Viral vectors remain the cornerstone of modern gene therapy, enabling targeted delivery of therapeutic genes to specific tissues. As the pipeline of gene- and cell-based therapies expands, the complexity of vector engineering and

manufacturing has surged. In response, artificial intelligence (AI) and machine learning (ML) are transforming the design, optimization, and scale-up of viral vectors, driving greater precision, efficiency, and safety. This article explores five key areas where AI is reshaping viral vector technology:

1. Market Dynamics and AI Adoption
2. Next-Generation Vector Engineering
3. AI-Driven Capsid Discovery and Licensing Deals
4. AI in Bioprocessing and Manufacturing
5. Clinical Impact and Future Outlook

1. Market Dynamics and AI Adoption

The global viral vector and plasmid DNA manufacturing market is projected to grow from $2.48 billion in 2025 to over $10.66 billion by 2034, at a CAGR of 15.7%. Two major drivers behind this expansion are:

• Surging Gene Therapy Demand: New approvals—such as Novartis’s Zolgensma for spinal muscular atrophy and uniQure’s Hemgenix for hemophilia B—have demonstrated both clinical success and commercial viability, fueling capacity needs.
• AI in Biomanufacturing: Companies are integrating AI for real-time bioreactor control, predictive maintenance, and digital-twin simulations, enhancing consistency and reducing production costs.

These trends indicate that maturity in AI adoption is now a core competitive factor in viral vector supply chains.

2. Next-Generation Vector Engineering

Historically, viral vectors were derived from natural serotypes with limited tissue specificity, manufacturing yields, and immunogenicity profiles. Recent advances illustrate how novel plasmid backbones and capsid modifications are overcoming these barriers:

• Specialized Plasmid Tools: In March 2024, Polyplus (a Sartorius division) launched PLUS® AAV-RC2, an innovative recapture plasmid that streamlines production of AAV2 vectors by improving packaging efficiency and yield. This reagent is designed for both research and commercial-scale manufacturing, reducing upstream variability and cost per dose.
• Capsid Fitness Prediction with ML: A publication in Human Gene Therapy described CAP-PLM, a protein-language-model-based ML approach that predicts the “fitness” (i.e., yield and stability) of AAV2 capsid mutants directly from amino acid sequences. With a Pearson correlation of 0.818, this in silico model can replace labor-intensive screening and accelerate the identification of optimized capsids for clinical use.

Together, these developments show how AI-informed design is moving from experimental to routine practice, enabling bespoke vector platforms tailored to each therapeutic indication.

3. AI-Driven Capsid Discovery and Licensing Deals

AI has also catalyzed strategic partnerships between pharma and vector-engineering startups:

• Roche–Dyno Therapeutics Collaboration: In October 2024, Roche agreed to pay $50 million upfront, with potential milestones exceeding $1 billion, to license Dyno’s AI-powered CapsidMap platform. This partnership aims to generate novel AAV capsids with improved tissue targeting, immune evasion, and manufacturability, particularly for neurological and ophthalmic gene therapies.
• NVIDIA and Dyno Alliance: Dyno announced in May 2024 that it is leveraging NVIDIA’s BioNeMo cloud platform to accelerate its ML-driven sequence design pipeline. The collaboration combines Dyno’s high-throughput in vivo screening data with NVIDIA’s AI infrastructure, shortening design cycles from months to weeks and enabling iterative improvement of vector properties at scale.

These deals underscore how big-pharma is banking on AI startups to deliver next-generation vectors, sharing risk and resources to stay at the forefront of gene delivery technology.

4. AI in Bioprocessing and Manufacturing

Beyond design, AI is revolutionizing viral vector production across the entire workflow:

• Predictive Bioreactor Control: AI-powered control systems dynamically adjust parameters such as pH, temperature, and nutrient supply, maximizing vector yields and minimizing batch failures. Real-time sensor data feed ML models that optimize perfusion rates and harvest timing, cutting process development timelines by up to 30%.
• Quality-by-AI: Automated image recognition and deep-learning tools monitor contaminants, cell morphology, and purity metrics during downstream processing. For instance, AI-driven NGS and qPCR analysis validate capsid integrity and genome copy number, ensuring batch-to-batch consistency without manual intervention.
• Digital Twins and Scale-Up: Virtual replicas of manufacturing lines allow engineers to simulate scale-up scenarios, testing new bioreactor geometries or purification resins in silico before committing to capital-intensive builds. This approach reduces risks in high-value commercial facilities, as seen in case studies by Form Bio and Charles River Laboratories, which integrate AI data streams to predict vector titer and optimize filtration performance.

Collectively, these innovations are lowering the cost of goods and accelerating timelines from laboratory to clinic.

5. Clinical Impact and Future Outlook

The convergence of AI and vector engineering is already enabling novel therapies and expanding the pipeline:

• Clinical Success Stories: AAV-based gene therapies such as Zolgensma (onasemnogene abeparvovec-xioi) for spinal muscular atrophy and Hemgenix (etranacogene dezaparvovec) for hemophilia B have demonstrated durable efficacy in single-dose regimens, validating decades of vector research and manufacturing innovation​U.S. Food and Drug Administration​U.S. Food and Drug Administration.
• Broadening Indications: Dozens of AAV and lentiviral vectors are now in clinical trials for neurological, cardiac, ophthalmic, and metabolic disorders. AI-designed capsids with tailored tropism could broaden target tissues—such as muscle, brain, or lung—while minimizing off-target expression and immune reactions.
• Next Steps: As regulatory agencies become more familiar with AI-derived data, we anticipate streamlined IND reviews for vectors whose design and manufacturing analytics are powered by validated ML models. In parallel, multi-omics datasets (e.g., host-vector immunogenicity profiling) will feed next-generation AI platforms, enabling truly personalized vector selection based on patient HLA types and preexisting immunity.

Conclusion

The integration of AI into viral vector design and manufacturing is ushering in a new era for gene therapy. From in silico capsid fitness prediction and high-throughput sequence engineering, to real-time manufacturing control and digital twins, AI is delivering unprecedented speed, precision, and cost-efficiency. Strategic collaborations—such as Roche’s multi-hundred-million-dollar pact with Dyno—highlight industry confidence that AI will be the linchpin of next-generation vector platforms. As more gene therapies gain regulatory approvals and enter the market, the synergy between AI and viral vectors promises to enhance therapeutic safety, broaden treatable diseases, and make transformative medicines accessible to millions of patients worldwide. The biotech sector’s focus on AI-driven innovation in vector engineering is not merely a trend but a fundamental shift—a prerequisite for the scalable, affordable, and precise delivery of genetic medicines in the 2020s and beyond.