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ORCIDPedro J. Ballester
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2020 – today
- 2024
[j15]Klaudia Caba, Viet-Khoa Tran-Nguyen
, Taufiq Rahman, Pedro J. Ballester:
Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors. J. Cheminformatics 16(1): 40 (2024)- [c13]Qianrong Guo, Saiveth Hernández-Hernández, Pedro J. Ballester:
Scaffold Splits Overestimate Virtual Screening Performance. ICANN (10) 2024: 58-72 - [i3]Qianrong Guo, Saiveth Hernández-Hernández, Pedro J. Ballester:
Scaffold Splits Overestimate Virtual Screening Performance. CoRR abs/2406.00873 (2024) - 2023
- [j14]Viet-Khoa Tran-Nguyen, Pedro J. Ballester:
Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons. J. Chem. Inf. Model. 63(5): 1401-1405 (2023) - 2022
- [c12]Saiveth Hernández-Hernández, Sachin Vishwakarma, Pedro J. Ballester:
Conformal prediction of small-molecule drug resistance in cancer cell lines. COPA 2022: 92-108 - 2021
- [j13]Louison Fresnais, Pedro J. Ballester:
The impact of compound library size on the performance of scoring functions for structure-based virtual screening. Briefings Bioinform. 22(3) (2021) - [j12]Chayanit Piyawajanusorn, Linh C. Nguyen, Ghita Ghislat, Pedro J. Ballester:
A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling. Briefings Bioinform. 22(6) (2021) - 2020
- [i2]Christiam F. Frasser, Carola de Benito, Vincent Canals, Miquel Roca, Pedro J. Ballester, Josep L. Rosselló:
Stochastic-based Neural Network hardware acceleration for an efficient ligand-based virtual screening. CoRR abs/2006.02505 (2020)
2010 – 2019
- 2019
- [j11]Hongjian Li, Jiangjun Peng, Pavel Sidorov, Yee Leung, Kwong-Sak Leung, Man Hon Wong, Gang Lu, Pedro J. Ballester:
Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data. Bioinform. 35(20): 3989-3995 (2019) - 2018
- [j10]Antoni Morro, Vicent Canals, Antoni Oliver, Miquel L. Alomar, Fabio Galán-Prado, Pedro J. Ballester, José Luis Rosselló:
A Stochastic Spiking Neural Network for Virtual Screening. IEEE Trans. Neural Networks Learn. Syst. 29(4): 1371-1375 (2018) - 2016
- [j9]Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester:
Correcting the impact of docking pose generation error on binding affinity prediction. BMC Bioinform. 17(S-11): 308 (2016) - [j8]Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester:
USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniques. Nucleic Acids Res. 44(Webserver-Issue): W436-W441 (2016) - 2015
- [c11]Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester:
The Use of Random Forest to Predict Binding Affinity in Docking. IWBBIO (2) 2015: 238-247 - 2014
- [j7]Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester:
Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study. BMC Bioinform. 15: 291 (2014) - [j6]Sachin P. Patil, Pedro J. Ballester, Cassidy R. Kerezsi:
Prospective virtual screening for novel p53-MDM2 inhibitors using ultrafast shape recognition. J. Comput. Aided Mol. Des. 28(2): 89-97 (2014) - [j5]Pedro J. Ballester, Adrian Schreyer, Tom L. Blundell:
Does a More Precise Chemical Description of Protein-Ligand Complexes Lead to More Accurate Prediction of Binding Affinity? J. Chem. Inf. Model. 54(3): 944-955 (2014) - [c10]Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester:
The Importance of the Regression Model in the Structure-Based Prediction of Protein-Ligand Binding. CIBB 2014: 219-230 - [c9]Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester:
The Impact of Docking Pose Generation Error on the Prediction of Binding Affinity. CIBB 2014: 231-241 - 2012
- [c8]Pedro J. Ballester:
Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression. PRIB 2012: 14-25 - [i1]Michael P. Menden, Francesco Iorio, Mathew Garnett, Ultan McDermott, Cyril Benes, Pedro J. Ballester, Julio Saez-Rodriguez:
Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. CoRR abs/1212.0504 (2012) - 2011
- [j4]Pedro J. Ballester, John B. O. Mitchell:
Comments on "Leave-Cluster-Out Cross-Validation Is Appropriate for Scoring Functions Derived from Diverse Protein Data Sets": Significance for the Validation of Scoring Functions. J. Chem. Inf. Model. 51(8): 1739-1741 (2011) - 2010
- [j3]Pedro J. Ballester, John B. O. Mitchell:
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinform. 26(9): 1169-1175 (2010)
2000 – 2009
- 2007
- [j2]Pedro J. Ballester, W. Graham Richards:
Ultrafast shape recognition to search compound databases for similar molecular shapes. J. Comput. Chem. 28(10): 1711-1723 (2007) - [c7]Pedro J. Ballester, Jonathan N. Carter:
Model calibration of a real petroleum reservoir using a parallel real-coded genetic algorithm. IEEE Congress on Evolutionary Computation 2007: 4313-4320 - 2006
- [j1]Jonathan N. Carter, Pedro J. Ballester, Zohreh Tavassoli, Peter R. King:
Our calibrated model has poor predictive value: An example from the petroleum industry. Reliab. Eng. Syst. Saf. 91(10-11): 1373-1381 (2006) - [c6]Pedro J. Ballester, W. Graham Richards:
A Multiparent Version of the Parent-Centric Normal Crossover for Multimodal Optimization. IEEE Congress on Evolutionary Computation 2006: 2999-3006 - 2005
- [c5]Pedro J. Ballester, John Stephenson, Jonathan N. Carter, Kerry Gallagher:
Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX. Congress on Evolutionary Computation 2005: 498-505 - 2004
- [c4]Pedro J. Ballester, Jonathan N. Carter:
An Effective Real-Parameter Genetic Algorithm with Parent Centric Normal Crossover for Multimodal Optimisation. GECCO (1) 2004: 901-913 - [c3]Pedro J. Ballester, Jonathan N. Carter:
Tackling an Inverse Problem from the Petroleum Industry with a Genetic Algorithm for Sampling. GECCO (2) 2004: 1299-1300 - [c2]Pedro J. Ballester, Jonathan N. Carter:
An Algorithm to Identify Clusters of Solutions in Multimodal Optimisation. WEA 2004: 42-56 - 2003
- [c1]Pedro J. Ballester, Jonathan N. Carter:
Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization. GECCO 2003: 706-717
Coauthor Index
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