Author
University of TorontoORCID 0000-0003-2112-1402Unclaimed ORCID profile · follow to get notified of new preprints
3 papers
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Matthew C. McFee, Philip M. Kim
SLiMNet detects functional similarity in SLiM pairs using deep learning and protein LLM embeddings, identifying new motifs and providing functional annotations.
Matthew C. McFee, Jisun Kim, Philip M. Kim
This paper introduces EuDockScore, a scoring model for protein-protein interfaces using euclidean graph neural networks, and provides variants for antibody-antigen docking and re-ranking AlphaFold-Multimer outputs.
Matthew C. McFee, Philip M. Kim
GDockScore is a graph-based deep learning model for protein-protein docking scoring, achieving state-of-the-art performance on challenging datasets.