Publications dans lesquelles il/elle collabore avec HUMBERTO GONZALEZ DIAZ (30)

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

    Phytomedicine, Vol. 128

2022

  1. Multi-output chemometrics model for gasoline compounding

    Fuel, Vol. 310

  2. Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2-Acylpyrrole Derivatives

    Journal of chemical information and modeling, Vol. 62, Núm. 16, pp. 3928-3940

2021

  1. 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

  2. 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

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

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

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

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

  2. PTML Model for Selection of Nanoparticles, Anticancer Drugs, and Vitamins in the Design of Drug-Vitamin Nanoparticle Release Systems for Cancer Cotherapy

    Molecular pharmaceutics, Vol. 17, Núm. 7, pp. 2612-2627

  3. PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives

    ACS Combinatorial Science, Vol. 22, Núm. 3, pp. 129-141

  4. 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

  5. Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models

    Nanoscale, Vol. 12, Núm. 25, pp. 13471-13483

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

    Biology, Vol. 9, Núm. 8, pp. 1-15

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

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

2019

  1. Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTPγS Binding Assays

    ACS Chemical Neuroscience, Vol. 10, Núm. 11, pp. 4476-4491

  2. Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models

    Nanoscale, Vol. 11, Núm. 45, pp. 21811-21823