Publicaciones en colaboración con investigadoras/es de Ikerbasque, Fundación Vasca para la Ciencia (123)

2024

  1. Identification of Riluzole derivatives as novel calmodulin inhibitors with neuroprotective activity by a joint synthesis, biosensor, and computational guided strategy

    Biomedicine and Pharmacotherapy, Vol. 174

  2. Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family

    Journal of Chemical Information and Modeling, Vol. 64, Núm. 6, pp. 1841-1852

  3. MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products

    Journal of Cheminformatics, Vol. 16, Núm. 1

  4. Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling

    Environmental Science and Technology, Vol. 58, Núm. 23, pp. 10116-10127

  5. NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

    Journal of Nanobiotechnology, Vol. 22, Núm. 1

  6. On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

    Beilstein Journal of Nanotechnology, Vol. 15, pp. 535-555

  7. OptiMo-LDLr: An Integrated In Silico Model with Enhanced Predictive Power for LDL Receptor Variants, Unraveling Hot Spot Pathogenic Residues

    Advanced Science, Vol. 11, Núm. 13

  8. Warfarin–A natural anticoagulant: A review of research trends for precision medication

    Phytomedicine, Vol. 128

2023

  1. Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends

    Computers in Biology and Medicine, Vol. 155

  2. Trends in Nanoparticles for Leishmania Treatment: A Bibliometric and Network Analysis

    Diseases, Vol. 11, Núm. 4

2022

  1. A Fuzzy System Classification Approach for QSAR Modeling of αAmylase and α-Glucosidase Inhibitors

    Current Computer-Aided Drug Design, Vol. 18, Núm. 7, pp. 469-479

  2. Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds

    Molecular Pharmaceutics, Vol. 19, Núm. 7, pp. 2151-2163

  3. Multi-output chemometrics model for gasoline compounding

    Fuel, Vol. 310

  4. Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches

    Environmental Research, Vol. 214

  5. Towards rational nanomaterial design by predicting drug-nanoparticle system interaction vs. bacterial metabolic networks

    Environmental Science: Nano, Vol. 9, Núm. 4, pp. 1391-1413

2021

  1. Ifptml mapping of drug graphs with protein and chromosome structural networks vs. Pre‐clinical assay information for discovery of antimalarial compounds

    International Journal of Molecular Sciences, Vol. 22, Núm. 23

  2. MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants

    JACC: Basic to Translational Science, Vol. 6, Núm. 11, pp. 815-827

  3. New experimental and computational tools for drug discovery. Part – XII

    Current Topics in Medicinal Chemistry

  4. Palladium-mediated synthesis and biological evaluation of C-10b substituted Dihydropyrrolo[1,2-b]isoquinolines as antileishmanial agents

    European Journal of Medicinal Chemistry, Vol. 220

  5. Predicting metabolic reaction networks with Perturbation-Theory Machine Learning (PTML) models

    Current Topics in Medicinal Chemistry, Vol. 21, Núm. 9, pp. 819-827