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Raquel Rodríguez-Pérez
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2020 – today
- 2024
- [j9]Cas Wognum, Jeremy R. Ash, Matteo Aldeghi, Raquel Rodríguez-Pérez, Cheng Fang
, Alan C. Cheng, Daniel J. Price, Djork-Arné Clevert, Ola Engkvist
A call for an industry-led initiative to critically assess machine learning for real-world drug discovery. Nat. Mac. Intell. 6(10): 1120-1121 (2024) - 2023
- [j8]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): 67 (2023) - 2022
- [j7]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): 355-362 (2022) - [j6]Jürgen Bajorath, Ana L. Chávez-Hernández
Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. J. Cheminformatics 14(1): 82 (2022) - [j5]Seid Hamzic, Richard A. Lewis
Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks. J. Chem. Inf. Model. 62(13): 3180-3190 (2022) - 2021
- [j4]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): 285-295 (2021) - 2020
- [b1]Raquel Rodríguez-Pérez:
Machine Learning Methodologies for Interpretable Compound Activity Predictions. University of Bonn, Germany, 2020 - [j3]Raquel Rodríguez-Pérez
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J. Comput. Aided Mol. Des. 34(10): 1013-1026 (2020) - [j2]Raquel Rodríguez-Pérez
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): 36 (2020)
2010 – 2019
- 2019
- [d4]Filip Miljkovic, Raquel Rodríguez-Pérez, Jürgen Bajorath
Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes. Version 1. Zenodo, 2019 [all versions] - [d3]Filip Miljkovic, Raquel Rodríguez-Pérez, Jürgen Bajorath
Machine Learning Models for Predicting Kinase Inhibitors with Different Binding Modes. Version 2. Zenodo, 2019 [all versions] - [d2]Raquel Rodríguez-Pérez
Compound activity classes from ChEMBL for machine learning analysis. Version 1. Zenodo, 2019 [all versions] - [d1]Raquel Rodríguez-Pérez
Compound activity classes from ChEMBL for machine learning analysis. Version 2. Zenodo, 2019 [all versions] - 2017
- [j1]Raquel Rodríguez-Pérez
Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. J. Chem. Inf. Model. 57(4): 710-716 (2017)
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