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Nature Communications|September 6, 2019
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraintsJoe G Greener, Shaun M Kandathil, David T JonesProteins|July 13, 2019
Prediction of interresidue contacts with DeepMetaPSICOV in CASP13Shaun M Kandathil, Joe G Greener, David T JonesProteins|October 8, 2019
Recent developments in deep learning applied to protein structure predictionShaun M Kandathil, Joe G Greener, David T JonesNature Reviews. Molecular Cell Biology|September 14, 2021
A guide to machine learning for biologistsJoe G Greener, Shaun M Kandathil, Lewis Moffat, et al.Proceedings of the National Academy of Sciences of the United States of America|January 25, 2022
Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteinsShaun M Kandathil, Joe G Greener, Andy M Lau, et al.Bioinformatics (Oxford, England)|May 3, 2018
High precision in protein contact prediction using fully convolutional neural networks and minimal sequence featuresDavid T Jones, Shaun M KandathilPlos One|September 2, 2021
Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteinsJoe G Greener, David T JonesScientific Reports|November 3, 2018
Design of metalloproteins and novel protein folds using variational autoencodersJoe G Greener, Lewis Moffat, David T JonesCurrent Opinion in Structural Biology|June 15, 2023
Machine learning methods for predicting protein structure from single sequencesShaun M Kandathil, Andy M Lau, David T JonesNature Communications|December 19, 2023
Merizo: a rapid and accurate protein domain segmentation method using invariant point attentionAndy M Lau, Shaun M Kandathil, David T JonesPageof 38