Lista de obras -

A Comparative Study of Nonlinear Machine Learning for the "In Silico" Depiction of Tyrosinase Inhibitory Activity from Molecular Structure

artículo científico publicado en 2011

A Simple Method to Predict Blood-Brain Barrier Permeability of Drug-Like Compounds Using Classification Trees

artículo científico publicado en 2017

A Two QSAR Way for Antidiabetic Agents Targeting Using α-Amylase and α-Glucosidase Inhibitors: Model Parameters Settings in Artificial Intelligence Techniques

scholarly article by Karel Dieguez-Santana et al published 20 July 2017 in Letters in drug design & discovery

A rational workflow for sequential virtual screening of chemical libraries on searching for new tyrosinase inhibitors

artículo científico publicado en 2014

Analysis of Proteasome Inhibition Prediction Using Atom-Based Quadratic Indices Enhanced by Machine Learning Classification Techniques

scholarly article by Gerardo Casanola-Martin et al published 31 May 2014 in Letters in drug design & discovery

Atom- and bond-based 2D TOMOCOMD-CARDD approach and ligand-based virtual screening for the drug discovery of new tyrosinase inhibitors.

artículo científico publicado en 2008

Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies

scientific article published on 16 May 2019

Bond-based 2D TOMOCOMD-CARDD approach for drug discovery: aiding decision-making in ‘in silico’ selection of new lead tyrosinase inhibitors

artículo científico publicado en 2007

Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening

artículo científico publicado en 2015

Carbon Nanotubes' Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra.

artículo científico publicado en 2017

Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays.

artículo científico publicado en 2007

Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling.

artículo científico publicado en 2015

Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry.

artículo científico publicado en 2017

Ligand-based computer-aided discovery of tyrosinase inhibitors. Applications of the TOMOCOMD-CARDD method to the elucidation of new compounds

artículo científico publicado en 2010

Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.

artículo científico publicado en 2017

Novel coumarin-based tyrosinase inhibitors discovered by OECD principles-validated QSAR approach from an enlarged, balanced database.

artículo científico publicado en 2010

Prediction of Aquatic Toxicity of Benzene Derivatives to Tetrahymena pyriformis According to OECD Principles.

artículo científico publicado en 2016

Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database.

artículo científico publicado en 2016

Prediction of tyrosinase inhibition activity using atom-based bilinear indices.

artículo científico publicado en 2007

QSAR-Based CMs and TOMOCOMD-CARD Approach for the Discovery of New Tyrosinase Inhibitor Chemicals

QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents.

artículo científico publicado en 2015

Quantitative structure-activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries

artículo científico publicado en 2017

TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices.

artículo científico publicado en 2006

Tyrosinase enzyme: 1. An overview on a pharmacological target

artículo científico

Vanilloid Derivatives as Tyrosinase Inhibitors Driven by Virtual Screening‐Based QSAR Models ⬇️

artículo científico publicado el 1 de diciembre de 2010

Lista de obras de

A Comparative Study of Nonlinear Machine Learning for the "In Silico" Depiction of Tyrosinase Inhibitory Activity from Molecular Structure

A Simple Method to Predict Blood-Brain Barrier Permeability of Drug-Like Compounds Using Classification Trees

A Two QSAR Way for Antidiabetic Agents Targeting Using α-Amylase and α-Glucosidase Inhibitors: Model Parameters Settings in Artificial Intelligence Techniques

A rational workflow for sequential virtual screening of chemical libraries on searching for new tyrosinase inhibitors

Analysis of Proteasome Inhibition Prediction Using Atom-Based Quadratic Indices Enhanced by Machine Learning Classification Techniques

Atom- and bond-based 2D TOMOCOMD-CARDD approach and ligand-based virtual screening for the drug discovery of new tyrosinase inhibitors.

Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies

Bond-based 2D TOMOCOMD-CARDD approach for drug discovery: aiding decision-making in ‘in silico’ selection of new lead tyrosinase inhibitors

Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening

Carbon Nanotubes' Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra.

Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays.

Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling.

Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry.

Ligand-based computer-aided discovery of tyrosinase inhibitors. Applications of the TOMOCOMD-CARDD method to the elucidation of new compounds

Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis.

Novel coumarin-based tyrosinase inhibitors discovered by OECD principles-validated QSAR approach from an enlarged, balanced database.

Prediction of Aquatic Toxicity of Benzene Derivatives to Tetrahymena pyriformis According to OECD Principles.

Prediction of acute toxicity of phenol derivatives using multiple linear regression approach for Tetrahymena pyriformis contaminant identification in a median-size database.

Prediction of tyrosinase inhibition activity using atom-based bilinear indices.

QSAR-Based CMs and TOMOCOMD-CARD Approach for the Discovery of New Tyrosinase Inhibitor Chemicals

QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents.

Quantitative structure-activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries

TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices.

Tyrosinase enzyme: 1. An overview on a pharmacological target

Vanilloid Derivatives as Tyrosinase Inhibitors Driven by Virtual Screening‐Based QSAR Models ⬇️