Alex Abrudan

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.

This paper introduces a model-agnostic framework to condition Boltzmann generators for sampling cyclic peptide conformations without retraining, using chemically informed loss functions and geometric constraints.