Report title: Predicting drugs based on deep learning model-binding affinity of the target
Reporter: Wang Kaili
Report time:March 11, 2023 19:30-20:00
Reporting location: New Campus Information Building416
Report Summary:Drug-Prediction of target binding affinity is an important issue in drug development，With the widespread application of artificial intelligence in drug discovery，People have tested various deep learning models and tried to improve the prediction accuracy of drug-target binding affinity。To better explore long-range and short-range interactions in drug-targets，Proposed a multi-frame deep learning model that combines traditional seabet online sports betting convolution and atrous convolution。In this constructed model，The binding pocket of the target protein is used as a local feature to predict the binding affinity between drug and target for the first time。The results show，Compared to other deep learning models,DeepDTAF has higher prediction accuracy。In addition，It is of great significance in drug prediction for Alzheimer’s disease and human immunodeficiency diseases。
Introduction to Dr. Wang Kaili:
seabet casino review Wang Kaili, Central South University2019 PhD student，Instructor Professor Li Min。Research direction: bioinformatics，Deep Learning，Drug target interaction prediction。He is currently the first author of 1 paper published in Bioinformatics,Briefings in Bioinformatics published 2 papers。 In addition，2 papers under review，1 article in preparation。
