Protein research software is changing the game in biological research

Proteins are the main performers of life's functions and are also core molecular tools in biomedicine and synthetic biology. However, the sequence space of proteins is so vast that it’s truly daunting—take a protein made of 100 residues from 20 amino acids, for example, the theoretical number of possible sequences far exceeds the total number of atoms in the universe. Searching for functional molecules in this enormous space using traditional methods is like looking for a needle in a haystack.

Artificial intelligence is fundamentally changing this situation. At the heart of this revolution are a bunch of new protein research software and platforms.

1. From 'wet lab screening' to 'intelligent-driven'

 AI-driven protein research and development

Traditional protein R&D is essentially a high-cost, low-efficiency trial-and-error path. From discovery to preclinical development, an antibody typically takes months or even years, requiring extensive wet experiments for repeated screening and validation. The involvement of AI has enabled bioresearch to leap from "passive interpretation" to "active design." Scientists no longer need to blindly pan for gold in massive molecules; instead, they can "create" entirely new molecules from scratch based on specific needs.

According to market research data, the structural bioinformatics software market will grow from $1.56 billion in 2025 to $1.71 billion in 2026, with a compound annual growth rate of 10.1%, and is expected to reach $2.54 billion by 2030. Behind this rapid growth lies a true reflection of the industrialization of AI-driven protein design tools.

AI for Science is evolving from an auxiliary tool into an autonomous research agent. The evolution of AI for Science has entered the 3.0 stage, where AI can autonomously complete the entire closed-loop process of "designing experiments→ executing experiments→ analyzing results→ iterative optimization." Protein research software is no longer just a simple computing plugin, but a true reshaping of the underlying R&D infrastructure.

2. Core capabilities of protein research software

Currently, mainstream protein research software and platforms typically build core capabilities in the following dimensions:

Structure prediction is fundamental to protein research. Structural prediction tools centered on deep learning and generative models can efficiently predict the three-dimensional structure of proteins by using amino acid sequences and simulate interactions with other molecules. In recent years, this field has continuously achieved breakthroughs in both precision and speed.

Functional design is a higher goal. From directed evolution to redesign, from enzyme activity modification to antibody discovery, AI empowers scientists with the ability to "design and get what you want." For example, in protein design, methods that integrate AI and first principles have overcome the bottleneck of ultra-high-precision molecular dynamic structure prediction, greatly improving simulation accuracy and efficiency, reaching industrial-grade levels.

In addition, dynamic simulation capabilities are also rapidly evolving. Proteins are not static structures; they continuously move and undergo conformational changes in physiological environments. Generative deep learning systems like BioEmu can generate thousands of conformation samples per hour on a single GPU, significantly outperforming traditional MD simulation in conformational sampling speed.

3. Lowering the barrier: Making AI accessible to every biologist

Matwings Technology

Another key trend in protein research software is 'democratization,' making it easy for researchers without an AI background to get started. Conversational interaction is becoming standard. Users only need to input their task goals in natural language, and the system can automatically break down the tasks, call the appropriate tools, and carry out the design process. This interaction innovation significantly lowers the barrier to using AI tools.

Take Tianwu Tech’s conversational protein R&D agent MatwingsVenus™ (Xiaowu™) as an example. This platform is built around the agent to create a one-stop protein research and development platform. It supports retrieval of billions of real-labeled protein data, integrates over 200 protein design tools, and more than 30 expert-tuned skills. Users just need to input their task goals in natural language, and the system can automatically break down the tasks, coordinate design, prediction, analysis, and screening capabilities, completing a series of tasks like deep research, enzyme mining, directed evolution, and de novo design.

Even more noteworthy is that a truly 'one-stop' platform has gone beyond pure computational services and moved into deep collaboration between cloud design and physical experiments. After the agent completes the design, the platform can directly link the results to automated shared labs, driving robots to perform sample preparation, protein purification, and functional testing, and finally feed the experimental results back into the next round of AI design — creating a 'conversational dry-wet loop' where computation drives wet experiments and wet experiments inform computation.

4. From Algorithms to Implementation: Value Verification in Real Cases

 Case Study

The industrial value of protein research software must ultimately be validated in real R&D scenarios.

In the biopharmaceutical field, Tianwu Technology partnered with Kingsai Pharmaceuticals, a leading domestic growth hormone company, to quadruple the alkali resistance of a conventional non-alkali-resistant monodomain antibody in just four months, achieving large-scale industrial production of 5,000 liters. This became the world's first industrialized case of large model protein design, saving the company over ten million yuan in annual costs.

From enzyme mining and performance optimization to cross-disciplinary R&D implementation, MatwingsVenus™ has ™ completed the entire process of "design—validation—iteration" across multiple real-world projects. These cases show that the value of protein research software lies not only in "fast calculation," but also in integrating design, validation, and iteration into a unified intelligent framework, with real industrial application scenarios serving as the best measure of its value.

5. The Future Is Here: The Evolutionary Direction of Protein Research Software

Looking ahead, there is still vast room for the evolution of protein research software.

Conversational scientific research agents will continue to deepen. With the improvement of large model capabilities, AI can not only perform specific tasks but also conduct industry research, literature retrieval, and experimental protocol design, truly becoming a "digital partner" for researchers.

The wet and dry closed loop will become the industry standard. The shift from "selling software only" to "selling services/selling molecules" means protein research platforms are evolving from simple algorithm providers into full-stack R&D infrastructure capable of delivering actual molecular products.

The balance between generality and exclusivity is still being explored. General-purpose protein large models demonstrate strong capabilities across various tasks, but specialized tools still maintain advantages in certain specific scenarios. How to combine the strengths of both is an ongoing challenge in platform design.

Biologists no longer need to be AI experts, but AI can become a super tool accessible to every biother. This vision is turning from ideal to reality, driven by protein research software.

When protein research shifts from "searching for a needle in a haystack" to "precise design," and when AI replaces tedious trial-and-error experiments, scientists will be able to devote more energy to strategic scientific judgment and innovative thinking. This is not only an improvement in efficiency but also a fundamental transformation in life sciences research models. Protein research software is the core driving force behind this transformation.