Raquel Rodríguez-Pérez

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 8

Top Topics

Systems Biology

Top Venues

J. Cheminformatics

J. Comput. Aided Mol. Des.

J. Chem. Inf. Model.

Publications

Kenza Amara, Raquel Rodríguez-Pérez, José Jiménez-Luna
Explaining compound activity predictions with a substructure-aware loss for graph neural networks. J. Cheminformatics 15 (1) (2023)
Seid Hamzic, Richard A. Lewis, Sandrine Desrayaud, Cihan Soylu, Mike Fortunato, Grégori Gerebtzoff, Raquel Rodríguez-Pérez
Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks. J. Chem. Inf. Model. 62 (13) (2022)
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
Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. J. Cheminformatics 14 (1) (2022)
Raquel Rodríguez-Pérez, Jürgen Bajorath
Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery. J. Comput. Aided Mol. Des. 36 (5) (2022)
Raquel Rodríguez-Pérez, Jürgen Bajorath
Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions. J. Comput. Aided Mol. Des. 35 (3) (2021)
Raquel Rodríguez-Pérez, Jürgen Bajorath
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J. Comput. Aided Mol. Des. 34 (10) (2020)
Raquel Rodríguez-Pérez, Filip Miljkovic, Jürgen Bajorath
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 12 (1) (2020)
Raquel Rodríguez-Pérez, Martin Vogt, Jürgen Bajorath
Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. J. Chem. Inf. Model. 57 (4) (2017)