Publicacions en què col·labora amb Cristian-Robert Munteanu (58)

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

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

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

  2. Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems

    Scientific Reports, Vol. 8, Núm. 1

  3. Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l -Prolyl- l -leucyl-glycinamide Peptidomimetics

    ACS Chemical Neuroscience, Vol. 9, Núm. 11, pp. 2572-2587

2017

  1. Carbon nanotubes’ effect on mitochondrial oxygen flux dynamics: Polarography experimental study and machine learning models using star graph trace invariants of raman spectra

    Nanomaterials, Vol. 7, Núm. 11

  2. Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory

    Scientific Reports, Vol. 7, Núm. 1

  3. Experimental study and ANN dual-time scale perturbation model of electrokinetic properties of microbiota

    Frontiers in Microbiology, Vol. 8, Núm. JUN

  4. Experimental-Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov-Shannon Entropy Invariants

    Journal of Chemical Information and Modeling, Vol. 57, Núm. 5, pp. 1029-1044