Exhibiting at the AI & Machine Learning Convention


GTN has developed an end-to-end approach to designing TSA drug candidates, enabling the development of drugs that are an order of magnitude more potent, less toxic and more robust to genetic mutations than would otherwise possible.

This results in better candidates earlier in the drug discovery process, and de-risks in vivo and clinical stages. Moreover, in contrast to competing methods, the transition state approach does not require access to large amounts of experimental data, enabling a significant shortening of timelines in the early stages of drug discovery, as well as access towards valuable first-in-class targets.

The design of TSAs is based on robust thermodynamic and kinetic principles from the on-set of drug design which is opposite the prevailing practices drug discovery where drug design is driven by potency measures.