Deep-Learning-Based Drug–Target Interaction Prediction

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yong‐Huan; Lü, Hongmei

doi:10.1021/acs.jproteome.6b00618

Cited by 421 publications

(262 citation statements)

References 37 publications

(49 reference statements)

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“…These results demonstrate the effectiveness of using our newly proposed features for prediction of drug targets. Furthermore, our classifier outperforms other drug-target prediction methods published in recent years [12,19,31,32] (Fig. 4d), achieving favorable performance with a highly imbalanced dataset.…”

Section: A Novel Training Scheme Prevents Overfitting and Solves Thementioning

confidence: 68%

Revealing new therapeutic opportunities through drug target prediction via class imbalance-tolerant machine learning

Liang

2019

Preprint

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In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug-gene phenotype similarity and gene expression profile similarity, that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that target unexplored genes. By providing newly predicted drug-target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes.

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Section: A Novel Training Scheme Prevents Overfitting and Solves Thementioning

confidence: 68%

Revealing new therapeutic opportunities through drug target prediction via class imbalance-tolerant machine learning

Liang

2019

Preprint

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“…Recently, deep learning has been applying to drug discovery [20], [21]. It has achieved superior performance compared to traditional machine learning techniques in many problems in drug development such as drug visual screening [22], [23], drug-target profiling [24], [25], [26], [27], drug repositioning [28], [29]. Especially in the drug response problem, deep learning is utilized to automatically learn genomic features of cell lines and the structural features of drugs to predict anticancer drug responsiveness [30], [31], [32], [33].…”

Section: Introductionmentioning

confidence: 99%

Graph convolutional networks for drug response prediction

Nguyen¹,

Nguyen

Le³

2020

Preprint

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Background: Drug response prediction is an important problem in computational personalized medicine. Many machine learning-, especially deep learning-, based methods have been proposed for this task. However, these methods often represented the drugs as strings, which are not a natural way to depict molecules. Also, interpretation has not been considered thoroughly in these methods. Methods: In this study, we propose a novel method, GraphDRP, based on graph convolutional network for the problem. In GraphDRP, drugs are represented in molecular graphs directly capturing the bonds among atoms, meanwhile cell lines are depicted as binary vectors of genomic aberrations. Representative features of drugs and cell lines are learned by convolution layers, then combined to represent for each drug-cell line pair. Finally, the response value of each drug-cell line pair is predicted by a fully-connected neural network. Four variants of graph convolutional networks are used for learning the features of drugs. Results: We find that GraphDRP outperforms tCNN in all performance measures for all experiments. Also, through saliency maps of the resulting GraphDRP models, we discover the contribution of the genomic aberrations to the responses. Conclusion: Representing drugs as graphs are able to improve the performance of drug response prediction. Data and source code can be downloaded at https://github.com/hauldhut/GraphDRP.

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“…For example, Wen et al [17] extracted drug and target features from their chemical substructure and sequence information, and used deep belief network (DBN) to predict potential DTIs.…”

Section: Introductionmentioning

confidence: 99%

RFDTI: Using Rotation Forest with Feature Weighted for Drug-Target Interaction Prediction from Drug Molecular Structure and Protein Sequence

Wang

You

et al. 2020

Preprint

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The identification and prediction of Drug-Target Interactions (DTIs) is the basis for screening drug candidates, which plays a vital role in the development of innovative drugs. However, due to the time-consuming and high cost constraints of biological experimental methods, traditional drug target identification technologies are often difficult to develop on a large scale. Therefore, in silico methods are urgently needed to predict drug-target interactions in a genome-wide manner. In this article, we design a new in silico approach, named RFDTI to predict the DTIs combine Feature weighted Rotation Forest (FwRF) classifier with protein amino acids information. This model has two outstanding advantages: a) using the fusion data of protein sequence and drug molecular fingerprint, which can fully carry information; b) using the classifier with feature selection ability, which can effectively remove noise information and improve prediction performance. More specifically, we first use Position-Specific Score Matrix (PSSM) to numerically convert protein sequences and utilize Pseudo Position-Specific Score Matrix (PsePSSM) to extract their features.Then a unified digital descriptor is formed by combining molecular fingerprints representing drug information. Finally, the FwRF is applied to implement on Enzyme, Ion Channel, GPCR, and Nuclear Receptor data sets. The results of the five-fold cross-validation experiment show that the prediction accuracy of this approach reaches 91.68%, 88.11%, 84.72% and 78.33% on four benchmark data sets, respectively. To further validate the performance of the RFDTI, we compare it with other excellent methods and Support Vector Machine (SVM) model. In addition, 7 of the 10 highest predictive scores in predicting novel DTIs were validated by relevant databases. The experimental results of cross-validation indicated that RFDTI is feasible in predicting the relationship among drugs and target, and can provide help for the discovery of new candidate drugs.

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Deep-Learning-Based Drug–Target Interaction Prediction

Cited by 421 publications

References 37 publications

Revealing new therapeutic opportunities through drug target prediction via class imbalance-tolerant machine learning

Revealing new therapeutic opportunities through drug target prediction via class imbalance-tolerant machine learning

Graph convolutional networks for drug response prediction

RFDTI: Using Rotation Forest with Feature Weighted for Drug-Target Interaction Prediction from Drug Molecular Structure and Protein Sequence

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