| PTML combinatorial model of ChEMBL compounds assays for multiple types of cancer H Bediaga, S Arrasate, H González-Díaz ACS Combinatorial Science 20 (11), 621-632, 2018 | 51 | 2018 |
| PTML multi-label algorithms: models, software, and applications B Ortega-Tenezaca, V Quevedo-Tumailli, H Bediaga, J Collados, ... Current Topics in Medicinal Chemistry 20 (25), 2326-2337, 2020 | 11 | 2020 |
| Synthesis, pharmacological, and biological evaluation of 2-furoyl-based MIF-1 peptidomimetics and the development of a general-purpose model for allosteric modulators (ALLOPTML) IE Sampaio-Dias, JE Rodriguez-Borges, V Yanez-Perez, S Arrasate, ... ACS Chemical Neuroscience 12 (1), 203-215, 2020 | 10 | 2020 |
| Multi-output chemometrics model for gasoline compounding H Bediaga, MI Moreno, S Arrasate, JL Vilas, L Orbe, E Unzueta, ... Fuel 310, 122274, 2022 | 6 | 2022 |
| On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems S He, JS Abarrategi, H Bediaga, S Arrasate, H González-Díaz Beilstein Journal of Nanotechnology 15 (1), 535-555, 2024 | | 2024 |
| NANO. PTML Model for read-across prediction of nanosystems in neurosciences. Computational model and experimental case of study S He, K Nader, JS Abarrategi, H Bediaga, D Nocedo-Mena, E Ascencio, ... | | 2024 |
| Supporting Information: OptiMo-LDLr: An Integrated In Silico Model with Enhanced Predictive Power for LDL Receptor Variants, Unraveling Hot Spot Pathogenic Residues A Larrea, I Sasiain, S Jebari-Benslaiman, U Galicia-García, KB Uribe, ... John Wiley & Sons, 2024 | | 2024 |
| On Additive Artificial Intelligence Discovery of Nanoparticle-Neurodegenerative Disease Drug Delivery Systems S He, JS Abarrategi, H Bediaga, S Arrasate, H González-Díaz Beilstein Archives 2024 (1), 10, 2024 | | 2024 |
| AI-Driven Cheminformatics Models of Chemical Mixtures for Sustainable Design of Drop-in Biofuel Blends H Bediaga, I Moreno-Benítez, S Arrasate, JL Vilas-Vilela, L Orbe, ... | | 2023 |
| IFPTML Algorithms: From Cheminformatics Models to Software Development, Startup Creation, and Innovation Transference H Bediaga | | 2023 |
| USEDAT-NEURODAT’21 IBRO-PERC Training Program R Isasi, AL Svalastog, Y Perez-Riverol, S Knafo, A Duardo-Sanchez, ... MDPI, 2022 | | 2022 |
| On Nanoscale Metal-Organic Frameworks for Therapeutic, Imaging, and Sensing Applications H Bediaga, M Urgoiti, A Letona, C Elicegui MDPI, 2021 | | 2021 |
| On the Study of Crystal Structures by Simulation and Modeling H Bediaga MDPI, 2021 | | 2021 |
| LAGA: New software for new drug design using Perturbation Theory and Machine Learning techniques J Collados, H Bediaga MDPI AG, 2020 | | 2020 |
| Quantitative Structure-Activity Relationship (QSAR) Model Review H Bediaga MDPI AG, 2020 | | 2020 |
| PTML-LDA One-Condition model for the design of new anti-cancer compounds H Bediaga MDPI AG, 2019 | | 2019 |
| PTML Model Prediction of Preclinical Activity H Bediaga, SA Gil MDPI AG, 2018 | | 2018 |
| MOL2NET, International Conference Series on Multidisciplinary Sciences H Bediaga | | |
