Peptide design stops picking among twenty letters: AtomWeaver packs side-chain atoms first and names the residue afterwards, so a new building block costs minutes
The inverse-folding models used to design peptides emit a probability over exactly twenty natural residues at each position, and adding an unnatural one means supplying its structural data and retraining the whole model. AtomWeaver instead grows side chains as atoms in three dimensions under a fixed backbone and a target protein, then matches each finished atom cloud against a reference library of residue templates to read off identity. On PeptideArena, 353 backbones against twelve targets, it stayed competitive with twenty-residue methods and held the same performance regime with a 300-residue vocabulary.