Quentin Trolliet

This paper compares chemical descriptors and deep learning embeddings for scoring de novo peptide designs, finding classical descriptors are effective and efficient for developability prediction.

The paper introduces a hybrid method using a protein language model and Monte Carlo sampling to generate enzyme mutants that preserve structural features while varying overall sequence, providing sequences for experimental testing.