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Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values

by Rodríguez-Pérez, Raquel, Bajorath, Jürgen
Published in Journal of medicinal chemistry (27.08.2020)

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Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions

by Rodríguez-Pérez, Raquel, Bajorath, Jürgen
Published in Journal of computer-aided molecular design (01.10.2020)

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Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties

by Di Lascio, Elena, Gerebtzoff, Grégori, Rodríguez-Pérez, Raquel
Published in Molecular pharmaceutics (06.03.2023)

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Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction

by Rodríguez-Pérez, Raquel, Vogt, Martin, Bajorath, Jürgen
Published in ACS omega (31.10.2017)

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Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects

by Fluetsch, Andrin, Di Lascio, Elena, Gerebtzoff, Grégori, Rodríguez-Pérez, Raquel
Published in Molecular pharmaceutics (01.04.2024)

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Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition

by Fluetsch, Andrin, Trunzer, Markus, Gerebtzoff, Grégori, Rodríguez-Pérez, Raquel
Published in Chemical research in toxicology (15.04.2024)

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Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes

by Miljković, Filip, Rodríguez-Pérez, Raquel, Bajorath, Jürgen
Published in Journal of medicinal chemistry (27.08.2020)

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Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors

by Rodríguez-Pérez, Raquel, Bajorath, Jürgen
Published in ACS omega (28.02.2019)

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Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks

by Hamzic, Seid, Lewis, Richard, Desrayaud, Sandrine, Soylu, Cihan, Fortunato, Mike, Gerebtzoff, Grégori, Rodríguez-Pérez, Raquel
Published in Journal of chemical information and modeling (11.07.2022)

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Multispecies Machine Learning Predictions of In Vitro Intrinsic Clearance with Uncertainty Quantification Analyses

by Rodríguez-Pérez, Raquel, Trunzer, Markus, Schneider, Nadine, Faller, Bernard, Gerebtzoff, Grégori
Published in Molecular pharmaceutics (02.01.2023)

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Application of machine learning models for property prediction to targeted protein degraders

by Peteani, Giulia, Huynh, Minh Tam Davide, Gerebtzoff, Grégori, Rodríguez-Pérez, Raquel
Published in Nature communications (09.07.2024)

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Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics

by Rodríguez-Pérez, Raquel, Bajorath, Jürgen
Published in Scientific reports (09.07.2021)

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Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds

by Rodríguez-Pérez, Raquel, Vogt, Martin, Bajorath, Jürgen
Published in Journal of chemical information and modeling (24.04.2017)

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Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis

by Miljković, Filip, Rodríguez-Pérez, Raquel, Bajorath, Jürgen
Published in ACS omega (14.12.2021)

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Identification of bile salt export pump inhibitors using machine learning: Predictive safety from an industry perspective

by Rodríguez-Pérez, Raquel, Gerebtzoff, Grégori
Published in Artificial intelligence in the life sciences (01.12.2021)

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Explaining compound activity predictions with a substructure-aware loss for graph neural networks

by Amara, Kenza, Rodríguez-Pérez, Raquel, Jiménez-Luna, José
Published in Journal of cheminformatics (25.07.2023)

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Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data

by Rodríguez-Pérez, Raquel, Bajorath, Jürgen
Published in ACS omega (30.09.2018)

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Prediction of Compound Profiling Matrices Using Machine Learning

by Rodríguez-Pérez, Raquel, Miyao, Tomoyuki, Jasial, Swarit, Vogt, Martin, Bajorath, Jürgen
Published in ACS omega (30.04.2018)

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EdgeSHAPer: Bond-centric Shapley value-based explanation method for graph neural networks

by Mastropietro, Andrea, Pasculli, Giuseppe, Feldmann, Christian, Rodríguez-Pérez, Raquel, Bajorath, Jürgen
Published in iScience (21.10.2022)

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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

by Rodríguez-Pérez, Raquel, Miljković, Filip, Bajorath, Jürgen
Published in Journal of cheminformatics (24.05.2020)

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