Unsupervised machine learning leads to an abiotic picomolar peptide ligand

Here, we combined unsupervised machine learning (ML), non-natural amino acids, and affinity-selection mass-spectrometry (AS-MS) for the discovery of ultra-high affinity peptidomimetics that bind to a protein target. Peptides and peptidomimetics were discovered using AS-MS, encoded using diverse representations, and decomposed into two-dimensional “maps” of the chemical space by dimensionality reduction. These maps showed well-defined clusters of target-specific binders distinct from the remaining chemical space that included nonspecific and nonbinding peptides. Experimental testing of abiotic peptidomimetics confirmed the discovery of low nanomolar to picomolar binders and the accurate mapping of high-affinity binders across the co-learned sequence space. With ML and AS-MS, we anticipate this cartographic approach will accelerate the definition of chemical design spaces for the prediction and generation of functional peptidomimetics.