DTI-RCNN: New Efficient Hybrid Neural Network Model to Predict Drug–Target Interactions

Zheng, Xiaoping; He, Song; Song, Xinyu; Zhang, Zhongnan; Bo, Xiaochen

doi:10.1007/978-3-030-01418-6_11

Cited by 17 publications

(4 citation statements)

References 15 publications

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“…(4) ensures that (3) In addition to F e→v , the function F v→v have be introduced to the atom update method, which aggregates the hidden information of the adjacent atoms to a specified atom v as shown in Eq. (5). Then the atom aggregated representation h l−1 v→v is computed by the update Eq.…”

Section: Undirected-cmpnn For Molecular Representationmentioning

confidence: 99%

“…The recurrent neural network (RNN) is an effective method for extracting protein and molecular features from sequential data. Zheng et al [5] extracted potential semantic information between protein and molecule through Long Short-Term Memory Network (LSTM), a special recurrent neural network, for drugtarget interaction (DTI) prediction. DeepH-DTA [6] used a bidirectional ConvLSTM [7] to model spatial sequence information on SMILES data.…”

Section: Introductionmentioning

confidence: 99%

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Drug-target binding affinity prediction using message passing neural network and self supervised learning

Xia,

Xu,

Pan

et al. 2023

BMC Genomics

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Background Drug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much knowledge of the biochemical background. However, there are still room for improvement in DTA prediction: (1) only focusing on the information of the atom leads to an incomplete representation of the molecular graph; (2) the self-supervised learning method could be introduced for protein representation. Results In this paper, a DTA prediction model using the deep learning method is proposed, which uses an undirected-CMPNN for molecular embedding and combines CPCProt and MLM models for protein embedding. An attention mechanism is introduced to discover the important part of the protein sequence. The proposed method is evaluated on the datasets Ki and Davis, and the model outperformed other deep learning methods. Conclusions The proposed model improves the performance of the DTA prediction, which provides a novel strategy for deep learning-based virtual screening methods.

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Section: Undirected-cmpnn For Molecular Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Drug-target binding affinity prediction using message passing neural network and self supervised learning

Xia,

Xu,

Pan

et al. 2023

BMC Genomics

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“…With continuous breakthroughs in natural language processing technologies in recent years, deep-learning models that rely on linear sequence inputs rather than 3D structures have emerged. The DTI-RCNN ( Zheng et al 2018 ) employs a combination of long short-term memory network and CNN methods for affinity prediction.…”

Section: Introductionmentioning

confidence: 99%

DEAttentionDTA: protein–ligand binding affinity prediction based on dynamic embedding and self-attention

Chen,

Huang,

Shen

et al. 2024

Bioinformatics

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Motivation Predicting protein–ligand binding affinity is crucial in new drug discovery and development. However, most existing models rely on acquiring 3D structures of elusive proteins. Combining amino acid sequences with ligand sequences and better highlighting active sites are also significant challenges. Results We propose an innovative neural network model called DEAttentionDTA, based on dynamic word embeddings and a self-attention mechanism, for predicting protein–ligand binding affinity. DEAttentionDTA takes the 1D sequence information of proteins as input, including the global sequence features of amino acids, local features of the active pocket site, and linear representation information of the ligand molecule in the SMILE format. These three linear sequences are fed into a dynamic word-embedding layer based on a 1D convolutional neural network for embedding encoding and are correlated through a self-attention mechanism. The output affinity prediction values are generated using a linear layer. We compared DEAttentionDTA with various mainstream tools and achieved significantly superior results on the same dataset. We then assessed the performance of this model in the p38 protein family. Availability and implementation The resource codes are available at https://github.com/whatamazing1/DEAttentionDTA.

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“…TransformerCPI [8] predicted compound-protein interaction (CPI) by a transformer model with self-attention mechanism. DTI-RCNN [9] integrated LSTM and CNN into the model, where drug and gene data were input into LSTM to extract the features respectively, and then concatenated the two features and sent it to the CNN network for DTI prediction. Wang et al [10] proposed a novel model that combined protein features and drug molecular fingerprint information, and sent features into a rotation forest classifier to predict DTI.…”

Section: Introductionmentioning

confidence: 99%

GLSTM-DTA: Application of Prediction Improvement Model Based on GNN and LSTM

Liang

Jiang

Min

et al. 2022

J. Phys.: Conf. Ser.

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Most prediction models of drug-target binding affinity (DTA) treated drugs and targets as sequences, and feature extraction networks could not sufficiently extract features. Inspired by DeepDTA and GraphDTA, we proposed an improved model named GLSTM-DTA for DTA prediction, which combined Graph Neural Network (GNN) and Long Short-Term Memory Network (LSTM). The feature extraction block consists of two parts: GNN block and LSTM block, which extract drug features and protein features respectively. The novelty of our work is using LSTM, instead of Convolutional neural network (CNN) to extract protein sequence features, which is facilitating to capture long-term dependencies in sequence. In addition, we also converted drugs into graph structures and use GNN for feature extraction. The improved model performs better than DeepDTA and GraphDTA. The comprehensive results prove the advantages of our model in accurately predicting the binding affinity of drug-targets.

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DTI-RCNN: New Efficient Hybrid Neural Network Model to Predict Drug–Target Interactions

Cited by 17 publications

References 15 publications

Drug-target binding affinity prediction using message passing neural network and self supervised learning

Drug-target binding affinity prediction using message passing neural network and self supervised learning

DEAttentionDTA: protein–ligand binding affinity prediction based on dynamic embedding and self-attention

GLSTM-DTA: Application of Prediction Improvement Model Based on GNN and LSTM

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