G. R. Lee

This paper introduces LigandMPNN, a deep learning method for protein sequence design that models non-protein atomic contexts, achieving better performance than existing methods in recovering interacting residues.

The paper designs nanomolar binders for diverse small molecules using a pseudocycle-based shape complementarity method, enabling direct conversion into sensing systems.

This paper designs a protein family that binds and senses small molecules using deep learning and physics-based methods, achieving nanomolar sensitivity for cortisol.

This paper introduces RFAA and RFdiffusionAA, models that can predict and design complex biomolecular assemblies including proteins, nucleic acids, small molecules, and covalent modifications.

The paper generates high-quality protein conformations using a variational autoencoder, achieving better sampling accuracy than MD simulations or AlphaFold2 for K-Ras.

This paper designs high-affinity protein binders for bioactive helical peptides using deep learning and generative models, achieving picomolar affinity without experimental optimization.

This study evaluates how the limit of detection (LoD) of SARS-CoV-2 assays affects false negative rates using 27,500 test results, finding a 13% increase in false negatives per 10-fold LoD increase.