Prediction of plasma protein binding rate based on machine learning
10.11665/j.issn.1000-5048.20210607
- VernacularTitle:基于机器学习的药物血浆蛋白结合率的预测
- Author:
Mingyu FU
1
;
Yiyang ZHU
;
Chunyong WU
;
Fengzhen HOU
;
Yuan GUAN
Author Information
1. 中国药科大学理学院
- Publication Type:Journal Article
- Keywords:
plasma protein binding rate;
machine learning;
XGBoost algorithm;
RF algorithm;
drug discovery
- From:
Journal of China Pharmaceutical University
2021;52(6):699-706
- CountryChina
- Language:Chinese
-
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.
