iDTi-CSsmoteB: Identification of Drug–Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost With Over-Sampling Technique SMOTE

Drug-target interactions play an important role for biomedical drug discovery and development. However, it is expensive and time-consuming to accomplish this task by experimental determination. Therefore, developing computational techniques for drug-target interaction prediction is urgent and has practical significance. In this work, we propose an effective computational model of dual Laplacian graph regularized matrix completion, referred to as DLGRMC briefly, to infer the unknown drug-target interactions. Specifically, DLGRMC transforms the task of drug-target interaction prediction into a matrix completion problem, in which the potential interactions between drugs and targets can be obtained based on the prediction scores after the matrix completion procedure. In DLGRMC, the drug pairwise chemical structure similarities and the target pairwise genomic sequence similarities are fully exploited to serve the matrix completion by using a dual Laplacian graph regularization term; i.e., drugs with similar chemical structure are more likely to have interactions with similar targets and targets with similar genomic sequence similarity are more likely to have interactions with similar drugs. In addition, during the matrix completion process, an indicator matrix with binary values which indicates the indices of the observed drug-target interactions is deployed to preserve the experimental confirmed interactions. Furthermore, we develop an alternative iterative strategy to solve the constrained matrix completion problem based on Augmented Lagrange Multiplier algorithm. We evaluate DLGRMC on five benchmark datasets and the results show that DLGRMC outperforms several state-of-the-art approaches in terms of 10-fold cross validation based AUPR values and PR curves. In addition, case studies also demonstrate that DLGRMC can successfully predict most of the experimental validated drug-target interactions.

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In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences

Scientific Reports ◽

10.1038/s41598-017-10724-0 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 31

Author(s):

Zhengwei Li ◽

Pengyong Han ◽

Zhu-Hong You ◽

Xiao Li ◽

Yusen Zhang ◽

...

Keyword(s):

In Silico ◽

Drug Target ◽

Chemical Structure ◽

Protein Sequences ◽

Interaction Networks ◽

In Silico Prediction ◽

Target Interaction

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Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization

BioMed Research International ◽

10.1155/2021/5599263 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Aizhen Wang ◽

Minhui Wang

Keyword(s):

Matrix Factorization ◽

Drug Target ◽

Chemical Structure ◽

Genomic Sequence ◽

Practical Significance ◽

Factorization Problem ◽

Target Interaction ◽

Interaction Prediction ◽

Gradient Descent Algorithm ◽

Laplacian Graph

Drug-target interactions provide useful information for biomedical drug discovery as well as drug development. However, it is costly and time consuming to find drug-target interactions by experimental methods. As a result, developing computational approaches for this task is necessary and has practical significance. In this study, we establish a novel dual Laplacian graph regularized logistic matrix factorization model for drug-target interaction prediction, referred to as DLGrLMF briefly. Specifically, DLGrLMF regards the task of drug-target interaction prediction as a weighted logistic matrix factorization problem, in which the experimentally validated interactions are allocated with larger weights. Meanwhile, by considering that drugs with similar chemical structure should have interactions with similar targets and targets with similar genomic sequence similarity should in turn have interactions with similar drugs, the drug pairwise chemical structure similarities as well as the target pairwise genomic sequence similarities are fully exploited to serve the matrix factorization problem by using a dual Laplacian graph regularization term. In addition, we design a gradient descent algorithm to solve the resultant optimization problem. Finally, the efficacy of DLGrLMF is validated on various benchmark datasets and the experimental results demonstrate that DLGrLMF performs better than other state-of-the-art methods. Case studies are also conducted to validate that DLGrLMF can successfully predict most of the experimental validated drug-target interactions.

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Development of Peptides that Inhibit Aminoglycoside-Modifying Enzymes and β-Lactamases for Control of Resistant Bacteria

Current Protein and Peptide Science ◽

10.2174/1389203721666200915113630 ◽

2020 ◽

Vol 21 (10) ◽

pp. 1011-1026

Author(s):

Bruna O. Costa ◽

Marlon H. Cardoso ◽

Octávio L. Franco

Keyword(s):

Drug Target ◽

Bacterial Infections ◽

Antimicrobial Agents ◽

Treatment Strategies ◽

Resistance Mechanisms ◽

Resistant Bacteria ◽

Specific Chemical ◽

Target Interaction ◽

Multiresistant Bacteria ◽

Acute Bacterial Infections

: Aminoglycosides and β-lactams are the most commonly used antimicrobial agents in clinical practice. This occurs because they are capable of acting in the treatment of acute bacterial infections. However, the effectiveness of antibiotics has been constantly threatened due to bacterial pathogens producing resistance enzymes. Among them, the aminoglycoside-modifying enzymes (AMEs) and β-lactamase enzymes are the most frequently reported resistance mechanisms. AMEs can inactivate aminoglycosides by adding specific chemical molecules in the compound, whereas β-lactamases hydrolyze the β-lactams ring, preventing drug-target interaction. Thus, these enzymes provide a scenario of multidrug-resistance and a significant threat to public health at a global level. In response to this challenge, in recent decades, several studies have focused on the development of inhibitors that can restore aminoglycosides and β-lactams activity. In this context, peptides appear as a promising approach in the field of inhibitors for future antibacterial therapies, as multiresistant bacteria may be susceptible to these molecules. Therefore, this review focused on the most recent findings related to peptide-based inhibitors that act on AMEs and β-lactamases, and how these molecules could be used for future treatment strategies.

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Entropy Model for Multiplex Drug-Target Interaction Endpoints of Drug Immunotoxicity

Current Topics in Medicinal Chemistry ◽

10.2174/15680266113139990114 ◽

2013 ◽

Vol 13 (14) ◽

pp. 1636-1649 ◽

Cited By ~ 24

Author(s):

Esvieta Tenorio-Borroto ◽

Xerardo Garcia-Mera ◽

Claudia Penuelas-Rivas ◽

Juan Vasquez-Chagoyan ◽

Francisco Prado-Prado ◽

...

Keyword(s):

Drug Target ◽

Entropy Model ◽

Target Interaction

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Some Remarks on Prediction of Drug-Target Interaction with Network Models

Current Topics in Medicinal Chemistry ◽

10.2174/1568026617666170414145015 ◽

2017 ◽

Vol 17 (21) ◽

Cited By ~ 2

Author(s):

Shao-Wu Zhang ◽

Xiao-Ying Yan

Keyword(s):

Drug Target ◽

Network Models ◽

Target Interaction

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Prediction of Nitration Sites Based on FCBF Method and Stacking Ensemble Model

Current Proteomics ◽

10.2174/1570164618999210101222637 ◽

2021 ◽

Vol 18 ◽

Author(s):

Min Liu ◽

Lu Zhang ◽

Xinyi Qin ◽

Tao Huang ◽

Ziwei Xu ◽

...

Keyword(s):

Prediction Model ◽

Protein Sequence ◽

Cross Validation ◽

Transplant Rejection ◽

Sampling Technique ◽

Single Type ◽

Post Translational Modification ◽

Tyrosine Residues ◽

Under Sampling ◽

Fusion Features

Background: Nitration is one of the important Post-Translational Modification (PTM) occurring on the tyrosine residues of proteins. The occurrence of protein tyrosine nitration under disease conditions is inevitable and represents a shift from the signal transducing physiological actions of -NO to oxidative and potentially pathogenic pathways. Abnormal protein nitration modification can lead to serious human diseases, including neurodegenerative diseases, acute respiratory distress, organ transplant rejection and lung cancer. Objective: It is necessary and important to identify the nitration sites in protein sequences. Predicting that which tyrosine residues in the protein sequence are nitrated and which are not is of great significance for the study of nitration mechanism and related diseases. Methods: In this study, a prediction model of nitration sites based on the over-under sampling strategy and the FCBF method was proposed by stacking ensemble learning and fusing multiple features. Firstly, the protein sequence sample was encoded by 2701-dimensional fusion features (PseAAC, PSSM, AAIndex, CKSAAP, Disorder). Secondly, the ranked feature set was generated by the FCBF method according to the symmetric uncertainty metric. Thirdly, in the process of model training, use the over- and under- sampling technique was used to tackle the imbalanced dataset. Finally, the Incremental Feature Selection (IFS) method was adopted to extract an optimal classifier based on 10-fold cross-validation. Results and Conclusion: Results show that the model has significant performance advantages in indicators such as MCC, Recall and F1-score, no matter in what way the comparison was conducted with other classifiers on the independent test set, or made by cross-validation with single-type feature or with fusion-features on the training set. By integrating the FCBF feature ranking methods, over- and under- sampling technique and a stacking model composed of multiple base classifiers, an effective prediction model for nitration PTM sites was build, which can achieve a better recall rate when the ratio of positive and negative samples is highly imbalanced.

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