Oligonucleotide-based medicines (OBMs) have the potential to interrupt and reverse a huge range of genetic diseases, but there are too many possible OBMs to conduct safety and toxicity screening using old-fashioned trial-and-error approaches. Creyon takes a combined in vivo, in vitro, and in silico approach to solving that problem. The company knows how its library of survey compounds affects mammalian organs and whether they’re effective in patient cells; using this information, it has trained machine-learning algorithms to predict whether novel OBMs will have toxic or immune-stimulating effects.
News & Insights
Creating a software co-pilot for drug development and a new marketplace for investors and outsourced R&D and manufacturing
Using self-supervised machine learning to map the circuit diagram of tumor biology and develop new immunotherapies for cancer
Advancing precision medicine by combining the efficacy of antibodies with the binding ability of small molecules
Using synthetic biology to manufacture carbon-negative materials