Ardigen Biologics discovery platform

Ardigen’s Biologics Discovery Platform is an AI-powered system that integrates computational modeling and machine learning with laboratory validation to accelerate the design, optimization, and development of therapeutic biologics. It enables the rapid identification and refinement of protein-based drug candidates such as antibodies, peptides, and engineered binding scaffolds through an iterative, data-driven approach.

Ardigen’s platform operates in a closed loop, where AI-generated candidates are prioritized based on predicted performance and experimentally tested in the lab. The resulting assay data are then fed back into the models to refine predictions and inform the next design cycle. This continuous feedback loop ensures that computational outputs are grounded in biological reality and that model accuracy improves over time.

The platform supports a broad range of amino-acid-based molecules, like monoclonal antibodies, nanobodies, T-cell receptors (TCRs) peptides, fusion proteins, cytokines, peptides, and novel scaffolds. Its modular architecture allows for customization of workflows depending on the target class, therapeutic modality, and stage of development.

The AI-lab loop accelerates development by combining fast, hypothesis-generating AI models with real-world biological testing in an iterative workflow. This reduces the number of low-value experimental cycles, shortens lead optimization timelines, and improves the likelihood of generating therapeutics with the desired biophysical and biochemical properties.

Yes. Ardigen provides comprehensive antibody optimization services, including humanization, affinity enhancement, developability profiling, and liability removal. These services are powered by AI models trained on curated antibody datasets and supported by structural modeling and in vitro assay data.

In silico modeling enables the exploration of vast sequence and structural spaces without the need for costly and time-consuming lab work. It allows for virtual screening of candidates, prediction of key biophysical properties, and rapid iteration cycles. By identifying the most promising molecules computationally, in silico modeling reduces experimental load and shortens time to lead selection.