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Pedro Ballester
Pedro Ballester
Royal Society Wolfson Fellow & Senior Lecturer at Imperial College London
Verified email at imperial.ac.uk - Homepage
Title
Cited by
Cited by
Year
A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
PJ Ballester, JBO Mitchell
Bioinformatics 26 (9), 1169-1175, 2010
7902010
Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties
MP Menden, F Iorio, M Garnett, U McDermott, CH Benes, PJ Ballester, ...
PLoS one 8 (4), e61318, 2013
5202013
Ultrafast shape recognition to search compound databases for similar molecular shapes
PJ Ballester, WG Richards
Journal of computational chemistry 28 (10), 1711-1723, 2007
3592007
Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening
QU Ain, A Aleksandrova, FD Roessler, PJ Ballester
Wiley Interdisciplinary Reviews: Computational Molecular Science 5 (6), 405-424, 2015
3092015
Performance of machine-learning scoring functions in structure-based virtual screening
M Wójcikowski, PJ Ballester, P Siedlecki
Scientific Reports 7 (1), 46710, 2017
2812017
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
MP Menden, D Wang, MJ Mason, B Szalai, KC Bulusu, Y Guan, T Yu, ...
Nature communications 10 (1), 1-17, 2019
2592019
Improving AutoDock Vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets
H Li, KS Leung, MH Wong, PJ Ballester
Molecular informatics 34 (2‐3), 115-126, 2015
2142015
Does a more precise chemical description of protein–ligand complexes lead to more accurate prediction of binding affinity?
PJ Ballester, A Schreyer, TL Blundell
Journal of chemical information and modeling 54 (3), 944-955, 2014
1962014
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open …
J Guinney, T Wang, TD Laajala, KK Winner, JC Bare, EC Neto, SA Khan, ...
The Lancet Oncology 18 (1), 132-142, 2017
1552017
Machine‐learning scoring functions for structure‐based virtual screening
H Li, KH Sze, G Lu, PJ Ballester
Wiley Interdisciplinary Reviews: Computational Molecular Science 11 (1), e1478, 2021
1272021
Predicting synergism of cancer drug combinations using NCI-ALMANAC data
P Sidorov, S Naulaerts, J Ariey-Bonnet, E Pasquier, PJ Ballester
Frontiers in chemistry 7, 509, 2019
1152019
Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study
H Li, KS Leung, MH Wong, PJ Ballester
BMC bioinformatics 15 (1), 1-12, 2014
1122014
istar: A web platform for large-scale protein-ligand docking
H Li, KS Leung, PJ Ballester, MH Wong
PLoS One 9 (1), e85678, 2014
1122014
Machine‐learning scoring functions for structure‐based drug lead optimization
H Li, KH Sze, G Lu, P Ballester
WIREs Computational Molecular Science, e1465, 2020
1112020
Ultrafast shape recognition for similarity search in molecular databases
PJ Ballester, WG Richards
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2007
1022007
Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferases
PJ Ballester, I Westwood, N Laurieri, E Sim, WG Richards
Journal of The Royal Society Interface 7 (43), 335-342, 2010
992010
An effective real-parameter genetic algorithm with parent centric normal crossover for multimodal optimisation
PJ Ballester, JN Carter
Genetic and evolutionary computation conference, 901-913, 2004
992004
Ultrafast shape recognition: evaluating a new ligand-based virtual screening technology
PJ Ballester, PW Finn, WG Richards
Journal of Molecular Graphics and Modelling 27 (7), 836-845, 2009
952009
Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX
PJ Ballester, J Stephenson, JN Carter, K Gallagher
2005 IEEE Congress on Evolutionary Computation 1, 498-505, 2005
942005
Low-quality structural and interaction data improves binding affinity prediction via random forest
H Li, KS Leung, MH Wong, PJ Ballester
Molecules 20 (6), 10947-10962, 2015
892015
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