A novel drug-drug interactions prediction method based on a graph attention network
With the increasing need for public health and drug development, combination therapy has become widely used in clinical settings. However, the risk of unanticipated adverse effects and unknown toxicity caused by drug-drug interactions (DDIs) is a serious public health issue for polypharmacy safety....
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2023-08-01
|
| Series: | Electronic Research Archive |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2023286?viewType=HTML |
| _version_ | 1797657936953081856 |
|---|---|
| author | Xian Tan Shijie Fan Kaiwen Duan Mengyue Xu Jingbo Zhang Pingping Sun Zhiqiang Ma |
| author_facet | Xian Tan Shijie Fan Kaiwen Duan Mengyue Xu Jingbo Zhang Pingping Sun Zhiqiang Ma |
| author_sort | Xian Tan |
| collection | DOAJ |
| description | With the increasing need for public health and drug development, combination therapy has become widely used in clinical settings. However, the risk of unanticipated adverse effects and unknown toxicity caused by drug-drug interactions (DDIs) is a serious public health issue for polypharmacy safety. Traditional experimental methods for detecting DDIs are expensive and time-consuming. Therefore, many computational methods have been developed in recent years to predict DDIs with the growing availability of data and advancements in artificial intelligence. In silico methods have proven to be effective in predicting DDIs, but detecting potential interactions, especially for newly discovered drugs without an existing DDI network, remains a challenge. In this study, we propose a predicting method of DDIs named HAG-DDI based on graph attention networks. We consider the differences in mechanisms between DDIs and add learning of semantic-level attention, which can focus on advanced representations of DDIs. By treating interactions as nodes and the presence of the same drug as edges, and constructing small subnetworks during training, we effectively mitigate potential bias issues arising from limited data availability. Our experimental results show that our method achieves an F1-score of 0.952, proving that our model is a viable alternative for DDIs prediction. The codes are available at: https://github.com/xtnenu/DDIFramework. |
| first_indexed | 2024-03-11T17:51:44Z |
| format | Article |
| id | doaj.art-31af1ad513fb4b8fba33fce6637c42e3 |
| institution | Directory Open Access Journal |
| issn | 2688-1594 |
| language | English |
| last_indexed | 2024-03-11T17:51:44Z |
| publishDate | 2023-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj.art-31af1ad513fb4b8fba33fce6637c42e32023-10-18T01:53:15ZengAIMS PressElectronic Research Archive2688-15942023-08-013195632564810.3934/era.2023286A novel drug-drug interactions prediction method based on a graph attention networkXian Tan0Shijie Fan 1Kaiwen Duan2Mengyue Xu 3Jingbo Zhang4Pingping Sun 5Zhiqiang Ma61. School of Information Science and Technology, Northeast Normal University, Changchun, China1. School of Information Science and Technology, Northeast Normal University, Changchun, China1. School of Information Science and Technology, Northeast Normal University, Changchun, China1. School of Information Science and Technology, Northeast Normal University, Changchun, China1. School of Information Science and Technology, Northeast Normal University, Changchun, China1. School of Information Science and Technology, Northeast Normal University, Changchun, China2. School of Sciences Changchun Humanities and Sciences College, Changchun, ChinaWith the increasing need for public health and drug development, combination therapy has become widely used in clinical settings. However, the risk of unanticipated adverse effects and unknown toxicity caused by drug-drug interactions (DDIs) is a serious public health issue for polypharmacy safety. Traditional experimental methods for detecting DDIs are expensive and time-consuming. Therefore, many computational methods have been developed in recent years to predict DDIs with the growing availability of data and advancements in artificial intelligence. In silico methods have proven to be effective in predicting DDIs, but detecting potential interactions, especially for newly discovered drugs without an existing DDI network, remains a challenge. In this study, we propose a predicting method of DDIs named HAG-DDI based on graph attention networks. We consider the differences in mechanisms between DDIs and add learning of semantic-level attention, which can focus on advanced representations of DDIs. By treating interactions as nodes and the presence of the same drug as edges, and constructing small subnetworks during training, we effectively mitigate potential bias issues arising from limited data availability. Our experimental results show that our method achieves an F1-score of 0.952, proving that our model is a viable alternative for DDIs prediction. The codes are available at: https://github.com/xtnenu/DDIFramework.https://www.aimspress.com/article/doi/10.3934/era.2023286?viewType=HTMLdrug-drug interactiongraph attention networkmachine learninggraph embeddingcomputational biology |
| spellingShingle | Xian Tan Shijie Fan Kaiwen Duan Mengyue Xu Jingbo Zhang Pingping Sun Zhiqiang Ma A novel drug-drug interactions prediction method based on a graph attention network Electronic Research Archive drug-drug interaction graph attention network machine learning graph embedding computational biology |
| title | A novel drug-drug interactions prediction method based on a graph attention network |
| title_full | A novel drug-drug interactions prediction method based on a graph attention network |
| title_fullStr | A novel drug-drug interactions prediction method based on a graph attention network |
| title_full_unstemmed | A novel drug-drug interactions prediction method based on a graph attention network |
| title_short | A novel drug-drug interactions prediction method based on a graph attention network |
| title_sort | novel drug drug interactions prediction method based on a graph attention network |
| topic | drug-drug interaction graph attention network machine learning graph embedding computational biology |
| url | https://www.aimspress.com/article/doi/10.3934/era.2023286?viewType=HTML |
| work_keys_str_mv | AT xiantan anoveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT shijiefan anoveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT kaiwenduan anoveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT mengyuexu anoveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT jingbozhang anoveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT pingpingsun anoveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT zhiqiangma anoveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT xiantan noveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT shijiefan noveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT kaiwenduan noveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT mengyuexu noveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT jingbozhang noveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT pingpingsun noveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork AT zhiqiangma noveldrugdruginteractionspredictionmethodbasedonagraphattentionnetwork |