Pedro J. Ballester
Orcid: 0000-0002-4078-743X
According to our database1,
Pedro J. Ballester
authored at least 28 papers
between 2003 and 2024.
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Bibliography
2024
Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors.
J. Cheminformatics, December, 2024
2023
Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons.
J. Chem. Inf. Model., March, 2023
2021
A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling.
Briefings Bioinform., 2021
The impact of compound library size on the performance of scoring functions for structure-based virtual screening.
Briefings Bioinform., 2021
2020
Stochastic-based Neural Network hardware acceleration for an efficient ligand-based virtual screening.
CoRR, 2020
2019
Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data.
Bioinform., 2019
2018
IEEE Trans. Neural Networks Learn. Syst., 2018
2016
USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniques.
Nucleic Acids Res., 2016
Correcting the impact of docking pose generation error on binding affinity prediction.
BMC Bioinform., 2016
2015
Proceedings of the Bioinformatics and Biomedical Engineering, 2015
2014
Does a More Precise Chemical Description of Protein-Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?
J. Chem. Inf. Model., 2014
Prospective virtual screening for novel p53-MDM2 inhibitors using ultrafast shape recognition.
J. Comput. Aided Mol. Des., 2014
Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study.
BMC Bioinform., 2014
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2014
The Importance of the Regression Model in the Structure-Based Prediction of Protein-Ligand Binding.
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2014
2012
Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties
CoRR, 2012
Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression.
Proceedings of the Pattern Recognition in Bioinformatics, 2012
2011
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., 2011
2010
A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking.
Bioinform., 2010
2007
Ultrafast shape recognition to search compound databases for similar molecular shapes.
J. Comput. Chem., 2007
Model calibration of a real petroleum reservoir using a parallel real-coded genetic algorithm.
Proceedings of the IEEE Congress on Evolutionary Computation, 2007
2006
Our calibrated model has poor predictive value: An example from the petroleum industry.
Reliab. Eng. Syst. Saf., 2006
A Multiparent Version of the Parent-Centric Normal Crossover for Multimodal Optimization.
Proceedings of the IEEE International Conference on Evolutionary Computation, 2006
2005
Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX.
Proceedings of the IEEE Congress on Evolutionary Computation, 2005
2004
Proceedings of the Experimental and Efficient Algorithms, Third International Workshop, 2004
Tackling an Inverse Problem from the Petroleum Industry with a Genetic Algorithm for Sampling.
Proceedings of the Genetic and Evolutionary Computation, 2004
An Effective Real-Parameter Genetic Algorithm with Parent Centric Normal Crossover for Multimodal Optimisation.
Proceedings of the Genetic and Evolutionary Computation, 2004
2003
Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization.
Proceedings of the Genetic and Evolutionary Computation, 2003