The future of drug design is in AI. RareFoldGPCR: Agonist Design Beyond Natural Amino Acids. Paper: www.biorxiv.org/content/10.1... Code: github.com/patrickbryan...

Update: RareFold 🧬 Our AI framework for protein design with 29 noncanonical AAs now shows designed binders (linear + cyclic) are non-immunogenic in patient-derived assays — paving the way for safe next-gen peptide therapeutics. 👉https://www.biorxiv.org/content/10.1101/2025.05.19.654846v2

Our study where we develop EvoBind2: Design of linear and cyclic peptide binders from protein sequence information is now published! www.nature.com/articles/s42...

Cool! Congrats @proteinator.bsky.social 🎉

Now published: our study on human-pathogen protein-protein interactions! We identify 30 interactions with an expected TM-score ≥0.9, tripling the structural coverage in these networks. One novel interaction was validated with mass spectrometry. journals.plos.org/ploscompbiol...

Our latest work is out: we designed dual GLP1R/GCGR agonists—cyclic peptides that activate both metabolic receptors, entirely from sequence alone. This has never been done before. www.biorxiv.org/content/10.1...

You can also design in Colab now: colab.research.google.com/github/patri...

Happy to release our breakthrough AI-model: RareFold, which predicts and designs proteins with noncanonical AAs. With EvoBindRare, we designed linear & cyclic peptide binders with high affinity & novel binding modes, wet lab validated. 📄 biorxiv.org/content/10.1... 💻 github.com/patrickbryan...