Gabriela Falcón-Cano†, Christophe Molina∥, and Miguel Ángel Cabrera-Pérez †, §, ‡
†Unit of Modeling and Experimental Biopharmaceutics. Centro de Bioactivos Químicos. Universidad Central “Marta Abreu” de las Villas. Santa Clara 54830, Villa Clara, Cuba
∥PIKAÏROS S.A, 31650 Saint Orens de Gameville, France
§Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Burjassot 46100, Valencia, Spain
‡Department of Engineering, Area of Pharmacy and Pharmaceutical Technology, Miguel Hernández University, 03550 Sant Joan d’Alacant, Alicante, Spain
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 com-plexity of oral bioavailability and the limited experimental data in public domain have mainly restricted the development of reliable in-silico models to predict this property from the chemi-cal structure. In this study we present a KNIME automated workflow to predict human oral bio-availability 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 bioavailability 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.
Journal of Chemical Information and Modeling ( https://pubs.acs.org/doi/10.1021/acs.jcim.0c00019)
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KNIME WORKFLOW : Available at https://pikairos.eu/download/HOB-classification (445 Mbytes)
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Workflow Available under the Revised BSD License
Copyright (c) 2020, Gabriela Falcón-Cano, Christophe Molina, Miguel Ángel Cabrera-Pérez & Pikaïros.
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