players, supported by established computational platforms, AI capabilities, proprietary datasets, and broad applications across protein and biologics discovery. Other prominent participants include Absci Corp., Insilico Medicine, Isomorphic Labs, Chai Discovery, EvolutionaryScale, Profluent, BigHat Bio, Cradle, A-Alpha Bio Inc., Latent Labs, Basecamp Research, Nabla Bio Inc., and LabGenius Limited, which are advancing generative AI, protein language models, structure-based AI, machine learning, and automated protein engineering workflows. The competitive landscape also comprises emerging and specialized companies such as Arzeda Corporation, Evozyne, Menten AI, ProteinQure, Outpace Bio, AI Proteins, MonodBio, Biomatter Inc., and Diffuse Bio, with capabilities spanning enzyme engineering, de novo protein design, antibody engineering, protein therapeutics, protein-protein interaction prediction, and computational protein optimization. Competition is being shaped by advances in generative protein design, foundation models, AI-designed antibodies, protein structure prediction, protein stability and developability optimization, and AI-guided experimental validation, while strategic collaborations, proprietary biological datasets, integrated computational and laboratory workflows, and expansion into therapeutic and industrial applications continue to influence the market landscape.

Related Reports:

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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