Pedro J. Ballester

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 10

Top Topics

Chemical Structures

Multiple Linear Regression

Virtual Screening

Top Venues

Briefings Bioinform.

Publications

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) (2024)
Qianrong Guo, Saiveth Hernández-Hernández, Pedro J. Ballester
Scaffold Splits Overestimate Virtual Screening Performance. CoRR (2024)
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) (2023)
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)
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)
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 (2020)
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) (2019)
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) (2018)
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) (2016)
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) (2016)
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)
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) (2014)
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) (2014)
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 (2014)
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)
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)
Pedro J. Ballester
Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression. PRIB (2012)
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 (2012)
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) (2011)
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) (2010)
Pedro J. Ballester, W. Graham Richards
Ultrafast shape recognition to search compound databases for similar molecular shapes. J. Comput. Chem. 28 (10) (2007)
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)
Pedro J. Ballester, W. Graham Richards
A Multiparent Version of the Parent-Centric Normal Crossover for Multimodal Optimization. IEEE Congress on Evolutionary Computation (2006)
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) (2006)
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)
Pedro J. Ballester, Jonathan N. Carter
An Algorithm to Identify Clusters of Solutions in Multimodal Optimisation. WEA (2004)
Pedro J. Ballester, Jonathan N. Carter
Tackling an Inverse Problem from the Petroleum Industry with a Genetic Algorithm for Sampling. GECCO (2) (2004)
Pedro J. Ballester, Jonathan N. Carter
An Effective Real-Parameter Genetic Algorithm with Parent Centric Normal Crossover for Multimodal Optimisation. GECCO (1) (2004)
Pedro J. Ballester, Jonathan N. Carter
Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization. GECCO (2003)