| Interpretation of machine learning models using shapley values: application to compound potency and multi‑target activity predictions R Rodríguez‑Pérez, J Bajorath Journal of computer-aided molecular design, 2020 | 304 | 2020 |
| Interpretation of compound activity predictions from complex machine learning models using local approximations and shapley values R Rodríguez-Pérez, J Bajorath Journal of medicinal chemistry 63 (16), 8761-8777, 2019 | 233 | 2019 |
| Support vector machine classification and regression prioritize different structural features for binary compound activity and potency value prediction R Rodríguez-Pérez, M Vogt, J Bajorath ACS omega 2 (10), 6371-6379, 2017 | 99 | 2017 |
| Evolution of support vector machine and regression modeling in chemoinformatics and drug discovery R Rodríguez-Pérez, J Bajorath Journal of Computer-Aided Molecular Design 36 (5), 355-362, 2022 | 73 | 2022 |
| Overoptimism in cross-validation when using partial least squares-discriminant analysis for omics data: a systematic study R Rodríguez-Pérez, L Fernández, S Marco Analytical and bioanalytical chemistry 410 (23), 5981-5992, 2018 | 55 | 2018 |
| Multitask machine learning for classifying highly and weakly potent kinase inhibitors R Rodriguez-Perez, J Bajorath Acs Omega 4 (2), 4367-4375, 2019 | 54 | 2019 |
| Explainable machine learning for property predictions in compound optimization: miniperspective R Rodríguez-Pérez, J Bajorath Journal of medicinal chemistry 64 (24), 17744-17752, 2021 | 43 | 2021 |
| Machine learning models for accurate prediction of kinase inhibitors with different binding modes F Miljkovic, R Rodriguez-Perez, J Bajorath Journal of medicinal chemistry 63 (16), 8738-8748, 2019 | 39 | 2019 |
| Prediction of compound profiling matrices using machine learning R Rodríguez-Pérez, T Miyao, S Jasial, M Vogt, J Bajorath ACS omega 3 (4), 4713-4723, 2018 | 38 | 2018 |
| Influence of varying training set composition and size on support vector machine-based prediction of active compounds R Rodríguez-Pérez, M Vogt, J Bajorath Journal of chemical information and modeling 57 (4), 710-716, 2017 | 35 | 2017 |
| Multi-unit calibration rejects inherent device variability of chemical sensor arrays A Solórzano, R Rodríguez-Pérez, M Padilla, T Graunke, L Fernandez, ... Sensors and Actuators B: Chemical 265, 142-154, 2018 | 33 | 2018 |
| Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis F Miljković, R Rodríguez-Pérez, J Bajorath ACS Omega 6 (49), 33293–33299, 2021 | 28 | 2021 |
| 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 … R Rodríguez-Pérez, J Bajorath ACS omega 3 (9), 12033-12040, 2018 | 25 | 2018 |
| Feature importance correlation from machine learning indicates functional relationships between proteins and similar compound binding characteristics R Rodríguez-Pérez, J Bajorath Scientific reports 11 (1), 14245, 2021 | 23 | 2021 |
| Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and … R Rodríguez-Pérez, F Miljković, J Bajorath Journal of Cheminformatics 12, 1-14, 2020 | 18 | 2020 |
| Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds J Bajorath, AL Chávez-Hernández, M Duran-Frigola, ... Journal of Cheminformatics 14 (1), 82, 2022 | 17 | 2022 |
| Predicting in vivo compound brain penetration using multi-task graph neural networks S Hamzic, R Lewis, S Desrayaud, C Soylu, M Fortunato, G Gerebtzoff, ... Journal of chemical information and modeling 62 (13), 3180-3190, 2022 | 16 | 2022 |
| Machine learning for small molecule drug discovery in academia and industry A Volkamer, S Riniker, E Nittinger, J Lanini, F Grisoni, E Evertsson, ... Artificial Intelligence in the Life Sciences 3, 100056, 2023 | 15 | 2023 |
| Multispecies machine learning predictions of in vitro intrinsic clearance with uncertainty quantification analyses R Rodríguez-Pérez, M Trunzer, N Schneider, B Faller, G Gerebtzoff Molecular Pharmaceutics 20 (1), 383-394, 2022 | 14 | 2022 |
| EdgeSHAPer: Bond-centric Shapley value-based explanation method for graph neural networks A Mastropietro, G Pasculli, C Feldmann, R Rodríguez-Pérez, J Bajorath Iscience 25 (10), 2022 | 14 | 2022 |
