Shaun M Kandathil
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A guide to machine learning for biologists
JG Greener, SM Kandathil, L Moffat, DT Jones
Nature reviews Molecular cell biology 23 (1), 40-55, 2022
A review of the chemistry and pharmacology of the date fruits (Phoenix dactylifera L.)
MS Baliga, BRV Baliga, SM Kandathil, HP Bhat, PK Vayalil
Food research international 44 (7), 1812-1822, 2011
High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features
DT Jones, SM Kandathil
Bioinformatics 34 (19), 3308-3315, 2018
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints
JG Greener, SM Kandathil, DT Jones
Nature communications 10 (1), 3977, 2019
Prediction of interresidue contacts with DeepMetaPSICOV in CASP13
SM Kandathil, JG Greener, DT Jones
Proteins: Structure, Function, and Bioinformatics 87 (12), 1092-1099, 2019
Recent developments in deep learning applied to protein structure prediction
SM Kandathil, JG Greener, DT Jones
Proteins: Structure, Function, and Bioinformatics 87 (12), 1179-1189, 2019
Accuracy and tractability of a kriging model of intramolecular polarizable multipolar electrostatics and its application to histidine
SM Kandathil, TL Fletcher, Y Yuan, J Knowles, PLA Popelier
Journal of computational chemistry 34 (21), 1850–1861, 2013
Generating, maintaining, and exploiting diversity in a memetic algorithm for protein structure prediction
M Garza-Fabre, SM Kandathil, J Handl, J Knowles, SC Lovell
Evolutionary computation 24 (4), 577-607, 2016
Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins
SM Kandathil, JG Greener, AM Lau, DT Jones
Proceedings of the National Academy of Sciences 119 (4), e2113348119, 2022
The prediction of atomic kinetic energies from coordinates of surrounding atoms using kriging machine learning
TL Fletcher, SM Kandathil, PLA Popelier
Theoretical Chemistry Accounts 133 (7), 1499, 2014
Design in the DARK: learning deep generative models for De Novo protein design
L Moffat, SM Kandathil, DT Jones
bioRxiv, 2022.01. 27.478087, 2022
Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction
SM Kandathil, J Handl, SC Lovell
Proteins: Structure, Function, and Bioinformatics 84, 411–426, 2016
Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging
TJ Hughes, SM Kandathil, PLA Popelier
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 136, 32-41, 2015
Improved fragment-based protein structure prediction by redesign of search heuristics
SM Kandathil, M Garza-Fabre, J Handl, SC Lovell
Scientific Reports 8 (1), 13694, 2018
Deep learning-based prediction of protein structure using learned representations of multiple sequence alignments
SM Kandathil, JG Greener, AM Lau, DT Jones
Biorxiv, 2020.11. 27.401232, 2020
Proton tunnelling and promoting vibrations during the oxidation of ascorbate by ferricyanide?
SM Kandathil, MD Driscoll, RV Dunn, NS Scrutton, S Hay
Physical Chemistry Chemical Physics 16 (6), 2256-2259, 2014
Adaptive HIV-1 evolutionary trajectories are constrained by protein stability
AS Olabode, SM Kandathil, SC Lovell, DL Robertson
Virus Evolution 3 (2), vex019, 2017
Machine learning methods for predicting protein structure from single sequences
SM Kandathil, AM Lau, DT Jones
Current Opinion in Structural Biology 81, 102627, 2023
Merizo: a rapid and accurate protein domain segmentation method using invariant point attention
AM Lau, SM Kandathil, DT Jones
Nature Communications 14 (1), 8445, 2023
Uncommon mutational profiles of metastatic colorectal cancer detected during routine genotyping using next generation sequencing
C Franczak, SM Kandathil, P Gilson, M Husson, M Rouyer, J Demange, ...
Scientific Reports 9 (1), 7083, 2019
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