基于机器学习的药物血浆蛋白结合率的预测

付洺宇; 朱一阳; 吴春勇; 侯凤贞; 关媛

doi:10.11665/j.issn.1000-5048.20210607

基于机器学习的药物血浆蛋白结合率的预测

1.
中国药科大学理学院，南京 211198
2.
中国药科大学药物分析系，南京 211198

基金项目: 国家自然科学基金资助项目（No.82074128）；“双一流”学科创新团队资助项目（No.CPU2018GY30）

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出版历程
- 收稿日期: 2021-03-03
- 修回日期: 2021-11-08
- 刊出日期: 2021-12-24

Prediction of plasma protein binding rate based on machine learning

1.
School of Science, China Pharmaceutical University, Nanjing 211198
2.
Department of Pharmaceutical Analysis, China Pharmaceutical University, Nanjing 211198, China

Funds: This study was supported by the National Natural Science Foundation of China (No.82074128), and the Program for Innovation Team of the "Double-First Class" Disciplines (No.CPU2018GY30)

摘要

摘要: 预测药物在血浆中的蛋白结合率，有助于了解药物的药代动力学特征，对药物发现的早期研究有重要的参考价值。本研究收集了2 452个临床药物的血浆蛋白结合率信息，用Molecular Operating Environment（MOE）和Mordred两种软件计算分子描述符，将算得的分子描述符作为模型的输入特征。使用极端梯度提升（extreme gradient boosting， XGBoost）算法和随机森林（random forest，RF）算法构建机器学习模型。结果表明，与MOE相比，将Mordred计算的分子描述符作为模型的输入，构建的模型预测性能更优。使用XGBoost算法和RF算法构建模型的预测性能结果相近，最优模型的R²均为0.715。此外，根据研究结果得出药物血浆蛋白结合率与药物分子的一些理化性质参数，如水溶性，辛醇/水分配系数以及共轭双键密切相关。通过这些参数预测药物血浆蛋白结合率具有方便快捷的优点，可以为相关药代动力学研究提供参考依据。
- 血浆蛋白结合率 /
- 机器学习 /
- XGBoost算法 /
- RF算法 /
- 药物发现
Abstract: Predicting the protein binding rate of drugs in plasma is helpful to us in understanding the pharmacokinetic characteristics of drugs, with much value of reference for early research on drug discovery. In this study, plasma protein binding rate information of 2 452 clinical drugs were collected.Two pieces of software, Molecular Operating Environment (MOE) and Mordred, were used to calculate molecular descriptors, which were used as input features of the model.Extreme gradient boosting (XGBoost) algorithm and random forest (RF) algorithm were then used to build a machine learning model.The results showed that, compared with MOE, the prediction performance of the constructed model was better using the molecular descriptor calculated by Mordred as the input of the model.The prediction performance results of the model constructed using the XGBoost algorithm and the RF algorithm were similar, and the R2 of the optimal model were both 0.715.According to the research results, it can be concluded that the drug plasma protein binding rate is closely related to some physical and chemical properties of the drug molecule, such as water solubility, octanol/water partition coefficient and conjugated double bonds.Using these parameters to predict the plasma protein binding rate of drugs has the advantages of convenience and efficiency, which can provide reference for related pharmacokinetic studies.
- plasma protein binding rate /
- machine learning /
- XGBoost algorithm /
- RF algorithm /
- drug discovery

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文章访问数: 311
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基于机器学习的药物血浆蛋白结合率的预测

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Prediction of plasma protein binding rate based on machine learning

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