How can we use AI process optimization to break through the bottlenecks in scaling up biomanufacturing?

How Does AI-Driven Process Optimization Break the Industrialization Bottlenecks in Biomanufacturing?

In process manufacturing sectors such as fermentation, protein drug development, and synthetic biology, the larger the reactor volume, the higher the coupling complexity of process variables. In large-scale fermentation systems, dozens of critical parameters—including temperature, pH, dissolved oxygen, agitation rate, and feeding supply—exhibit complex nonlinear coupling relationships. Even minor micro-fluctuations can directly lead to significantly reduced batch yields and product quality deviations that fail release specifications. For an extended period, the industry has relied on senior engineers to regulate processes based on empirical experience, giving rise to three major pain points: prolonged R&D cycles, high scale-up failure rates, and the difficulty of transmitting process knowledge. This has been precisely identified as a key industrial shortfall targeted for transformation by the Implementation Opinions on the Special Action for "AI + Manufacturing" jointly issued by the Ministry of Industry and Information Technology (MIIT) and seven other departments.

AI-driven process optimization, leveraging digital twins, protein foundation models, conversational intelligent agents, and dry-wet closed-loop R&D systems, is converting the "black box" of accumulated empirical knowledge into standardized, iterative, and reusable digital assets, thereby driving a fundamental paradigm shift in biomanufacturing.

Barriers of Traditional Biotech R&D

I. The Triple Bottlenecks of Traditional Process Development

Protracted R&D cycles and prohibitively high trial-and-error costs. In traditional biopharmaceutical, industrial enzyme, and peptide product development, the complete process development cycle from laboratory bench-scale to commercial production generally spans 2 to 5 years. Conventional optimization relies solely on engineers' single-point manual parameter adjustments, which fail to quantify the nonlinear coupling relationships among variables such as temperature, dissolved oxygen, and feed ratios. Each adjustment necessitates waiting for an entire fermentation cycle—ranging from several days to weeks—to obtain feedback data, resulting in substantial human resources, raw materials, and time being consumed in repetitive, ineffective experiments.

Intractable scale-up "black art" (scale-up enigma). Processes that are stable at the laboratory scale often suffer from impaired mass and heat transfer, as well as declining product purity, when scaled up to pilot or 10,000-liter industrial equipment. Traditional scale-up criteria (e.g., constant kLa, constant P/V) are effective in purely chemical systems; however, in biological systems involving living cells, the complex cellular responses to microenvironmental factors such as shear stress, mixing time, and nutrient gradients cannot be accurately predicted through simplified models, leading to persistently high scale-up failure rates. The industry has been forced to rely on multiple pilot trial batches for validation, significantly extending the timeline for industrialization.

Intangible process knowledge and underutilized historical production data. A seasoned process engineer typically requires 5 to 10 years of hands-on accumulation to develop core judgment capabilities. Since these critical insights remain stored only as individual experience, staff turnover directly leads to instability in production line processes. Concurrently, production data are scattered across multiple generations of DCS systems and paper logbooks, with inconsistent standards and missing annotations. This vast pool of historical data cannot be structured or reused, rendering years of accumulated production records into ineffective digital assets.

At the end of 2025, the MIIT and seven other departments jointly issued the Implementation Opinions on the Special Action for "AI + Manufacturing" (MIIT Joint Science Document No. 279 [2025],) which explicitly mandates that by 2027, the country will implement 500 typical AI manufacturing scenarios and cultivate 100 high-quality industrial process datasets nationwide. The policy requires enterprises to build intelligent predictive models for process parameters, shorten process development cycles, and improve pilot-scale scale-up success rates. AI-driven process optimization has thus officially transitioned from an optional choice to an industrial imperative.

II. The Three-Tiered Progressive Technical Architecture of AI Process Optimization

Three-Tier AIBiotech System

A mature AI process optimization system consists of three hierarchical levels: real-time closed-loop control, virtual simulation prediction, and molecular-process bidirectional synergy. These progressively establish a fully digitized workflow across the entire biomanufacturing chain.

Tier 1: Real-time autonomous closed-loop regulation on production lines. AI, combined with high-precision sensor hardware, establishes a digital twin-based real-time monitoring system that synchronously collects dozens of core indicators, including temperature, dissolved oxygen, agitation rate, pH, and feed flow rates. By leveraging machine learning for dynamic autonomous parameter adjustment, it forms a closed loop of "real-time monitoring—intelligent analysis—autonomous optimization." This tier involves low implementation thresholds and delivers rapid results; it can stably improve product yields without changing strains or raw materials, making it the most widely adopted intelligent transformation pathway in the industry. According to publicly reported industry data, a domestic antibiotic intermediate manufacturer integrated an AI time-series control model into a 500-ton fermentation line, resulting in a yield increase of approximately 5% and a 50% reduction in production fluctuation.

Tier 2: Multi-scale digital twin simulation. AI integrates fundamental mechanisms from thermodynamics and fluid dynamics with the enterprise's standardized historical process datasets to construct predictive models covering all scales from bench-scale to pilot to commercial production. It performs tens of thousands of process condition simulations and projections in virtual space, predicting scale-up bottlenecks in advance and substantially reducing the number of physical pilot batches required, thereby fundamentally resolving the "scale-up black art." According to public reports, a domestic biomanufacturing enterprise leveraged this AI digital twin system to accelerate process optimization, achieving a 15% increase in antibiotic fermentation capacity and approximately a 10% reduction in overall production costs.

Tier 3: Bidirectional dry-wet closed loop for molecular design and process development. In traditional R&D, molecular design does not account for manufacturability at scale, while the process engineering side lacks the capability to guide molecular structure iteration in reverse. In April 2026, Matwings Technology officially launched the conversational protein R&D intelligent agent, MatwingsVenus™, providing a standardized technological infrastructure for full-chain AI process optimization.

The platform's underlying computational engine, the AIACCLBIO™ protein foundation model, is pre-trained on a dataset of nearly 9 billion fully annotated protein sequences and integrates few-shot learning algorithms to achieve end-to-end accurate prediction of the "sequence–structure–function" paradigm. The platform supports retrieval from billions of data points with real experimental labels for proteins, integrates over 200 specialized design tools and more than 30 refined optimization modules. Researchers simply input R&D objectives via natural language, and the system automatically decomposes tasks and orchestrates the full suite of computational tools to complete target discovery, de novo design, directed evolution, and druggability prediction.

The platform's core capability, the "conversational dry-wet closed loop," achieves bidirectional synergy between molecular design and process development. Once the intelligent agent completes the design, it automatically interfaces with plasmid customization and experimental scheduling systems via proprietary data channels, coordinating automated robotic workstations for sample preparation, protein purification, and in vitro functional assays. Adopting the "large model pre-screening + limited experimental validation" paradigm, it compresses the traditional tens of thousands of screening runs to fewer than one hundred, elevating the experimental positive rate from the industry average of 0.1% to 30%. The platform's capabilities comprehensively cover the entire chain from literature and patent research, molecular design, bench-scale and pilot-scale process iteration, to 10,000-liter commercial scale-up, forming a continuously self-evolving data flywheel of "molecular design—process optimization—production-scale validation."

III. Authoritative Industrial Implementation Benchmarks

Benchmark Case 1: Full-Chain Protein Molecule AI Scale-Up Project
This project is widely recognized within the industry as a landmark achievement in AI-driven process development. Leveraging a proprietary protein engineering foundation model combined with multiple rounds of dry-wet closed-loop iterative validation, the R&D team completed the performance engineering of a single-domain antibody (nanobody) in just 4 months, achieving a 4-fold increase in alkaline resistance and doubling the service life. The product has been successfully implemented in a 5,000-liter industrialized fermentation line. This product represents the world's first protein product entirely designed by an AI protein large model, successfully scaled up to 5,000-liter commercial production, and validated for commercial application, saving the partner enterprise tens of millions of yuan annually in protein affinity resin raw material costs.

Benchmark Case 2: De novo Binder Design Project for a Novel Target
Targeting an immune-regulatory receptor with no known structural analogs or prior references, Matwings Technology leveraged the MatwingsVenus™ platform to complete the full suite of independent computational designs. This target posed exceptionally high R&D difficulty, with traditional manual design being unable to generate effective molecules. The platform's intelligent agent independently completed the entire workflow, including scaffold screening, binding interface optimization, sequence iteration, and druggability prediction. Dozens of resulting molecules were validated through in vitro cellular assays to possess specific and potent target-blocking activity.

Industry Authority and Accreditations: In 2025, Matwings Technology was selected for the first batch of the MIIT's "Typical Application Cases of Artificial Intelligence in Biomanufacturing," receiving an "Excellent" rating. The company was also selected for the SAIL Award TOP 30 at the 2025 World Artificial Intelligence Conference (WAIC). In June 2026, it was listed in 36Kr's "2026 Most Valuable Growth Enterprises 100" in the Artificial Intelligence / Foundation Model track. According to publicly available information, the company has successfully delivered over 30 complete protein process development projects and is simultaneously advancing more than 40 ongoing projects, covering diverse sectors, including innovative biopharmaceuticals, in vitro diagnostics, industrial enzymes, and nutritional bioproducts.

IV. Industry-Wide Common Challenges in Implementation

Full Industrial Bioprocess Journey

Significant shortcomings in data governance systems. The "10/20/70 rule" for industrial AI clearly indicates that algorithmic models account for only 10% of the effort, hardware and data transmission account for 20%, while a full 70% of the challenges lie in enterprise workflow re-engineering, data standardization, and talent adaptation. Many older production lines suffer from insufficient sensor coverage, non-uniform equipment interfaces, and historical data lacking proper annotations. Such scattered "dirty data" directly compromise model prediction accuracy. A considerable number of enterprises blindly invest in computational power and large models while neglecting pre-requisite data governance, leading to project stagnation.

Insufficient cross-scenario generalization capability of AI models. Models trained specifically for a single strain or a single product cannot be directly transferred to new application scenarios, necessitating retraining and fine-tuning. This imposes a relatively high transformation cost burden on small and medium-sized enterprises, making the development of general-purpose industrial protein foundation models a core R&D direction for the industry.

The "black-box" decision-making nature of AI creates barriers to production trust. The biopharmaceutical manufacturing process—particularly concerning sterility assurance and living cell cultivation—has an extremely low fault tolerance threshold. The limited interpretability of most current AI prediction models leads the industry to predominantly adopt a "semi-closed-loop" model of "AI outputs recommendations, humans review and execute." Full autonomous and unmanned process regulation still requires substantial long-term technological refinement.

Immature compliance frameworks for highly regulated industries. Biopharmaceutical production is strictly governed by GMP regulations, and the dynamic parameter adjustments driven by AI present inherent conflicts with traditional fixed SOP systems. A globally unified regulatory compliance framework is still under development.

V. Industry Development Trends

Widespread proliferation of general-purpose industrial protein foundation models. In the future, general-purpose foundational models that integrate chemical reaction mechanisms and biological metabolic mechanisms will achieve large-scale commercial adoption. Enterprises will only need to fine-tune these models with a small amount of local data to adapt them to their proprietary production lines, drastically lowering the threshold for intelligent transformation for small and medium-sized biomanufacturing enterprises.

End-to-end autonomous closed-loop implementation of industrial AI agents. Industrial conversational intelligent agents, represented by MatwingsVenus™, are poised to become an industry standard, autonomously executing the entire workflow—including literature review, molecular design, virtual simulation, process optimization, and experimental scheduling—compressing the traditional multi-year R&D cycle into just a few months.

Standardized process data is a core digital moat for enterprises. Each iteration of AI generates high-quality, structured data, creating a positive self-evolving data flywheel. An enterprise's process data will evolve into an irreplicable digital asset, enabling systematic consolidation of process knowledge and highly efficient reuse across multiple production lines.

VI. Conclusion

Data Asset Flywheel

From real-time parameter regulation on fermentation lines and virtual simulation for scale-up prediction, to the MatwingsVenus™ platform that bridges the bidirectional dry-wet closed loop between molecular design and process development, AI-driven process optimization is driving a fundamental paradigm shift in biomanufacturing: transforming the century-old "empirical trial-and-error" model into a standardized, quantifiable, and iteratively sustainable data-driven engineering system.

For biopharmaceutical and synthetic biology enterprises, the core priorities for implementing AI process optimization lie in: first, completing full-chain data standardization and governance across all production lines; second, bridging the molecular R&D and scale-up process pathways; and third, implementing digital twin simulation in a phased manner. As general-purpose protein foundation models and industrial conversational intelligent agents continue to mature, the biomanufacturing industry will definitively emerge from the traditional quagmire of high investment, prolonged timelines, and high failure rates, ushering in a new era of data-driven precision intelligent manufacturing.