Predicting the fraction unbound of a drug in plasma plays a significant role in understanding its pharmacokinetic properties during in vitro studies of drug design and discovery. Owing to the gaining reliability of machine learning in biological predictive models and development of automated machine learning techniques for the ease of nonexperts of machine learning to optimize and maximize the reliability of the model, in this experiment, we built an in silico prediction model of a fraction unbound drug in human plasma using a chemical fingerprint and a freely available AutoML framework. The predictive model was trained on one of the largest data sets ever of 5471 experimental values using four different AutoML frameworks to compare their performance on this problem and to choose the most significant one. With a coefficient of determination of 0.85 on the test data set, our best prediction model showed better performance than other previously published models, giving our model significant importance in pharmacokinetic modeling.

中文翻译：

---

使用自动机器学习从化学指纹对人体血浆中未结合的部分进行计算机模拟预测

在药物设计和发现的体外研究过程中，预测药物血浆中未结合的部分在理解其药代动力学特性方面起着重要作用。由于在生物预测模型中获得机器学习的可靠性以及自动机器学习技术的发展，从而使非机器学习专家可以轻松地优化和最大化模型的可靠性，因此在本实验中，我们建立了一个计算机模拟的计算机模型使用化学指纹和可免费获得的AutoML框架在人血浆中分离未结合的药物。使用四个不同的AutoML框架，在5471个实验值中最大的数据集之一上训练了预测模型，以比较它们在此问题上的性能并选择最重要的一个。确定系数为0。