
Celeris Therapeutics uses AI and automated laboratories to design small-molecule proximity-inducing compounds that selectively degrade disease proteins. Its Celeris One platform applies structure-based geometric deep learning, generative design, and predictive models to prioritize E3 ligases, design linkers, and predict active degrader chemistries. An automated closed-loop wet lab generates experimental data to iteratively optimize candidates. Celeris collaborates with pharmaceutical and biotech partners and advances an internal CNS and oncology degrader pipeline.

Celeris Therapeutics uses AI and automated laboratories to design small-molecule proximity-inducing compounds that selectively degrade disease proteins. Its Celeris One platform applies structure-based geometric deep learning, generative design, and predictive models to prioritize E3 ligases, design linkers, and predict active degrader chemistries. An automated closed-loop wet lab generates experimental data to iteratively optimize candidates. Celeris collaborates with pharmaceutical and biotech partners and advances an internal CNS and oncology degrader pipeline.