Jones, David T.

Improved protein structure prediction using potentials from deep learning

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 3,378

A guide to machine learning for biologists

Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology

Machine Learning in BioinformaticsGene expression and cancer classification

Cited by 2,127

A large-scale evaluation of computational protein function prediction

Radivojac, P., Clark, W. T., Oron, T. R., Schnoes, A. M., Wittkop, T., Sokolov, A., Graim, K., Funk, C., Verspoor, K., Ben-Hur, A., et al. (2013) · Nature Methods

Bioinformatics and Genomic NetworksMachine Learning in Bioinformatics

Cited by 1,028

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Zhou, N., Jiang, Y., Bergquist, T. R., Lee, A. J., Kacsoh, B. Z., Crocker, A. W., Lewis, K. A., Georghiou, G., Nguyen, H. N., Hamid, M. N., et al. (2019) · Genome Biology

Bioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

Cited by 452

Protein structure prediction using multiple deep neural networks in the 13th critical assessment of protein structure prediction (CASP13)

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2019) · Proteins: Structure, Function, and Bioinformatics

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 347

Critical assessment of protein intrinsic disorder prediction

Necci, M., Piovesan, D., Predictors, C., Hoque, M. T., Walsh, I., Iqbal, S., Vendruscolo, M., Sormanni, P., Wang, C., Raimondi, D., et al. (2021) · Nature Methods

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 343

Assessing the Impact of Secondary Structure and Solvent Accessibility on Protein Evolution

Goldman, N., Thorne, J. L., & Jones, D. T. (1998) · Genetics

Evolution and Genetic DynamicsProtein Structure and Dynamics

Cited by 281

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

Greener, J. G., Kandathil, S. M., & Jones, D. T. (2019) · Nature Communications

Protein Structure and DynamicsEnzyme Structure and Function

Cited by 178

Exploring structural diversity across the protein universe with The Encyclopedia of Domains

Lau, A. M., Bordin, N., Kandathil, S. M., Sillitoe, I., Waman, V. P., Wells, J., Orengo, C. A., & Jones, D. T. (2024) · Science

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 140

Exploring structural diversity across the protein universe with The Encyclopedia of Domains

Lau, A. M., Bordin, N., Kandathil, S. M., Sillitoe, I., Waman, V. P., Wells, J., Orengo, C. A., & Jones, D. T. (2024) · Science

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 140

FFPred 3: feature-based function prediction for all Gene Ontology domains

Cozzetto, D., Minneci, F., Currant, H., & Jones, D. T. (2016) · Scientific Reports

Machine Learning in BioinformaticsBioinformatics and Genomic Networks

Cited by 127

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Lise, S., Archambeau, C., Pontil, M., & Jones, D. T. (2009) · BMC Bioinformatics

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 107

Merizo: a rapid and accurate protein domain segmentation method using invariant point attention

Lau, A. M., Kandathil, S. M., & Jones, D. T. (2023) · Nature Communications

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 48

Merizo: a rapid and accurate domain segmentation method using invariant point attention

Lau, A. M., Kandathil, S. M., & Jones, D. T. (2023) · Nature Communications

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 47

Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design

Moffat, L., Greener, J. G., & Jones, D. T. (2021) · bioRxiv (Cold Spring Harbor Laboratory)

Protein Structure and DynamicsEnzyme Structure and Function

Cited by 33

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Greener, J. G., & Jones, D. T. (2021) · PLOS ONE

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 29

Design in the DARK: Learning Deep Generative Models for De Novo Protein Design

Moffat, L., Kandathil, S. M., & Jones, D. T. (2022) · bioRxiv (Cold Spring Harbor Laboratory)

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 25

Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins

Kandathil, S. M., Greener, J. G., Lau, A. M., & Jones, D. T. (2020) · bioRxiv (Cold Spring Harbor Laboratory)

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 8