From Laboratory to Industrialization: Can Antibody Optimization Platforms Break Through the Multi-Objective Synergy Challenge?

The druggability and industrial value of antibody drugs depend heavily on multiple core molecular properties: binding affinity, structural stability, low immunogenicity, and high expression yield. Traditional antibody optimization relies on directed evolution and rational design paradigms, which have long been constrained by low library screening throughput, lengthy R&D cycles, and inherent trade-offs among multiple performance metrics, making it difficult to achieve a synergistic multi-objective optimum. To break through the iteration bottlenecks of the traditional DBTL (Design-Build-Test-Learn) cycle, AI-driven protein design technologies are progressively reconstructing the R&D paradigm in protein engineering.

I. Definition and Core Evaluation System of Antibody Optimization Platforms

1.1 Core Definition and Optimization Directions

An antibody optimization platform is a comprehensive technology system oriented toward the industrial translation of antibody drugs. Its core objective is to simultaneously achieve affinity maturation, thermostability improvement, immunogenicity reduction, and expression yield optimization, ultimately yielding antibody molecules that combine excellent biological activity with industrial producibility. The system covers the entire pipeline from molecular design, functional validation, performance iteration, to process adaptation, serving as the core technological foundation supporting antibody drugs from target discovery to clinical translation.

1.2 Core Quantitative Evaluation Metrics

The druggability evaluation of antibody drugs relies on a standardized and quantitative biochemical parameter system, where various metrics are mutually constraining—this is the fundamental reason why traditional optimization struggles to achieve synergistic improvements. Core metrics include:

Binding affinity: Quantified by the dissociation constant Kd (mol/L). Therapeutic antibodies typically need to reach nanomolar (nM) or even picomolar (pM) levels; the smaller the value, the stronger the binding.

Thermostability: Characterized by the melting temperature Tm (°C) and aggregation onset temperature Tagg, determining storage stability, process tolerance, and in vivo half-life.

Immunogenicity: Measures the risk of eliciting a host immune response, a prerequisite for clinical safety.

Expression yield: Transient expression is often measured in mg/L, while industrial stable cell line fermentation can reach g/L levels, determining large-scale production costs.

1.3 Standardized Assay and Validation Systems

Antibody functional assays strictly follow the national standard "General rules for enzyme immunoassay antibody detection" (GB/T 40265-2021). Enzyme-linked immunosorbent assay (ELISA) enables quantitative detection through the enzymatic chromogenic reaction of antigen-antibody-enzyme-labeled secondary antibody complexes; surface plasmon resonance (SPR) technology allows real-time measurement of binding kinetics; cell-based functional blocking assays directly verify the biological activity of antibodies.

The evaluation of antibody binding performance and that of industrial enzyme catalytic performance are comparable at the methodological level—both rely on standardized physicochemical parameters for objective quantification of molecular properties. However, their functional essences differ: the former focuses on antigen-specific binding, while the latter focuses on substrate catalytic hydrolysis. This universality provides a theoretical basis for a single platform to accommodate both antibody and industrial enzyme engineering.

II. Traditional Antibody Optimization: Bottlenecks and Limitations of the DBTL Cycle

Bottlenecks of Traditional DBTL Cycle

Traditional antibody optimization mainly relies on two paradigms—directed evolution and rational design—following the classic "Design-Build-Test-Learn" (DBTL) iterative logic. Directed evolution generates large mutant libraries (10⁶-10⁹ in size) via error-prone PCR, DNA shuffling, etc., and enriches beneficial variants through high-throughput display platforms such as phage display and yeast display. Rational design, on the other hand, performs targeted point mutations at key residues in the complementarity-determining regions (CDRs) based on antibody-antigen complex crystal structures.

Constrained by these paradigms, the traditional DBTL cycle suffers from four structural bottlenecks:

First, the conflict between library capacity and screening throughput. Mutant libraries can theoretically cover vast sequence space, but actual screening throughput is limited by display platforms and detection methods, leaving many potentially beneficial mutations undiscovered. A single project often involves tens of thousands of clones, with R&D cycles lasting 2–5 years.

Second, trade-off effects among multi-objective optimizations. Affinity enhancement often comes at the cost of reduced thermostability, and expression improvement may introduce immunogenicity risks. Single-metric improvements usually sacrifice other druggability attributes, making Pareto optimization difficult.

Third, the inherent sequence diversity limitation of natural antibody repertoires. Antibodies derived from animal immunization or patients are constrained by the immune system and cannot cover high-affinity binding modes for certain epitopes. For novel targets without known reference molecules, traditional approaches face substantially higher uncertainty.

Fourth, heavy reliance on manual wet-lab work and slow iteration. Each iteration requires manual execution of sequence synthesis, protein expression, purification, and assays, with feedback cycles taking weeks to months, failing to support rapid trial-and-error and performance refinement.

These bottlenecks compel the industry to move beyond purely experimental trial-and-error and make AI-driven intelligent protein design an industrial necessity.

III. Principles of AI Protein Design Technologies: From Zero-Shot Generalization to Atomistic Precision Modeling

AI Protein Design Zero-Shot & Atomistic Modeling

The rapid advancement of deep learning is shifting antibody R&D from “random screening and manual trial-and-error” toward “precision design and directed optimization.” In recent years, breakthroughs in zero-shot generation, atomistic de novo design, and precise evaluation of molecular interactions have provided core theoretical support for multi-objective synergistic optimization.

3.1 Zero-Shot Antibody Generation

Zero-shot protein design does not require training data on the specific target; it can directly generate candidate antibody molecules against the target epitope, perfectly suited for novel targets without reference sequences. In July 2025, a research team reported on bioRxiv a multimodal generative model, Chai-2, achieving a 15.5% average zero-shot antibody design hit rate across 52 novel targets without known PDB binders (20% for VHH, 13.7% for scFv), representing a >100-fold improvement over conventional computational methods. Around the same time, the Latent-X2 model, posted on arXiv, demonstrated, for the first time, zero-shot generation of drug-like human antibodies with high affinity and low immunogenicity, directly outputting candidate sequences meeting clinical developability criteria. The continued advancement of zero-shot technologies marks a transition from “screening known molecules” to “creating novel molecules” in antibody discovery.

3.2 Atomistic De Novo Design

In December 2025, an international research team published in Nature (2026, 649: 183-193) a study that achieved atomistically accurate de novo design of antibodies against user-specified epitopes by fine-tuning the RFdiffusion diffusion model combined with yeast display screening. The team designed VHH nanobodies against four targets, including Clostridium difficile toxin B and influenza hemagglutinin. Cryo-EM validation showed that the binding conformations of the designed antibodies closely matched the computational models—the main-chain deviation for the influenza hemagglutinin VHH was only 1.45 Å, and the CDR3 region deviation was as low as 0.8 Å. Initial affinities ranged from micromolar to nanomolar levels (varying by target), and after iterative optimization with OrthoRep affinity maturation, they could be improved to single-digit nanomolar. The study also demonstrated the feasibility of de novo design of scFvs (containing six CDRs).

3.3 Precise Evaluation of Molecular Interactions

The PLACER model provides a core tool for atomistic modeling of protein-small molecule interactions. A 2025 PNAS study (122 (45), e2427161122) reported that PLACER, as a graph neural network, can generate conformational ensembles of protein-small molecule systems and accurately assess the structural accuracy and pre-organization of active sites. The framework is in principle extensible to antibody binding interface evaluation and industrial enzyme active site optimization, although further experimental validation is needed for such applications.

IV. MatwingsVenus™ (晓鹜™)： Industrial-Scale Implementation of an Antibody Optimization Platform

Dry-Wet Closed-Loop Platform

Most current AI protein design models remain focused on academic research, addressing the theoretical question of “whether design is possible，” and cannot yet meet the industrial demands of “scalable, standardized delivery.” In April 2026, Matwings Technology released the conversational protein R&D agent Matwings Venus™ (晓鹜™)， which integrates the entire chain of AI algorithms, automated wet-lab experiments, and expert collaboration, positioning it as one of the industrial-grade protein optimization platforms that have achieved practical implementation in China.

4.1 Core Underlying Architecture

MatwingsVenus™ (晓鹜™) establishes a conversational dry-wet closed-loop system of “AI design – automated wet-lab – expert collaboration.” The platform is powered by a billion-scale real-labeled protein database (awarded second prize in the National Data Elements Competition), integrates over 200 professional design tools, 50 certified experts, and 30 domain-specific fine-tuned skill models. Users input tasks in natural language, and the platform automatically performs task decomposition, algorithm scheduling, sequence design, and performance prediction, covering mining, directed evolution, de novo design, and developability prediction across all scenarios.

4.2 Core Industrial Advantages

Compared with academic models, the core value of MatwingsVenus™ (晓鹜™) lies in its comprehensive upgrade of industrial capabilities. The platform compresses the traditional 2–5-year protein R&D cycle to 2–6 months, reducing the number of experimental clones from tens of thousands to around one hundred per project. In July 2026, Matwings Technology completed a Series A++ financing of several hundred million RMB, primarily for platform iteration and large-scale deployment in innovative drugs, the circular economy, and health & wellness.

4.3 Conversational Dry-Wet Closed-Loop Iteration Mechanism

The platform has established an autonomous intelligent communication and experiment scheduling system: after the AI agent completes molecular design, it automatically interfaces with plasmid ordering and experiment orchestration, driving automated robots to perform gene synthesis, protein expression, purification, and functional assays; the results are then automatically fed into the next AI iteration for continuous improvement. In June 2026, the platform officially launched recombinant antigen expression and antibody customization services, enabling the full “AI antibody design – wet-lab validation – performance iteration” chain.

4.4 Real-Target Project Validation

In a de novo design project against a novel immune checkpoint receptor, the platform demonstrated its multi-objective synergistic optimization capability. The target lacked homologous reference sequences, its surface was dominated by polar regions without conventional binding hotspots, and conventional optimization would have easily led to conflicts among affinity, stability, and yield.

Leveraging the MatwingsVenus™ (晓鹜™) agent, the platform automatically executed the entire computational design process, including scaffold screening, interface design, sequence optimization, immunogenicity prediction, and developability filtering. Automated wet-lab validation confirmed that multiple binder molecules exhibited clear cell-based blocking activity and high-affinity potential, preliminarily demonstrating the feasibility of multi-metric co-optimization. To date, the platform has delivered dozens of protein design projects, serving leading customers in innovative drugs, in vitro diagnostics, industrial enzymes, and nutritional health.

V. Cross-Industry Applications and Standardized Industrialization Closed Loop

Cross-Industry Applications

The AI multi-objective optimization technology of MatwingsVenus™ (晓鹜™) is broadly versatile, applicable not only to antibody drug development but also to industrial enzyme engineering and carbon-neutral biomanufacturing, forming a standardized, replicable industrial R&D closed loop.

Innovative drug sector: The platform can systematically scan the entire sequence space of antibody CDR regions, achieving multi-dimensional co-optimization of affinity, thermostability, expression yield, and immunogenicity while preserving epitope specificity. Leveraging protein-small molecule docking technologies, it can generate nanobodies, scFvs, cyclopeptides, and other candidate molecules against protein, peptide, and small-molecule targets, covering both innovative drug and IVD R&D needs.

Carbon-neutral biomanufacturing: Based on the universal logic of quantitative molecular performance evaluation, the platform can be transferred to industrial enzyme engineering. For key industrial enzymes such as PET depolymerases and industrial hydrolases, it enables multi-objective co-optimization of hydrolytic activity, thermostability, and acid/alkali tolerance. In an industrial case, Matwings Technology used the platform to increase the alkali resistance of a non-alkali-tolerant single-domain antibody by 4-fold within one year, and completed 5,000-L pilot-scale production verification.

Standardized R&D pipeline: The platform has established an efficient closed-loop R&D workflow: natural language input → agent task decomposition and tool invocation → candidate sequence output → automated wet-lab synthesis, expression, purification, and assays → experimental results fed back to the AI model → next iteration. This model replaces the traditional manual trial-and-error approach with a data-driven, self-iterating industrial system.

VI. Summary and Outlook

The traditional DBTL experimental paradigm is constrained by limited library screening, multi-metric trade-offs, and low iteration efficiency, making it difficult to achieve multi-objective synergistic optimization of antibodies. Academic algorithms such as RFdiffusion and PLACER have validated the theoretical feasibility of AI-driven precision protein design. The MatwingsVenus™ (晓鹜™) industrial-grade agent platform further fills the gaps of academic models in automated experimental closed-loop, standardized delivery, and scalable production, achieving the critical transition from "laboratory algorithms" to “industrial tools.”

In the future, antibody optimization platforms will continue to evolve in three directions: first, strengthening zero-shot design capabilities to reduce dependence on prior target data; second, refining multi-objective co-optimization algorithms to achieve Pareto optimality across affinity, stability, safety, and producibility; third, building a dry-wet closed-loop data flywheel, leveraging industrial experimental data to continuously improve AI models and accumulate sustainable, iterable core data assets.

Driven by both innovative drug industrialization and carbon-neutral biomanufacturing, AI-powered protein optimization platforms are becoming the core infrastructure bridging algorithmic theory and the biotech industry. From single-parameter optimization to multi-metric synergy, and from manual DBTL cycles to intelligent dry-wet closed-loop iteration, the R&D paradigm of protein engineering is being redefined. Fully integrated, closed-loop industrial AI optimization platforms will continue to lead the high-quality development of antibody therapeutics and green biomanufacturing.