Publicacións en colaboración con investigadores/as de Ikerbasque, Fundación Vasca para la Ciencia (46)

2024

  1. 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

  2. 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

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

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

  4. 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

  5. Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses

    Scientific Reports, Vol. 14, Núm. 1

2022

  1. Multi-output chemometrics model for gasoline compounding

    Fuel, Vol. 310

2021

  1. Prediction of anti-glioblastoma drug-decorated nanoparticle delivery systems using molecular descriptors and machine learning

    International Journal of Molecular Sciences, Vol. 22, Núm. 21

  2. Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML)

    ACS Chemical Neuroscience, Vol. 12, Núm. 1, pp. 203-215

2020

  1. A multi-objective approach for anti-osteosarcoma cancer agents discovery through drug repurposing

    Pharmaceuticals, Vol. 13, Núm. 11, pp. 1-16

  2. Gene prioritization through consensus strategy, enrichment methodologies analysis, and networking for osteosarcoma pathogenesis

    International Journal of Molecular Sciences, Vol. 21, Núm. 3

  3. MCDcalc: Markov chain molecular descriptors calculator for medicinal chemistry

    Current Topics in Medicinal Chemistry, Vol. 20, Núm. 4, pp. 305-317

  4. Net-net autoML selection of artificial neural network topology for brain connectome prediction

    Applied Sciences (Switzerland), Vol. 10, Núm. 4

  5. OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

    Scientific Reports, Vol. 10, Núm. 1

  6. Perturbation-theory machine learning (PTML) multilabel model of the CheMBL dataset of preclinical assays for antisarcoma compounds

    ACS Omega, Vol. 5, Núm. 42, pp. 27211-27220

  7. Prediction of antimalarial drug-decorated nanoparticle delivery systems with random forest models

    Biology, Vol. 9, Núm. 8, pp. 1-15

  8. Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks

    Scientific Reports, Vol. 10, Núm. 1

  9. Ptml multi-label algorithms: Models, software, and applications

    Current Topics in Medicinal Chemistry, Vol. 20, Núm. 25, pp. 2326-2337

2019

  1. PTML Model of Enzyme Subclasses for Mining the Proteome of Biofuel Producing Microorganisms

    Journal of Proteome Research, Vol. 18, Núm. 7, pp. 2735-2746

  2. Perturbation Theory Machine Learning Modeling of Immunotoxicity for Drugs Targeting Inflammatory Cytokines and Study of the Antimicrobial G1 Using Cytometric Bead Arrays

    Chemical Research in Toxicology, Vol. 32, Núm. 9, pp. 1811-1823

2018

  1. Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis

    Scientific Reports, Vol. 8, Núm. 1