Pedro J. Ballester

37 papers A 1B 3Journal 24Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Conformal prediction of molecule-induced cancer cell growth inhibition challenged by strong distribution shifts. Pattern Recognit. Saiveth Hernández-Hernández, Qianrong Guo, Pedro J. Ballester
2025	J	`jnl`	Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors. Briefings Bioinform. Klaudia Caba, Viet-Khoa Tran-Nguyen, Taufiq Rahman, Pedro J. Ballester
2025	J	`jnl`	Deconstructing biomarker generalisation failure in anti-pd1 cancer immunotherapy response: a multi-cohort framework. Briefings Bioinform. Elizabeth Amelia, Chayanit Piyawajanusorn, Pedro J. Ballester
2025	J	`jnl`	Deconstructing biomarker generalisation failure in cancer anti-pd1 immunotherapy: toward adaptive patient stratification. Briefings Bioinform. Elizabeth Amelia, Chayanit Piyawajanusorn, Pedro J. Ballester
2025	J	`jnl`	Target-specific machine-learning scoring functions enhance virtual screening for Briefings Bioinform. Klaudia Caba, Pedro J. Ballester
2025	J	`jnl`	UMAP-based clustering split for rigorous evaluation of AI models for virtual screening on cancer cell lines. J. Cheminformatics Qianrong Guo, Saiveth Hernández-Hernández, Pedro J. Ballester
2024	J	`jnl`	Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors. J. Cheminformatics Klaudia Caba, Viet-Khoa Tran-Nguyen, Taufiq Rahman, Pedro J. Ballester
2024	—	`conf`	Scaffold Splits Overestimate Virtual Screening Performance. ICANN (10) Qianrong Guo, Saiveth Hernández-Hernández, Pedro J. Ballester
2024	J	`jnl`	Scaffold Splits Overestimate Virtual Screening Performance. CoRR Qianrong Guo, Saiveth Hernández-Hernández, Pedro J. Ballester
2023	J	`jnl`	Beware of Simple Methods for Structure-Based Virtual Screening: The Critical Importance of Broader Comparisons. J. Chem. Inf. Model. Viet-Khoa Tran-Nguyen, Pedro J. Ballester
2022	—	`conf`	Conformal prediction of small-molecule drug resistance in cancer cell lines. COPA Saiveth Hernández-Hernández, Sachin Vishwakarma, Pedro J. Ballester
2021	J	`jnl`	A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling. Briefings Bioinform. Chayanit Piyawajanusorn, Linh C. Nguyen, Ghita Ghislat, Pedro J. Ballester
2021	J	`jnl`	The impact of compound library size on the performance of scoring functions for structure-based virtual screening. Briefings Bioinform. Louison Fresnais, Pedro J. Ballester
2020	J	`jnl`	Stochastic-based Neural Network hardware acceleration for an efficient ligand-based virtual screening. CoRR Christiam F. Frasser, Carola de Benito, Vincent Canals, Miquel Roca, Pedro J. Ballester, Josep L. Rosselló
2019	J	`jnl`	Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data. Bioinform. Hongjian Li, Jiangjun Peng, Pavel Sidorov, Yee Leung, Kwong-Sak Leung, Man Hon Wong, Gang Lu, Pedro J. Ballester
2018	J	`jnl`	A Stochastic Spiking Neural Network for Virtual Screening. IEEE Trans. Neural Networks Learn. Syst. Antoni Morro, Vicent Canals, Antoni Oliver, Miquel L. Alomar, Fabio Galán-Prado, Pedro J. Ballester, José Luis Rosselló
2016	J	`jnl`	Correcting the impact of docking pose generation error on binding affinity prediction. BMC Bioinform. Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester
2016	J	`jnl`	USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniques. Nucleic Acids Res. Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester
2015	—	`conf`	The Use of Random Forest to Predict Binding Affinity in Docking. IWBBIO (2) Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester
2014	J	`jnl`	Does a More Precise Chemical Description of Protein-Ligand Complexes Lead to More Accurate Prediction of Binding Affinity? J. Chem. Inf. Model. Pedro J. Ballester, Adrian Schreyer, Tom L. Blundell
2014	J	`jnl`	Prospective virtual screening for novel p53-MDM2 inhibitors using ultrafast shape recognition. J. Comput. Aided Mol. Des. Sachin P. Patil, Pedro J. Ballester, Cassidy R. Kerezsi
2014	J	`jnl`	Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study. BMC Bioinform. Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester
2014	—	`conf`	The Impact of Docking Pose Generation Error on the Prediction of Binding Affinity. CIBB Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester
2014	—	`conf`	The Importance of the Regression Model in the Structure-Based Prediction of Protein-Ligand Binding. CIBB Hongjian Li, Kwong-Sak Leung, Man Hon Wong, Pedro J. Ballester
2012	—	`conf`	Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression. PRIB Pedro J. Ballester
2012	J	`jnl`	Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties CoRR Michael P. Menden, Francesco Iorio, Mathew Garnett, Ultan McDermott, Cyril Benes, Pedro J. Ballester, Julio Saez-Rodriguez
2011	J	`jnl`	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. Pedro J. Ballester, John B. O. Mitchell
2010	J	`jnl`	A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinform. Pedro J. Ballester, John B. O. Mitchell
2007	B	`conf`	Model calibration of a real petroleum reservoir using a parallel real-coded genetic algorithm. IEEE Congress on Evolutionary Computation Pedro J. Ballester, Jonathan N. Carter
2007	J	`jnl`	Ultrafast shape recognition to search compound databases for similar molecular shapes. J. Comput. Chem. Pedro J. Ballester, W. Graham Richards
2006	B	`conf`	A Multiparent Version of the Parent-Centric Normal Crossover for Multimodal Optimization. IEEE Congress on Evolutionary Computation Pedro J. Ballester, W. Graham Richards
2006	J	`jnl`	Our calibrated model has poor predictive value: An example from the petroleum industry. Reliab. Eng. Syst. Saf. Jonathan N. Carter, Pedro J. Ballester, Zohreh Tavassoli, Peter R. King
2005	B	`conf`	Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX. Congress on Evolutionary Computation Pedro J. Ballester, John Stephenson, Jonathan N. Carter, Kerry Gallagher
2004	—	`conf`	An Algorithm to Identify Clusters of Solutions in Multimodal Optimisation. WEA Pedro J. Ballester, Jonathan N. Carter
2004	—	`conf`	An Effective Real-Parameter Genetic Algorithm with Parent Centric Normal Crossover for Multimodal Optimisation. GECCO (1) Pedro J. Ballester, Jonathan N. Carter
2004	—	`conf`	Tackling an Inverse Problem from the Petroleum Industry with a Genetic Algorithm for Sampling. GECCO (2) Pedro J. Ballester, Jonathan N. Carter
2003	A	`conf`	Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions in Multi-modal Optimization. GECCO Pedro J. Ballester, Jonathan N. Carter