Cheminformatics Perturbation-Theory Machine Learning Laboratory
CHEM PTML
Universidad Estatal Amazónica
Puyo, EcuadorPublicaciones en colaboración con investigadores/as de Universidad Estatal Amazónica (8)
2022
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Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches
Environmental Research, Vol. 214
2021
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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
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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
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Potentialities and applications of computational design methods in environmental and pharmacokinetic studies
Anales de la Academia de Ciencias de Cuba, Vol. 11, Núm. 1
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Predicting metabolic reaction networks with Perturbation-Theory Machine Learning (PTML) models
Current Topics in Medicinal Chemistry, Vol. 21, Núm. 9, pp. 819-827
2020
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Prediction of antimalarial drug-decorated nanoparticle delivery systems with random forest models
Biology, Vol. 9, Núm. 8, pp. 1-15
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Ptml multi-label algorithms: Models, software, and applications
Current Topics in Medicinal Chemistry, Vol. 20, Núm. 25, pp. 2326-2337
2016
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Multi-output model with box-jenkins operators of quadratic indices for prediction of malaria and cancer inhibitors targeting ubiquitin-proteasome pathway (UPP) proteins
Current Protein and Peptide Science, Vol. 17, Núm. 3, pp. 220-227