Lonza and Onava have entered into a research collaboration aimed at strengthening the experimental validation of AI-designed biologics, using Lonza’s GS Gene Expression System platform and GS Ori-Go vectors to generate biological data for Onava’s discovery platform. The agreement, structured as a Research Evaluation Agreement (REA), gives Onava access to Lonza’s gene expression technology through a flexible evaluation framework. The collaboration is intended to establish a closer connection between computationally designed molecules and laboratory-generated data, allowing experimental findings to inform subsequent design iterations.

For AI-driven biologics discovery, this feedback loop could help researchers evaluate candidates earlier, refine predictive models and consider development-related attributes before advancing molecules further through the pipeline. According to the companies, data generated through the GS Gene Expression System will be fed back into Onava’s computational models. The approach is designed to support iterative candidate optimization, with experimental results informing subsequent rounds of molecular design.

“AI-enabled drug discovery depends on the ability to connect computational design with robust experimental validation,” says Peter Droc, Vice President and Head of Licensing at Lonza. He noted that access to the GS Gene Expression System platform and GS Ori-Go vectors through the REA framework can help researchers generate biological data earlier and make more informed development decisions.

The arrangement is not expected to be limited to a single discovery program. Onava plans to apply the technology across multiple external collaborations and selected internal programs, according to the announcement.

The partnership also creates a potential pathway for continued engagement with Lonza as candidates progress. Its capabilities span cell line development, process development and manufacturing, offering support for programs moving beyond early discovery and into subsequent development stages.

For Onava, gene expression data represents an important input into the design process rather than simply a downstream consideration. The company is developing an AI-native platform for biologics discovery, with the goal of incorporating experimental evidence into the models used to design therapeutic molecules.

“For AI-native biologics discovery, expression is not simply a downstream manufacturing consideration; it is a critical source of training data,” says Sharrol Bachas, Chief Scientific Officer at Onava. Bachas said incorporating Lonza’s expression system and vectors into the platform is expected to help the company generate consistent experimental data across larger sets of AI-designed molecules and use those results to improve its models. The company’s approach seeks to account for both biological function and the characteristics that influence whether a molecule can be developed and manufactured successfully. By integrating experimental expression data into computational design cycles, the collaboration addresses a practical challenge in AI-enabled drug discovery: translating predictions into candidates with experimentally supported characteristics and a clearer path toward development.

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