Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization. - Pedro J. Ballester; Jonathan N. Carter (GECCO, 2003)

An Effective Real-Parameter Genetic Algorithm with Parent Centric Normal Crossover for Multimodal Optimisation. - Pedro J. Ballester; Jonathan N. Carter (GECCO (1), 2004)

Tackling an Inverse Problem from the Petroleum Industry with a Genetic Algorithm for Sampling. - Pedro J. Ballester; Jonathan N. Carter (GECCO (2), 2004)

An Algorithm to Identify Clusters of Solutions in Multimodal Optimisation. - Pedro J. Ballester; Jonathan N. Carter (WEA, 2004)

Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX. - Pedro J. Ballester; John Stephenson; Jonathan N. Carter; Kerry Gallagher (Congress on Evolutionary Computation, 2005)

A Multiparent Version of the Parent-Centric Normal Crossover for Multimodal Optimization. - Pedro J. Ballester; W. Graham Richards (IEEE Congress on Evolutionary Computation, 2006)

Our calibrated model has poor predictive value: An example from the petroleum industry. - Jonathan N. Carter; Pedro J. Ballester; Zohreh Tavassoli; Peter R. King (Reliab. Eng. Syst. Saf., 2006)

Model calibration of a real petroleum reservoir using a parallel real-coded genetic algorithm. - Pedro J. Ballester; Jonathan N. Carter (IEEE Congress on Evolutionary Computation, 2007)

Ultrafast shape recognition to search compound databases for similar molecular shapes. - Pedro J. Ballester; W. Graham Richards (J. Comput. Chem., 2007)

A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. - Pedro J. Ballester; John B. O. Mitchell (Bioinform., 2010)

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. - Pedro J. Ballester; John B. O. Mitchell (J. Chem. Inf. Model., 2011)

Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression. - Pedro J. Ballester (PRIB, 2012)

Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties - Michael P. Menden; Francesco Iorio; Mathew Garnett; Ultan McDermott; Cyril Benes; Pedro J. Ballester; Julio Saez-Rodriguez (CoRR, 2012)

The Importance of the Regression Model in the Structure-Based Prediction of Protein-Ligand Binding. - Hongjian Li; Kwong-Sak Leung; Man Hon Wong 001; Pedro J. Ballester (CIBB, 2014)

The Impact of Docking Pose Generation Error on the Prediction of Binding Affinity. - Hongjian Li; Kwong-Sak Leung; Man Hon Wong 001; Pedro J. Ballester (CIBB, 2014)

Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study. - Hongjian Li; Kwong-Sak Leung; Man Hon Wong 001; Pedro J. Ballester (BMC Bioinform., 2014)

Prospective virtual screening for novel p53-MDM2 inhibitors using ultrafast shape recognition. - Sachin P. Patil; Pedro J. Ballester; Cassidy R. Kerezsi (J. Comput. Aided Mol. Des., 2014)

Does a More Precise Chemical Description of Protein-Ligand Complexes Lead to More Accurate Prediction of Binding Affinity? - Pedro J. Ballester; Adrian Schreyer; Tom L. Blundell (J. Chem. Inf. Model., 2014)

The Use of Random Forest to Predict Binding Affinity in Docking. - Hongjian Li; Kwong-Sak Leung; Man Hon Wong 001; Pedro J. Ballester (IWBBIO (2), 2015)

Correcting the impact of docking pose generation error on binding affinity prediction. - Hongjian Li; Kwong-Sak Leung; Man Hon Wong 001; Pedro J. Ballester (BMC Bioinform., 2016)

USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniques. - Hongjian Li; Kwong-Sak Leung; Man Hon Wong 001; Pedro J. Ballester (Nucleic Acids Res., 2016)

A Stochastic Spiking Neural Network for Virtual Screening. - Antoni Morro; Vicent Canals; Antoni Oliver 002; Miquel L. Alomar; Fabio Galán-Prado; Pedro J. Ballester; José Luis Rosselló (IEEE Trans. Neural Networks Learn. Syst., 2018)

Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data. - Hongjian Li; Jiangjun Peng; Pavel Sidorov; Yee Leung; Kwong-Sak Leung; Man Hon Wong 001; Gang Lu; Pedro J. Ballester (Bioinform., 2019)

Stochastic-based Neural Network hardware acceleration for an efficient ligand-based virtual screening. - Christiam F. Frasser; Carola de Benito; Vincent Canals; Miquel Roca 001; Pedro J. Ballester; Josep L. Rosselló (CoRR, 2020)

The impact of compound library size on the performance of scoring functions for structure-based virtual screening. - Louison Fresnais; Pedro J. Ballester (Briefings Bioinform., 2021)

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling. - Chayanit Piyawajanusorn; Linh C. Nguyen; Ghita Ghislat; Pedro J. Ballester (Briefings Bioinform., 2021)

Conformal prediction of small-molecule drug resistance in cancer cell lines. - Saiveth Hernández-Hernández; Sachin Vishwakarma; Pedro J. Ballester (COPA, 2022)

Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons. - Viet-Khoa Tran-Nguyen; Pedro J. Ballester (J. Chem. Inf. Model., 2023)

Scaffold Splits Overestimate Virtual Screening Performance. - Qianrong Guo; Saiveth Hernández-Hernández; Pedro J. Ballester (ICANN (10), 2024)

Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors. - Klaudia Caba; Viet-Khoa Tran-Nguyen; Taufiq Rahman; Pedro J. Ballester (J. Cheminformatics, 2024)

UMAP-based clustering split for rigorous evaluation of AI models for virtual screening on cancer cell lines. - Qianrong Guo; Saiveth Hernández-Hernández; Pedro J. Ballester (J. Cheminformatics, 2025)