首页|GCARDTI:Drug-target interaction prediction based on a hybrid mechanism in drug SELFIES
GCARDTI:Drug-target interaction prediction based on a hybrid mechanism in drug SELFIES
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
The prediction of the interaction between a drug and a target is the most critical issue in the fields of drug development and repurposing.However,there are still two challenges in current deep learning research:(i)the structural information of drug molecules is not fully explored in most drug target studies,and the previous drug SMILES does not correspond well to effective drug molecules and(ii)exploration of the potential relationship between drugs and targets is in need of improvement.In this work,we use a new and better representation of the effective molecular graph structure,SELFIES.We propose a hybrid mechanism framework based on convolu-tional neural network and graph attention network to capture multi-view feature information of drug and target molecular structures,and we aim to enhance the ability to capture interaction sites between a drug and a target.In this study,our experiments using two different datasets show that the GCARDTI model outperforms a variety of different model algorithms on different metrics.We also demonstrate the accuracy of our model through two case studies.
NETL
NSTL
万方数据
