Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process. In this study, a total of 1588 drug molecules with HOB data were collected from the literature for the development of a classifying model that uses the consensus predictions of five random forest models. The consensus model shows excellent prediction accuracies on two independent test sets with two cutoffs of 20% and 50% for classification of molecules. The analysis of the importance of the input variables allowed the identification of the main molecular descriptors that affect the HOB class value. The model is available as a web server at www.icdrug.com/ICDrug/ADMET for quick assessment of oral bioavailability for small molecules. The results from this study provide an accurate and easy-to-use tool for screening of drug candidates based on HOB, which may be used to reduce the risk of failure in late stage of drug development.

中文翻译：

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HobPre：准确预测人类口服小分子生物利用度

人体口服生物利用度（HOB）是决定新药在临床试验中命运的关键因素。传统上，HOB 是使用昂贵且耗时的实验测试来测量的。在新药合成之前使用计算模型来评估 HOB 将有利于药物开发过程。在这项研究中，从文献中收集了总共 1588 个具有 HOB 数据的药物分子，用于开发使用五个随机森林模型的共识预测的分类模型。共识模型在两个独立的测试集上显示了出色的预测精度，两个截止值分别为 20% 和 50%，用于分子分类。对输入变量重要性的分析允许识别影响 HOB 类值的主要分子描述符。该模型可在 www.icdrug.com/ICDrug/ADMET 作为网络服务器获得，用于快速评估小分子的口服生物利用度。本研究结果为基于HOB的候选药物筛选提供了一种准确且易于使用的工具，可用于降低药物开发后期失败的风险。