MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism-Reference-Cited by-同舟云学术

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MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism

Published:2024-08-13 Issue:16 Volume:64 Page:6684-6698
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Peng Lihong¹^ORCID,Liu Xin¹^ORCID,Chen Min²,Liao Wen³,Mao Jiale³,Zhou Liqian¹

Affiliation:

1. College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, Hunan 412007, China

2. School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang, Hunan 421002, China

3. School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan 412007, China

Funder

Natural Science Foundation of Hunan Province

Education Department of Hunan Province

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00957

Reference71 articles.

1. RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information

2. A comparative analysis of Drug-Target Interaction detection approaches using Ensemble of Machine learning models

3. BINDTI: A bi-directional Intention network for drug-target interaction identification based on attention mechanisms

4. Drug–target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism

5. A comparison of embedding aggregation strategies in drug–target interaction prediction

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