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: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/23/7/3780 |
| _version_ | 1797439018558816256 |
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| author | Xun Wang Jiali Liu Chaogang Zhang Shudong Wang |
| author_facet | Xun Wang Jiali Liu Chaogang Zhang Shudong Wang |
| author_sort | Xun Wang |
| collection | DOAJ |
| description | 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, more and more researchers have developed CPI’s deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> (degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> = 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet. |
| first_indexed | 2024-03-09T11:46:46Z |
| format | Article |
| id | doaj.art-b3f5b8fba9494bb9b8f5623dd0134686 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T11:46:46Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-b3f5b8fba9494bb9b8f5623dd01346862023-11-30T23:22:01ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-03-01237378010.3390/ijms23073780SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep LearningXun Wang0Jiali Liu1Chaogang Zhang2Shudong Wang3College of Computer Science and Technology, China University of Petroleum, Qingdao 266555, ChinaCollege of Computer Science and Technology, China University of Petroleum, Qingdao 266555, ChinaCollege of Computer Science and Technology, China University of Petroleum, Qingdao 266555, ChinaCollege of Computer Science and Technology, China University of Petroleum, Qingdao 266555, ChinaIdentifying 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, more and more researchers have developed CPI’s deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> (degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> = 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet.https://www.mdpi.com/1422-0067/23/7/3780deep learningcompound-protein interactionscompound propertiesprotein prepertiesIC50 value |
| spellingShingle | Xun Wang Jiali Liu Chaogang Zhang Shudong Wang SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning International Journal of Molecular Sciences deep learning compound-protein interactions compound properties protein preperties IC50 value |
| title | SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning |
| title_full | SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning |
| title_fullStr | SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning |
| title_full_unstemmed | SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning |
| title_short | SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning |
| title_sort | ssgraphcpi a novel model for predicting compound protein interactions based on deep learning |
| topic | deep learning compound-protein interactions compound properties protein preperties IC50 value |
| url | https://www.mdpi.com/1422-0067/23/7/3780 |
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