Protein Structure Prediction Tools: From 'Single-Point Prediction' to 'Full-Chain Agents'

Since 2018, protein structure prediction tools represented by AlphaFold have completely transformed structural biology. The high-precision predictions of AlphaFold 2 and 3 won the Nobel Prize, and over 200 million protein structures have been predicted by AI. On this basis, RFdiffusion 3 developed by the University of Washington can predict the structures of any biomolecular complexes, and tools like GRASP (which integrates multi-source experimental information for complex modeling) are continuously expanding the boundaries of prediction capabilities.

However, a practical problem has emerged: after predicting the structure, then what?

For the vast majority of R&D teams, what is ultimately needed is not a beautiful 3D model, but a protein molecule with confirmed functionality and performance—suitable for practical scenarios such as drugs, enzymes, and biomaterials. From sequence input to functional protein delivery, the process involves a series of steps including industry research, database searches, directed evolution, de novo design, sequence optimization, druggability assessment, and wet lab validation. Each step often relies on different specialized tools, making the switching cost high, and non-specialized teams find it difficult to run the entire process smoothly in a short time.

A clear trend is emerging: the role of AI in the protein field is shifting from 'predicting static structures' to 'assisting dynamic design,' and further moving toward full-process automation. Structure prediction is only the starting point; the real value lies in embedding predictive capabilities into a complete design and validation loop.

MatwingsVenus agent

MatwingsVenus™ (Xiaowu™) Intelligent Agent: From Single-Point Tool to Conversational R&D Infrastructure

In April 2026, Matwings Technology launched the conversational protein R&D intelligent agent MatwingsVenus™ (Xiaowu™). It is not just a platform that integrates multiple prediction functions; its core design concept is to center on the intelligent agent, linking computational design, automated wet experiments, and expert interaction, extending digital intelligence into the physical experimental world.

Specifically, this is reflected in three aspects:

1. Natural Language-Driven Task Collaboration: Users input task objectives via natural language dialogue (e.g., "design a binder for a specific immunoregulatory receptor"), and the system automatically decomposes the task, dispatching relevant design, prediction, analysis, and screening capabilities. This means you don’t need to master multiple complex software and database operations to initiate a complete R&D workflow.

2. Full-Stack Capability Integration: Supports retrieval of tens of billions of real-labeled protein data, integrates 200 protein design tools, 50 platform-certified experts, and 30 domain-specialized skills optimized by various experts, covering mainstream application scenarios such as de novo design, enzyme mining, directed evolution, and protein design.

3. "Design as Validation, Validation as Iteration" Wet-Dry Closed Loop: This is the biggest difference between MatwingsVenus™ (Xiaowu™) and traditional protein structure prediction tools. Traditional tools output a static 3D model; Xiaowu, after completing the design, can automatically link candidate sequences to an automated shared laboratory—through a self-built communication mechanism, the design results are imported into plasmid ordering and scheduling workflows, driving robots to complete sample preparation, protein purification, and functional testing, with experimental results fed back into AI models for the next iteration. Computation drives wet experiments, and wet experiments inform computation, forming a closed loop.

What it means for industry practitioners

The implementation of the MatwingsVenus™ (XiaoWu™) intelligent agent is, in fact, the inevitable result of the paradigm evolution of AI-driven protein science — moving from a 'predictive tool' to a 'full-stack intelligent agent,' from 'structurally accessible' to 'functionally deliverable.'

For companies and research teams, this means a significant increase in R&D efficiency. For individuals and small teams, it means that complex R&D capabilities, which in the past were only mastered by large enterprises and major research institutions, are now transforming into infrastructure that individuals can access.

Current protein structure prediction tools are already powerful enough, but they are likely to be a component of future R&D workflows rather than the end point. The 'conversational dry-wet closed-loop' intelligent agent represented by MatwingsVenus™ (XiaoWu™) may be providing a new direction for the industry: allowing AI to move beyond being a single-point auxiliary tool and truly take over complex scientific processes, enabling researchers to focus on asking better scientific questions.