In Journal of chemical information and modeling
In-silico prediction of human oral bioavailability is a relevant tool for the selection of potential drug candidates and for the rejection of those molecules with less probability of success during the early stages of drug discovery and development. However, the high variability and complexity of oral bioavailability and the limited experimental data in public domain have mainly restricted the devel-opment of reliable in-silico models to predict this property from the chemical structure. In this study we present a KNIME automated workflow to predict human oral bioavailability of new drug and drug-like molecules, based on five machine learning approaches combined into an ensemble model. The workflow is freely accessible and allows the quickly and easily prediction of oral bioa-vailability for new molecules, where users do not require any knowledge or advanced experience in machine learning or statistical modeling to automatically obtain their predictions, increasing the potential use of the present proposal.
Falcón-Cano Gabriela, Molina Christophe, Cabrera-Pérez Miguel Angel