A distribution-aware and functionally relevant novel framework for generation and discovery of bioactive peptides
Recent advances in artificial intelligence have accelerated the discovery of bioactive peptides by enabling computational exploration of the vast peptide sequence space. However, existing peptide generation approaches generally rely on either distribution-learning models, which generate biologically realistic sequences but do not consistently optimize functional activity, or optimization based…
We haven't written up this one. bioRxiv has the full story — the link below goes straight to it.
