AI protein is quietly rewriting the fundamental logic of the biotech industry

From breakthroughs in biopharmaceutical targets and efficient iteration of industrial enzymes to the development of green new materials and precise cosmetic ingredient design, proteins, as the core functional carriers of life systems, have always been a fundamental element of the biotechnology industry. However, for a long time, the inherent limitations of natural proteins and the inefficiencies of traditional R&D have constrained the large-scale application of the biotech industry.

With the deep integration of generative AI with structural biology and synthetic biology, AI protein technology has completely broken the boundaries of traditional R&D. It has brought protein research from the era of "natural screening and passive modification" to a new stage of "on-demand design, precision customization, and efficient iteration," becoming one of the most promising core tracks in the biotech field today.

1. Definition and Core Logic: From "Natural Screening" to "On-Demand Design"

1.1 What is AI protein technology?

The industry generally defines AI protein (AI-designed protein) as an artificially designed protein molecule produced by an intelligent R&D system built on large AI models and deep learning algorithms. These differ technically from naturally extracted proteins and traditionally evolved proteins. Traditional protein R&D mainly relies on massive screenings through directed evolution and rational design based on physical energy functions, with extremely high cost for trial-and-error wet lab experiments.

Traditional protein R&D has fundamental limitations: natural proteins are the product of billions of years of evolution, with fixed structures and functions. Humans can only make minor modifications and perform passive screening; traditional artificial modifications are limited by screening efficiency and can only explore the local sequence space around natural protein scaffolds, making it hard to break functional limits formed by natural evolution.

The core disruption of AI protein technology lies in demand-driven forward design logic: abandoning blind trial-and-error, starting from the "target function," using AI models to learn the patterns of massive protein data, and inversely deriving amino acid sequences and structures that match functional requirements, ultimately designing brand-new protein molecules with customized performance. Its technical paths include sequence optimization, structure reshaping, and de novo design, with de novo protein design representing the highest technical barrier in the industry.

Compared with traditional R&D, AI proteins can precisely optimize key attributes such as target affinity, structural stability, solubility, immunogenicity, and tolerance to temperature and pH, effectively addressing pain points like poor druggability of natural proteins and weak industrial applicability. It truly achieves "adapting proteins to industrial needs, rather than being limited by natural endowments."

1.2 Core Data and Industry Background

According to statistics from third-party organizations, the global AI protein engineering market is expected to reach about $1.5 billion by 2025 and is projected to grow to $4.75 billion by 2031 (CAGR of around 21.2%). The AI protein design market is about $610 million in 2025 and is expected to exceed $2 billion by 2032 (CAGR of about 18.6%). The AI protein structure prediction market is around $1.8 billion in 2025 and is expected to hit $6.62 billion by 2030 (CAGR of roughly 29.6%). This rapid market growth highlights that the industry is increasingly recognizing this technological approach.

2. Core Technology Principles: How Does AI Crack the Code of Life Molecules?

 Generative Protein Design Pipeline

The function of a protein is determined by both its amino acid sequence and its three-dimensional structure. The complexity of its folding patterns has long made the mapping between structure and function a 'mystery' in life sciences. The core value of AI protein technology lies in using data-driven approaches and algorithmic iteration to unlock the fundamental 'sequence–structure–function' relationship. Its technical logic can be broken down into three major parts:

2.1 Massive Data Training to Build a Protein Knowledge Graph

High-precision AI protein models rely on billions of labeled protein data and hundreds of billions of specialized text data for training. This covers regular biological protein sequences as well as special functional proteins from extreme environments like volcanoes and the deep sea. Nearly 500 million protein performance data points with clear functional annotations have been accumulated, allowing accurate matching of proteins’ temperature, pH, and pressure tolerance in different scenarios. At the same time, over 200 specialized protein design tools and 30+ expert knowledge databases are integrated to form a complete data foundation for protein R&D.

2. 2 Multi-Modal Algorithm Deduction for Accurate Prediction and Design

Unlike traditional methods that depend on comparing known protein similarities, the next-generation AI protein model uses a multi-model collaborative structure (combining large pre-trained protein models, molecular dynamics simulations, quantum chemistry calculations, etc.) to independently infer the structure and function of entirely new sequences without relying on homologous proteins. From amino acid sequence arrangement to secondary structure folding to tertiary 3D formation, full-dimension precise inference is achieved. Backbone structure prediction accuracy can reach the atomic level, and prediction accuracy for key interface interactions is greatly improved.

2.3 Dry and Wet Loop Iteration to Achieve Industrial Validation

Proteins designed purely by algorithms are only theoretically feasible. Only by completing the closed loop of 'AI design—automated experiments—data feedback iteration' can industrial application be realized. This is the key step for AI protein technology to move from academic research to industrial use. High-quality industrial platforms can solve the common problem of "design works in theory but is hard to apply" through dry and wet loop iteration, reducing traditional R&D cycles from years to just months.

3. Exploding Industrial Value: Reshaping the Core Logic of Trillion-Dollar Bio Industry

AI proteins are not just a simple technological upgrade—they represent a fundamental paradigm shift in the biotech industry. With four core advantages—shortened development cycles, reduced costs, performance breakthroughs, and diverse application scenarios—they are fully penetrating key areas like biomedicine, industrial biotechnology, and green new materials.

3.1. Innovative Drug Development: Breaking Core Barriers in Druggability

In the fields of antibody drugs, peptide drugs, and protein vaccines, AI proteins can precisely optimize the binding affinity of drug molecules to their targets, bringing the affinity of some nanobodies and mini binding proteins up to the picomolar level suitable for drugs. They also improve solubility, metabolic stability, and immunogenicity, significantly reducing side effects and increasing the success rate of drug development. Traditional new protein drug target research often takes 2-5 years, but AI technology can shorten this to 3-6 months while reducing R&D costs by over 60%.

Notably, the global peptide therapy market exceeded $49 billion in 2025 and is expected to surpass $54 billion in 2026. Of the more than a hundred peptide products approved, nearly half have been developed in the last 20 years, and AI is accelerating breakthroughs in this field.

3.2. Industrial Biotechnology: Empowering Green, Low-Carbon Production

Industrial enzymes are core tools in biomanufacturing. AI proteins can be used to design novel industrial enzymes that are heat-resistant, acid-alkali resistant, and highly catalytic, replacing traditional chemical processes and significantly reducing energy consumption and pollution in the chemical, food, and textile industries. Traditional industrial enzyme development requires years of trial and error, but with AI, it can be shortened to just a few months while improving enzyme activity and lifespan, supporting a green upgrade of the industry.

3.3. Advanced New Materials and Aesthetics: Expanding Molecular Applications

Leveraging AI’s de novo design capabilities, new protein materials with special mechanical, optical, and biocompatible properties can be developed for applications like biomedical consumables, high-end skincare ingredients, and biodegradable materials. This overcomes the performance limitations of natural protein materials and opens up entirely new industry sectors.

4. Industry Implementation Benchmarks: Industrial-Grade 'Wet-Dry Closed Loop' Platforms Becoming Mainstream

 Scenes from the 2026 World Artificial Intelligence Conference exhibition

Currently, AI protein technology is accelerating from academic research to industrial applications, and bridging the 'wet-dry closed loop'—that is, the seamless connection between algorithm design and automated experimental validation—has become a key benchmark for measuring the industrialization capability of platforms.

Take Matwings Technology's self-developed MatwingsVenus™ (Xiaowu™) conversational protein R&D intelligent agent platform as an example. This platform, launched in April 2026, uses natural language interaction as the entry point and integrates AI design, tool scheduling, and automated experimental validation functions. Users can give conversational commands like 'design a protein targeting X' or 'increase the thermal stability of this enzyme,' and the AI agent will automatically break down the tasks, schedule over 200 protein design tools on the platform, and search through a protein database containing billions of actual labeled sequences, completing the full workflow from sequence design, structure prediction, to functional screening.

Even more crucially, AI design results can directly connect to the automated experimental platform, where robots handle sample preparation, protein purification, and functional testing, and the experimental data automatically flows back into the AI model for the next iteration—so users don’t have to manage multiple vendors or manually organize data, truly achieving the 'design as validation, validation as iteration' closed loop.

So far, the platform has been validated through multiple industrial projects: from de novo design of plastic-degrading enzymes (completed in six months with performance surpassing similar international R&D over ten years), to multi-round modifications of the sweet protein Monellin (increasing sweetness by dozens of times while maintaining heat tolerance at 75°C), as well as progress on VHH single-domain antibodies and immune regulatory receptor binders. At the 2026 World Artificial Intelligence Conference, MatwingsVenus™ (Xiaowu™) was selected as a 'treasure of the venue,' reflecting the trend of AI proteins moving from niche tracks to the main stage of industry.

5.Conclusion: R&D capabilities are shifting from being a 'resource barrier' to an 'idea barrier.'

The global bio-industry is shifting from the 'traditional experiment-driven' model to an 'AI data-driven' approach. AI proteins, as a core underlying technology for synthetic biology, biomanufacturing, and innovative medicine, are reshaping both the R&D paradigm and the industry landscape.

In the past, high-precision protein research was exclusive to leading pharmaceutical companies and top research institutions—it required large computing teams, self-built experimental platforms, and a hefty budget. But the development of AI protein technology is making this capability more platform-based and widely accessible. Just like cloud computing moved server resources from company data centers to the cloud, allowing small teams to access top-tier computing power, AI protein platforms package formerly multi-team collaborative R&D work into a simple conversational interface. Researchers just need to clearly describe their needs to access the full capability from AI design to automated experiments.

This is the real paradigm shift: the barrier to protein research is moving from 'resources' to 'ideas.' As more teams can now participate in protein innovation, and ideas can be rapidly tested and iterated, the pace of innovation in the bio-industry will soar to new heights.

In the future, with continuous improvements in AI model accuracy and ongoing refinement of automated experimental systems, protein research will gradually achieve end-to-end intelligence, customization, and scalability. Industrial-grade AI protein platforms with 'algorithms + data + closed-loop wet-dry processes + industrial application capabilities' will become core competitive advantages, continuously empowering cost reduction and efficiency in biomedicine, green transformation in industrial manufacturing, and breakthroughs in new materials.

AI is no longer just an auxiliary tool in bio R&D—it’s becoming the core productivity force that is reshaping the life-molecule industry. Programmable AI proteins are unlocking endless possibilities in life sciences, kicking off a new growth cycle for the bio-economy.