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ADME prediction with KNIME: In silico aqueous solubility consensus model based on supervised recursive random forest approaches

Gabriela Falcón-Cano ; Centro de Bioactivos Químicos. Universidad Central
Christophe Molina ; PIKAÏROS S.A., 31650 Saint Orens de Gameville
Miguel Angel Cabrera-Pérez ; Centro de Bioactivos Químicos. Universidad Central

Puni tekst: engleski pdf 3.031 Kb

str. 251-273

preuzimanja: 683



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. In this study a large and diverse database was generated with aqueous solubility values collected from two public sources; two new recursive machine-learning approaches were developed for data cleaning and variable selection, and a consensus model based on regression and classification algorithms was created. The modeling protocol, which includes the curation of chemical and experimental data, was implemented in KNIME, with the aim of obtaining an automated workflow for the prediction of new databases. Finally, we compared several methods or models available in the literature with our consensus model, showing results comparable or even outperforming previous published models.


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