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 the public domain have mainly restricted the development 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 quick and easy prediction of oral bioavailability for new molecules. 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.

中文翻译：

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使用KNIME进行ADME预测：开发和验证可用于预测人类口服生物利用度的公开工作流程。

在计算机模拟中，人类口服生物利用度的预测是用于选择潜在药物候选者以及在药物发现和开发的早期阶段以较小的成功概率拒绝那些分子的相关工具。然而，口服生物利用度的高变异性和复杂性以及公共领域中有限的实验数据主要限制了可靠的计算机模型的开发，以从化学结构预测该性质。在这项研究中，我们提出了一种KNIME自动化工作流程，基于五种组合成整体模型的机器学习方法来预测人类对新药和类药物分子的口服生物利用度。该工作流程可自由访问，并允许快速轻松地预测新分子的口服生物利用度。