Publications by authors named "Gabriela Falcon-Cano"
The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules.
View Article and Find Full Text PDFArticle Synopsis
- - The text discusses computational models that predict how well drugs dissolve in water based on their molecular structure, an important tool in drug design and discovery.
- - The authors review their previously developed recursive random forest model, evaluating its effectiveness on new data from the second "Solubility Challenge," despite inconsistencies in the data collected.
- - They also offer a KNIME automated workflow to help researchers easily use their model to predict solubility for potential new drug candidates early in the development process.
In-silico prediction of aqueous solubility plays an important role during the drug discovery and development processes. For many years, the limited performance of in-silico solubility models has been attributed to the lack of high-quality solubility data for pharmaceutical molecules. However, some studies suggest that the poor accuracy of solubility prediction is not related to the quality of the experimental data and that more precise methodologies (algorithms and/or set of descriptors) are required for predicting aqueous solubility for pharmaceutical molecules.
View Article and Find Full Text PDFArticle Synopsis
- In silico prediction of human oral bioavailability is crucial for identifying promising drug candidates and filtering out less effective ones in the early drug development stages.
- The study introduces a user-friendly KNIME automated workflow that employs five machine learning methods in an ensemble model to predict oral bioavailability.
- This accessible tool enables users without a background in machine learning to easily predict the oral bioavailability of new drug-like molecules, enhancing its practical applications.