Predicting the fraction unbound in plasma provides a good understanding of the pharmacokinetic properties of a drug to assist candidate selection in the early stages of drug discovery. It is also an effective tool to mitigate the risk of late-stage attrition and to optimize further screening. In this study, we built in silico prediction models of fraction unbound in human plasma with freely available software, aiming specifically to improve the accuracy in the low value ranges. We employed several machine learning techniques and built prediction models trained on the largest ever data set of 2738 experimental values. The classification model showed a high true positive rate of 0.826 for the low fraction unbound class on the test set. The strongly biased distribution of the fraction unbound in plasma was mitigated by a logarithmic transformation in the regression model, leading to improved accuracy at lower values. Overall, our models showed better performance than those of previously published methods, including commercial software. Our prediction tool can be used on its own or integrated into other pharmacokinetic modeling systems.

中文翻译：

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从化学结构预测人体血浆中未结合的分数：在低值范围内提高的准确性

预测血浆中未结合的部分可很好地理解药物的药代动力学特性，以帮助在药物发现的早期阶段进行候选药物的选择。它也是减轻后期磨损风险并优化进一步筛查的有效工具。在这项研究中，我们使用免费提供的软件建立了人体血浆中未结合部分的计算机模拟预测模型，旨在提高低值范围内的准确性。我们采用了几种机器学习技术，并在有史以来最大的2738个实验值数据集上构建了预测模型。分类模型对测试集上的低比例未绑定类别显示0.826的高真实阳性率。通过回归模型中的对数变换，可以减轻血浆中未结合部分的强烈偏向分布，从而在较低值下提高了准确度。总体而言，我们的模型显示出比以前发布的方法（包括商业软件）更好的性能。我们的预测工具可以单独使用，也可以集成到其他药代动力学建模系统中。