Jonathan Francis-Landau

The paper develops a high-throughput peptide-selection protocol to generate a large dataset of solid-binding peptides, emphasizing data quality and enabling future machine learning applications.

This paper introduces a generalized similarity metric, the Total Similarity Score, to predict peptide binding affinity by benchmarking it on peptide-protein datasets.

The paper introduces VSEPRnet, a method to encode biomolecule structures for functionality prediction, demonstrating improved accuracy in predicting peptide-MHC binding affinities.