SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning
Identifying compound-protein (drug-target, DTI) interactions (CPI) accurately is a key step in drug discovery. Including virtual screening and drug reuse, it can significantly reduce the time it takes to identify drug candidates and provide patients with timely and effective treatment. Recently, mor...
Main Authors: | Xun Wang, Jiali Liu, Chaogang Zhang, Shudong Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-03-01
|
Series: | International Journal of Molecular Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/1422-0067/23/7/3780 |
Similar Items
-
MCN-CPI: Multiscale Convolutional Network for Compound–Protein Interaction Prediction
by: Shuang Wang, et al.
Published: (2021-07-01) -
PMF-CPI: assessing drug selectivity with a pretrained multi-functional model for compound–protein interactions
by: Nan Song, et al.
Published: (2023-10-01) -
CAT-CPI: Combining CNN and transformer to learn compound image features for predicting compound-protein interactions
by: Ying Qian, et al.
Published: (2022-09-01) -
An overview of recent advances and challenges in predicting compound-protein interaction (CPI)
by: Li Yanbei, et al.
Published: (2023-12-01) -
Evaluation of antioxidant activity and phenolic compounds content in methanol extract obtained from leaves Commiphora Myrrha
by: Celia Eliane de Lara da Silva, et al.
Published: (2013-09-01)