Name
Abstract | ||
|---|---|---|
Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs. However, existing studies (i) ignore the essential correlations between atoms when encoding drug compounds and (ii) model the interaction of drug-target pairs simply by concatenation. Based on those observations, in this study, we propose an end-to-end model with multiple attention blocks to predict the binding affinity scores of drug-target pairs. Our proposed model offers the abilities to (i) encode the correlations between atoms by a relation-aware self-attention block and (ii) model the interaction of drug representations and target representations by the multi-head attention block. Experimental results of DTI prediction on two benchmark datasets show our approach outperforms existing methods, which are benefit from the correlation information encoded by the relation-aware self-attention block and the interaction information extracted by the multi-head attention block. Moreover, we conduct the experiments on the effects of max relative position length and find out the best max relative position length value . Furthermore, we apply our model to predict the binding affinity of Corona Virus Disease 2019 (COVID-19)-related genome sequences and FDA-approved drugs. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1093/bib/bbab117 | BRIEFINGS IN BIOINFORMATICS |
Keywords | DocType | Volume |
deep learning, drug-target interaction, self-attention, COVID-19 | Journal | 22 |
Issue | ISSN | Citations |
5 | 1467-5463 | 3 |
PageRank | References | Authors |
0.39 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuni Zeng | 1 | 3 | 0.39 |
| Xiangru Chen | 2 | 7 | 1.89 |
| Yujie Luo | 3 | 3 | 0.39 |
| Xuedong Li | 4 | 3 | 0.39 |
| Dezhong Peng | 5 | 285 | 27.92 |