Raquel Rodríguez-Pérez

13 papers Journal 12

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts. J. Chem. Inf. Model. Raquel Parrondo-Pizarro, Jessica Lanini, Raquel Rodríguez-Pérez
2025	J	`jnl`	Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery. J. Chem. Inf. Model. Jeremy R. Ash, Cas Wognum, Raquel Rodríguez-Pérez, Matteo Aldeghi, Alan C. Cheng, Djork-Arné Clevert, Ola Engkvist, Cheng Fang, Daniel J. Price, Jacqueline M. Hughes-Oliver, W. Patrick Walters
2024	J	`jnl`	A call for an industry-led initiative to critically assess machine learning for real-world drug discovery. Nat. Mac. Intell. Cas Wognum, Jeremy R. Ash, Matteo Aldeghi, Raquel Rodríguez-Pérez, Cheng Fang, Alan C. Cheng, Daniel J. Price, Djork-Arné Clevert, Ola Engkvist, W. Patrick Walters
2024	J	`jnl`	UNIQUE: A Framework for Uncertainty Quantification Benchmarking. J. Chem. Inf. Model. Jessica Lanini, Minh Tam Davide Huynh, Gaetano Scebba, Nadine Schneider, Raquel Rodríguez-Pérez
2023	J	`jnl`	Explaining compound activity predictions with a substructure-aware loss for graph neural networks. J. Cheminformatics Kenza Amara, Raquel Rodríguez-Pérez, José Jiménez-Luna
2022	J	`jnl`	Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. J. Cheminformatics Jürgen Bajorath, Ana L. Chávez-Hernández, Miquel Duran-Frigola, Eli Fernández-de Gortari, Johann Gasteiger, Edgar López-López, Gerald M. Maggiora, José L. Medina-Franco, Oscar Méndez-Lucio, Jordi Mestres, Ramón Alain Miranda-Quintana, Tudor I. Oprea, Fabien Plisson, Fernando D. Prieto-Martínez, Raquel Rodríguez-Pérez, Paola Rondón-Villarreal, Fernanda I. Saldívar-González, Norberto Sánchez-Cruz, Marilia Valli
2022	J	`jnl`	Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery. J. Comput. Aided Mol. Des. Raquel Rodríguez-Pérez, Jürgen Bajorath
2022	J	`jnl`	Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks. J. Chem. Inf. Model. Seid Hamzic, Richard A. Lewis, Sandrine Desrayaud, Cihan Soylu, Mike Fortunato, Grégori Gerebtzoff, Raquel Rodríguez-Pérez
2021	J	`jnl`	Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions. J. Comput. Aided Mol. Des. Raquel Rodríguez-Pérez, Jürgen Bajorath
2020	J	`jnl`	Assessing the information content of structural and protein-ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning. J. Cheminformatics Raquel Rodríguez-Pérez, Filip Miljkovic, Jürgen Bajorath
2020	J	`jnl`	Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J. Comput. Aided Mol. Des. Raquel Rodríguez-Pérez, Jürgen Bajorath
2020	—	`—`	Machine Learning Methodologies for Interpretable Compound Activity Predictions Raquel Rodríguez-Pérez
2017	J	`jnl`	Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. J. Chem. Inf. Model. Raquel Rodríguez-Pérez, Martin Vogt, Jürgen Bajorath