| 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 | 760 | 2010 |
| 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 | 508 | 2013 |
| Ultrafast shape recognition to search compound databases for similar molecular shapes PJ Ballester, WG Richards Journal of computational chemistry 28 (10), 1711-1723, 2007 | 352 | 2007 |
| 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 | 291 | 2015 |
| Performance of machine-learning scoring functions in structure-based virtual screening M Wójcikowski, PJ Ballester, P Siedlecki Scientific Reports 7 (1), 46710, 2017 | 267 | 2017 |
| 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 | 247 | 2019 |
| 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 | 210 | 2015 |
| 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 | 192 | 2014 |
| 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 | 150 | 2017 |
| 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 | 116 | 2021 |
| 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 | 110 | 2019 |
| 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 | 110 | 2014 |
| istar: A web platform for large-scale protein-ligand docking H Li, KS Leung, PJ Ballester, MH Wong PLoS One 9 (1), e85678, 2014 | 110 | 2014 |
| Machine‐learning scoring functions for structure‐based drug lead optimization H Li, KH Sze, G Lu, P Ballester WIREs Computational Molecular Science, e1465, 2020 | 102 | 2020 |
| Ultrafast shape recognition for similarity search in molecular databases PJ Ballester, WG Richards Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2007 | 101 | 2007 |
| 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 | 99 | 2004 |
| 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 | 97 | 2010 |
| 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 | 95 | 2009 |
| 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 | 93 | 2005 |
| 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 | 82 | 2015 |
Hongjian LiChinese University of Hong KongEmail confirmado em cuhk.edu.hk
