Drug-target continuous binding affinity prediction using multiple sources of information

作者：

Highlights：

• Integrating various sources of information to predict drug-target binding affinity.

• Takes advantages of both similarities and features.

• Gradient boosting regression model performs well in predicting new drug and new.

• Using various sources boost the accuracy of drug-target identification.

摘要

•Integrating various sources of information to predict drug-target binding affinity.•Takes advantages of both similarities and features.•Gradient boosting regression model performs well in predicting new drug and new.•Using various sources boost the accuracy of drug-target identification.

论文关键词：Continuous binding affinity,Drug-target interaction,Binding affinity prediction,Gradient boosting machine,K-nearest neighbor,Combining information

论文评审过程：Received 24 November 2020, Revised 23 July 2021, Accepted 24 August 2021, Available online 28 August 2021, Version of Record 30 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115810