Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. - Raquel Rodríguez-Pérez; Martin Vogt 001; Jürgen Bajorath (J. Chem. Inf. Model., 2017)

Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes. - Filip Miljkovic; Raquel Rodríguez-Pérez; Jürgen Bajorath (Zenodo, 2019)

Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes. - Filip Miljkovic; Raquel Rodríguez-Pérez; Jürgen Bajorath (Zenodo, 2019)

Compound activity classes from ChEMBL for machine learning analysis. - Raquel Rodríguez-Pérez; Jürgen Bajorath (Zenodo, 2019)

Compound activity classes from ChEMBL for machine learning analysis. - Raquel Rodríguez-Pérez; Jürgen Bajorath (Zenodo, 2019)

Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. - Raquel Rodríguez-Pérez; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2020)

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. - Raquel Rodríguez-Pérez; Filip Miljkovic; Jürgen Bajorath (J. Cheminformatics, 2020)

Machine Learning Methodologies for Interpretable Compound Activity Predictions - Raquel Rodríguez-Pérez (2020)

Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions. - Raquel Rodríguez-Pérez; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2021)

Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery. - Raquel Rodríguez-Pérez; Jürgen Bajorath (J. Comput. Aided Mol. Des., 2022)

Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. - 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; et al. (J. Cheminformatics, 2022)

Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks. - Seid Hamzic; Richard A. Lewis; Sandrine Desrayaud; Cihan Soylu; Mike Fortunato; Grégori Gerebtzoff; Raquel Rodríguez-Pérez (J. Chem. Inf. Model., 2022)

Explaining compound activity predictions with a substructure-aware loss for graph neural networks. - Kenza Amara; Raquel Rodríguez-Pérez; José Jiménez-Luna (J. Cheminformatics, 2023)

UNIQUE: A Framework for Uncertainty Quantification Benchmarking. - Jessica Lanini; Minh Tam Davide Huynh; Gaetano Scebba; Nadine Schneider; Raquel Rodríguez-Pérez (J. Chem. Inf. Model., 2024)

A call for an industry-led initiative to critically assess machine learning for real-world drug discovery. - 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 (Nat. Mac. Intell., 2024)