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Docking-based Virtual Screening with Multi-Task Learning

Authors :
Liu, Zijing
Ye, Xianbin
Fang, Xiaomin
Wang, Fan
Wu, Hua
Wang, Haifeng
Publication Year :
2021

Abstract

Machine learning shows great potential in virtual screening for drug discovery. Current efforts on accelerating docking-based virtual screening do not consider using existing data of other previously developed targets. To make use of the knowledge of the other targets and take advantage of the existing data, in this work, we apply multi-task learning to the problem of docking-based virtual screening. With two large docking datasets, the results of extensive experiments show that multi-task learning can achieve better performances on docking score prediction. By learning knowledge across multiple targets, the model trained by multi-task learning shows a better ability to adapt to a new target. Additional empirical study shows that other problems in drug discovery, such as the experimental drug-target affinity prediction, may also benefit from multi-task learning. Our results demonstrate that multi-task learning is a promising machine learning approach for docking-based virtual screening and accelerating the process of drug discovery.<br />Comment: accepted by IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021)

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2111.09502
Document Type :
Working Paper