Uploaded July 2025 | Updated September 2026, 2 weeks ago
Identifying a potential drug candidate for a disease can take many years and tens of millions of dollars. AI has the potential to reduce overall time and cost to develop a drug. However, applications of AI to drug discovery have been hindered by the limited availability of datasets that are large in scale, yet sufficiently focused on specific biological systems of interest.
In this talk, we will describe how ArsenalBio’s automation lab and discovery platform enable large scale data generation for training and validating AI models of T cells, a core cell type of the immune system with key roles in cancer, autoimmunity, and infection. We will introduce the gx1 model, a foundation model of T cell biology. We will demonstrate how the gx1 model can be used for two applications: 1) virtual screens at a scale not possible through wet-lab experiments; 2) patient stratification to obtain biological insights inaccessible through other methods.
Identifying a potential drug candidate for a disease can take many years and tens of millions of dollars. AI has the potential to reduce overall time and cost to develop a drug. However, applications of AI to drug discovery have been hindered by the limited availability of datasets that are large in scale, yet sufficiently focused on specific biological systems of interest.
In this talk, we will describe how ArsenalBio’s automation lab and discovery platform enable large scale data generation for training and validating AI models of T cells, a core cell type of the immune system with key roles in cancer, autoimmunity, and infection. We will introduce the gx1 model, a foundation model of T cell biology. We will demonstrate how the gx1 model can be used for two applications: 1) virtual screens at a scale not possible through wet-lab experiments; 2) patient stratification to obtain biological insights inaccessible through other methods.
