S. Kandathil

发表

University of Birmingham Generating, maintaining and exploiting diversity in a memetic algorithm for protein structure prediction

Shaun M. Kandathil, S. Lovell, J. Handl, 2016 .

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

Shaun M. Kandathil, Andy M. Lau, David T. Jones, 2022, Proceedings of the National Academy of Sciences.

Generating, Maintaining, and Exploiting Diversity in a Memetic Algorithm for Protein Structure Prediction

Joshua D. Knowles, Mario Garza-Fabre, Julia Handl, 2016, Evolutionary Computation.

A review of the chemistry and pharmacology of the date fruits (Phoenix dactylifera L.)

Shaun M. Kandathil, M. Baliga, H. Bhat, 2011 .

The prediction of atomic kinetic energies from coordinates of surrounding atoms using kriging machine learning

P. Popelier, T. Fletcher, S. Kandathil, 2014, Theoretical Chemistry Accounts.

A guide to machine learning for biologists

Shaun M. Kandathil, Joe G. Greener, Lewis Moffat, 2021, Nature Reviews Molecular Cell Biology.

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

David T. Jones, Shaun M. Kandathil, David T. Jones, 2018, Nature Communications.

Improved fragment-based protein structure prediction by redesign of search heuristics

S. Lovell, Julia Handl, S. Kandathil, 2018, Scientific Reports.

Sequence Covariation Analysis in Biological Polymers

Shaun M. Kandathil, David T. Jones, W. Taylor, 2019, Handbook of Statistical Genomics.

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

Shaun M. Kandathil, Joe G Greener, Andy M. Lau, 2021 .

Near-complete protein structural modelling of the minimal genome.

Shaun M. Kandathil, David T. Jones, Joe G. Greener, 2020, 2007.06623.

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

Shaun M. Kandathil, Lewis Moffat, David T. Jones, 2022, bioRxiv.

Adaptive HIV-1 evolutionary trajectories are constrained by protein stability

Shaun M. Kandathil, S. Lovell, D. Robertson, 2017, Virus evolution.

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

Shaun M. Kandathil, David T. Jones, Joe G. Greener, 2018, Nature Communications.

Prediction of interresidue contacts with DeepMetaPSICOV in CASP13

David T Jones, Joe G Greener, David T. Jones, 2019, Proteins.

High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features

David T. Jones, Shaun M. Kandathil, David T. Jones, 2018, Bioinform..

Uncommon mutational profiles of metastatic colorectal cancer detected during routine genotyping using next generation sequencing

A. Leroux, J. Merlin, P. Gilson, 2019, Scientific Reports.

Reliable Generation of Native-Like Decoys Limits Predictive Ability in Fragment-Based Protein Structure Prediction

Mario Garza-Fabre, Julia Handl, Shaun M. Kandathil, 2019, Biomolecules.

A guide to machine learning for biologists

Shaun M. Kandathil, Joe G. Greener, Lewis Moffat, 2021, Nature Reviews Molecular Cell Biology.

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

Andy M. Lau, David T. Jones, S. Kandathil, 2023, bioRxiv.

Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction

Julia Handl, Simon C. Lovell, Shaun M. Kandathil, 2016, Proteins.

Proton tunnelling and promoting vibrations during the oxidation of ascorbate by ferricyanide?

Shaun M. Kandathil, N. Scrutton, R. Dunn, 2014, Physical chemistry chemical physics : PCCP.

Recent developments in deep learning applied to protein structure prediction

David T Jones, Shaun M. Kandathil, Joe G Greener, 2019, Proteins.

Using Machine Learning to Explore the Relevance of Local and Global Features During Conformational Search in Rosetta

Joshua D. Knowles, Mario Garza-Fabre, Julia Handl, 2015, GECCO.

Accuracy and tractability of a kriging model of intramolecular polarizable multipolar electrostatics and its application to histidine

Joshua D. Knowles, Yongna Yuan, Paul L. A. Popelier, 2013, J. Comput. Chem..

Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging.

Paul L A Popelier, Shaun M Kandathil, P. Popelier, 2015, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

Prediction of interresidue contacts with DeepMetaPSICOV in CASP13

David T. Jones, Shaun M. Kandathil, Joe G Greener, 2019, bioRxiv.

Machine learning methods for predicting protein structure from single sequences.

Shaun M. Kandathil, Andy M. Lau, David T. Jones, 2023, Current opinion in structural biology.