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DeepRank-GNN: A Graph Neural Network Framework to Learn Patterns in
Compound–protein interaction prediction with end-to-end learning of
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Figure 4 from An inductive graph neural network model for compound
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GraphCPI: Graph Neural Representation Learning for Compound-Protein
Accurately predicting compound-protein interactions (CPIs) is of great help to increase the efficiency and reduce costs in drug development. Most of existing machine learning models for CPI prediction often represent compounds and proteins in one-dimensional strings, or use the descriptor-based methods. These models might ignore the fact that molecules are essentially structured by the ...
PDF GraphCPI: Graph Neural Representation Learning for Compound-Protein
latent representation for compound and protein, respectively. After that, we further feed the concatenation of two latent representations into a stack of fully connected layers, and finally GraphCPI outputs a binary value for the compound-protein pair (1 means interaction, and 0 means otherwise). B. Graph Representation for Compounds
PDF GraphCPI: Graph Neural Representation Learning for Compound-Protein
the graph neural representation, which differs our framework from the existing deep learning methods such as DeepCPI [8]. In a nutshell, the main contributions of this paper are as follows: We propose a framework that incorporates the advanced graph neural representation for compound and pre-trained embedding techniques for protein sequences ...
GraphCPI: Graph Neural Representation Learning for Compound-Protein
Request PDF | On Nov 1, 2019, Zhe Quan and others published GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction | Find, read and cite all the research you need on ...
GraphCPI: Graph Neural Representation Learning for Compound-Protein
An end-to-end deep learning framework called GraphCPI, which captures the structural information of compounds and leverages the chemical context of protein sequences for solving the CPI prediction task, comparing against classic and state-of-the-art methods. Accurately predicting compound-protein interactions (CPIs) is of great help to increase the efficiency and reduce costs in drug development.
PDF Effectively Identifying Compound-Protein Interaction using Graph Neural
Effectively Identifying Compound-Protein Interaction using Graph Neural Representation. Abstract—Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein ...
Effectively Identifying Compound-Protein Interaction using Graph Neural
Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein strings and/or the specific descriptors. However, they often ignore the fact that molecules are ...
GraphCPI: Graph Neural Representation Learning for Compound-Protein
An end-to-end deep learning framework named GraphCPI, which captures the structural information of compounds and leverages the chemical context of protein sequences for solving the CPI prediction task and can integrate any popular graph neural networks for learning compounds, and it combines with a convolutional neural network for embedding sequences.
GraphCPIs: A novel graph-based computational model for potential
Wu et al. 15 constructed a learnable drug-protein interaction network by using a graph neural network to dig up the network-level representation from compounds and amino acids. A unified framework was established by Ye et al. 16 based on a knowledge graph and recommendation system.
Effectively Identifying Compound-Protein Interaction Using Graph Neural
Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. ... we propose an end-to-end deep learning framework named <italic>GraphCPI</italic>, which captures the structural information of compounds and leverages the chemical context of protein sequences for ...
GraphCPI: Graph Neural Representation Learning for Compound-Protein
Accurately predicting compound-protein interactions (CPIs) is of great help to increase the efficiency and reduce costs in drug development. Most of existing machine learning models for CPI prediction often represent compounds and proteins in one-dimensional strings, or use the descriptor-based methods. These models might ignore the fact that molecules are essentially structured by the ...
Effectively Identifying Compound-Protein Interaction using Graph Neural
Effectively Identifying Compound-Protein Interaction using Graph Neural Representation - XuanLin1991/GraphCPI. ... Learning Pathways White papers, Ebooks, Webinars Customer Stories Partners Open Source GitHub Sponsors. Fund open source developers ...
Effectively Identifying Compound-Protein Interaction Using Graph Neural
Abstract. Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI ...
PDF Effectively Identifying Compound-Protein Interaction Using Graph Neural
Effectively Identifying Compound-Protein Interaction Using Graph Neural Representation. Abstract—Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein ...
SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions
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.
Effectively Identifying Compound-Protein Interaction Using Graph Neural
Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein strings and/or the specific descriptors. However, they often ignore the fact that molecules are ...
An inductive graph neural network model for compound-protein
Identifying the potential compound-protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most …
Effectively Identifying Compound-Protein Interaction Using Graph Neural
An end-to-end deep learning framework named GraphCPI, which captures the structural information of compounds and leverages the chemical context of protein sequences for solving the CPI prediction task and can integrate any popular graph neural networks for learning compounds, and it combines with a convolutional neural network for embedding sequences. Effectively identifying compound-protein ...
KGNN
Graphcpi: Graph neural representation learning for compound-protein interaction. In BIBM, pages 717-722, 2019. Google Scholar; Zhe Quan, Zhi-Jie Wang, Yuquan Le, Bin Yao, Kenli Li, and Jian Yin. An efficient framework for sentence similarity modeling. TASLP, 27(4):853-865, 2019.
GraphCPI: Graph Neural Representation Learning for Compound-Protein
DOI: 10.1109/BIBM47256.2019.8983267 Corpus ID: 211059646; GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction @article{Quan2019GraphCPIGN, title={GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction}, author={Zhe Quan and Yan Guo and Xuan Lin and Zhi-Jie Wang and Xiangxiang Zeng}, journal={2019 IEEE International Conference on ...
Xuan Lin
Zhe Quan, Yan Guo, Xuan Lin, Zhi-Jie Wang*, Xiangxiang Zeng, "GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction," 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 717-722, 2019.
GNNGL-PPI: multi-category prediction of protein-protein interactions
Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional ...
Compound-protein interaction prediction with end-to-end learning of
Motivation: In bioinformatics, machine learning-based methods that predict the compound-protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently, end-to-end representation learning for discrete symbolic data (e.g. words in natural language processing) using deep neural networks has demonstrated excellent performance on various difficult problems.
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Accurately predicting compound-protein interactions (CPIs) is of great help to increase the efficiency and reduce costs in drug development. Most of existing machine learning models for CPI prediction often represent compounds and proteins in one-dimensional strings, or use the descriptor-based methods. These models might ignore the fact that molecules are essentially structured by the ...
latent representation for compound and protein, respectively. After that, we further feed the concatenation of two latent representations into a stack of fully connected layers, and finally GraphCPI outputs a binary value for the compound-protein pair (1 means interaction, and 0 means otherwise). B. Graph Representation for Compounds
the graph neural representation, which differs our framework from the existing deep learning methods such as DeepCPI [8]. In a nutshell, the main contributions of this paper are as follows: We propose a framework that incorporates the advanced graph neural representation for compound and pre-trained embedding techniques for protein sequences ...
Request PDF | On Nov 1, 2019, Zhe Quan and others published GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction | Find, read and cite all the research you need on ...
An end-to-end deep learning framework called GraphCPI, which captures the structural information of compounds and leverages the chemical context of protein sequences for solving the CPI prediction task, comparing against classic and state-of-the-art methods. Accurately predicting compound-protein interactions (CPIs) is of great help to increase the efficiency and reduce costs in drug development.
Effectively Identifying Compound-Protein Interaction using Graph Neural Representation. Abstract—Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein ...
Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein strings and/or the specific descriptors. However, they often ignore the fact that molecules are ...
An end-to-end deep learning framework named GraphCPI, which captures the structural information of compounds and leverages the chemical context of protein sequences for solving the CPI prediction task and can integrate any popular graph neural networks for learning compounds, and it combines with a convolutional neural network for embedding sequences.
Wu et al. 15 constructed a learnable drug-protein interaction network by using a graph neural network to dig up the network-level representation from compounds and amino acids. A unified framework was established by Ye et al. 16 based on a knowledge graph and recommendation system.
Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. ... we propose an end-to-end deep learning framework named <italic>GraphCPI</italic>, which captures the structural information of compounds and leverages the chemical context of protein sequences for ...
Accurately predicting compound-protein interactions (CPIs) is of great help to increase the efficiency and reduce costs in drug development. Most of existing machine learning models for CPI prediction often represent compounds and proteins in one-dimensional strings, or use the descriptor-based methods. These models might ignore the fact that molecules are essentially structured by the ...
Effectively Identifying Compound-Protein Interaction using Graph Neural Representation - XuanLin1991/GraphCPI. ... Learning Pathways White papers, Ebooks, Webinars Customer Stories Partners Open Source GitHub Sponsors. Fund open source developers ...
Abstract. Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI ...
Effectively Identifying Compound-Protein Interaction Using Graph Neural Representation. Abstract—Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein ...
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.
Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein strings and/or the specific descriptors. However, they often ignore the fact that molecules are ...
Identifying the potential compound-protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most …
An end-to-end deep learning framework named GraphCPI, which captures the structural information of compounds and leverages the chemical context of protein sequences for solving the CPI prediction task and can integrate any popular graph neural networks for learning compounds, and it combines with a convolutional neural network for embedding sequences. Effectively identifying compound-protein ...
Graphcpi: Graph neural representation learning for compound-protein interaction. In BIBM, pages 717-722, 2019. Google Scholar; Zhe Quan, Zhi-Jie Wang, Yuquan Le, Bin Yao, Kenli Li, and Jian Yin. An efficient framework for sentence similarity modeling. TASLP, 27(4):853-865, 2019.
DOI: 10.1109/BIBM47256.2019.8983267 Corpus ID: 211059646; GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction @article{Quan2019GraphCPIGN, title={GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction}, author={Zhe Quan and Yan Guo and Xuan Lin and Zhi-Jie Wang and Xiangxiang Zeng}, journal={2019 IEEE International Conference on ...
Zhe Quan, Yan Guo, Xuan Lin, Zhi-Jie Wang*, Xiangxiang Zeng, "GraphCPI: Graph Neural Representation Learning for Compound-Protein Interaction," 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 717-722, 2019.
Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional ...
Motivation: In bioinformatics, machine learning-based methods that predict the compound-protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently, end-to-end representation learning for discrete symbolic data (e.g. words in natural language processing) using deep neural networks has demonstrated excellent performance on various difficult problems.