6 citations · 8 across the 5 of their papers we have counts for
5 papers
T-cell receptor specificity landscape revealed through de novo peptide design
Gian Marco Visani, Michael N. Pun, Anastasia A. Minervina +3
T-cells play a key role in adaptive immunity by mounting specific responses against diverse pathogens. An effective binding between T-cell receptors (TCRs) and pathogen-derived pep…
HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction
Gian Marco Visani, William Galvin, Zac Jones +5
Predicting the stability and fitness effects of amino acid mutations in proteins is a cornerstone of biological discovery and engineering. Various experimental techniques have been…
H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing
Gian Marco Visani, William Galvin, Michael Neal Pun +1
Accurately modeling protein 3D structure is essential for the design of functional proteins. An important sub-task of structure modeling is protein side-chain packing: predicting t…
Learning the shape of protein micro-environments with a holographic convolutional neural network
Michael N. Pun, Andrew Ivanov, Quinn Bellamy +5
Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure f…
Holographic-(V)AE: an end-to-end SO(3)-Equivariant (Variational) Autoencoder in Fourier Space
Gian Marco Visani, Michael N. Pun, Arman Angaji +1
Group-equivariant neural networks have emerged as a data-efficient approach to solve classification and regression tasks, while respecting the relevant symmetries of the data. Howe…