Leveraging Drug-Target Interaction Data for the Translation of Computational Models into Clinically Actionable Interventions

2021 ◽  
Author(s):  
Spencer C. Richmanz ◽  
Cole A. Lyman ◽  
Matthew C. Morris ◽  
Hongbao Caoy ◽  
Anastasia Nesterovay ◽  
...  
Keyword(s):  
Drug Target ◽  
Computational Models ◽  
Interaction Data ◽  
Target Interaction

scholarly journals Optimasi Data Tidak Seimbang pada Interaksi Drug Target dengan Sampling dan Ensemble Support Vector Machine

2020 ◽  
Vol 7 (6) ◽  
pp. 1221
Author(s):  
Nabila Sekar Ramadhanti ◽  
Wisnu Ananta Kusuma ◽  
Annisa Annisa
Keyword(s):  
Support Vector Machine ◽  
Nuclear Receptor ◽  
Protein Interaction ◽  
Drug Target ◽  
Imbalanced Data ◽  
Support Vector ◽  
Interaction Data ◽  
Target Interaction ◽  
Data Problem ◽  
F Measure

<p>Data tidak seimbang menjadi salah satu masalah yang muncul pada masalah prediksi atau klasifikasi. Penelitian ini memfokuskan untuk mengatasi masalah data tidak seimbang pada prediksi <em>drug-target interaction</em> (interaksi senyawa-protein). Ada banyak protein target dan senyawa obat yang terdapat pada basis data interaksi senyawa-protein yang belum divalidasi interaksinya secara eksperimen. Belum diketahuinya interaksi antar senyawa dan target tersebut membuat proporsi antara data yang diketahui interaksinya dan yang belum dikethui menjadi tidak seimbang. Data interaksi yang sangat tidak seimbang dapat menyebabkan hasil prediksi menjadi bias. Terdapat banyak cara untuk mengatasi data tidak seimbang ini, namun pada penelitian ini diimplementasikan metode yang menggabungkan <em>Biased Support Vector Machine</em> (BSVM), <em>oversampling, </em>dan <em>undersampling</em> dengan <em>Ensemble Support Vector Machine</em> (SVM). Penelitian ini mengeksplorasi efek sampling yang digabungkan dalam metode tersebut pada data interaksi senyawa-protein. Metode ini sudah diuji pada dataset <em>Nuclear Receptor,</em> <em>G-Protein Coupled Receptor</em> dan <em>Ion Channel </em>dengan rasio ketidakseimbangannya sebesar 14.6%, 32.36%, dan 28.2%. Hasil pengujian dengan menggunakan ketiga dataset tersebut menunjukkan nilai <em>area under curve</em> (AUC) secara berturut-turut sebesar 63.4%, 71.4%, 61.3% dan F-measure sebesar 54%, 60.7% dan 39%. Nilai akurasi dari metode yang digunakan masih terbilang cukup baik, walaupun nilai tersebut lebih kecil dari metode SVM tanpa perlakuan apapun. Nilai tersebut <em>bias</em> karena nilai AUC dan F-measure ternyata lebih kecil. Hal ini membuktikan bahwa metode yang diusulkan dapat menurunkan tingkat bias pada data tidak seimbang yang diuji dan meningkatkan nilai AUC dan f-measure sekitar 5%-20%.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Imbalanced data </em><em>has been one of the problems that arise in processing data. This research is focusing on handling imbalanced data problem for </em><em>drug-target</em><em> </em><em>(compound-protein) interaction data. There are many target protein and drug compound existed in compound-protein interaction databases, which many interactions are not validated yet by experiment. This unknown</em><em> interaction led drug target interaction to become imbalanced data. A really imbalanced data may cause bias to prediction result. There are many ways of handling imbalanced data, but this research implemented some methods such as BSVM, oversampling, undersampling with SVM ensemble. These method already solve the imbalanced data problem on other kind of data like image data. This research is focusing on exploration of effect on the sampling that used in these method for </em><em>compound-protein</em><em> interaction data. This method had been tested on </em><em>compound-protein</em><em> interaction Nuclear Receptor, GPCR</em> <em>and Ion Channel with 14.6%, 32.36% and 28.2% of imbalance ratio. The evaluation result using these three dataset show the value of AUC respectively 63.4%, 71.4%, 61.3% and F-measure of 54%, 60.7% and 39%. The score from this method is quite good, even though the score of accuracy and precision is smaller than the SVM. The value is bias because the AUC and F-measure score is smaller. This proves that the proposed method could reduce the bias rate in the evaluated imbalanced data and increase AUC and f-measure score from 5% to 20%.</em></p><p><em><strong><br /></strong></em></p>

Understanding Membrane Protein Drug Targets in Computational Perspective

2019 ◽  
Vol 20 (5) ◽  
pp. 551-564 ◽  
Author(s):  
Jianting Gong ◽  
Yongbing Chen ◽  
Feng Pu ◽  
Pingping Sun ◽  
Fei He ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Membrane Protein ◽  
Drug Target ◽  
Drug Targets ◽  
Computational Models ◽  
Drug Repurposing ◽  
Protein Targets ◽  
Target Interaction ◽  
Alternative Understanding

Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.

The Computational Models of Drug-target Interaction Prediction

2020 ◽  
Vol 27 (5) ◽  
pp. 348-358 ◽  
Author(s):  
Yijie Ding ◽  
Jijun Tang ◽  
Fei Guo
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Computational Models ◽  
Data Sets ◽  
Target Interaction ◽  
Interaction Prediction ◽  
The Past ◽  
Benchmark Data ◽  
Precision Recall Curve

:The identification of Drug-Target Interactions (DTIs) is an important process in drug discovery and medical research. However, the tradition experimental methods for DTIs identification are still time consuming, extremely expensive and challenging. In the past ten years, various computational methods have been developed to identify potential DTIs. In this paper, the identification methods of DTIs are summarized. What's more, several state-of-the-art computational methods are mainly introduced, containing network-based method and machine learning-based method. In particular, for machine learning-based methods, including the supervised and semisupervised models, have essential differences in the approach of negative samples. Although these effective computational models in identification of DTIs have achieved significant improvements, network-based and machine learning-based methods have their disadvantages, respectively. These computational methods are evaluated on four benchmark data sets via values of Area Under the Precision Recall curve (AUPR).

Drug-target interaction data cluster analysis based on improving the density peaks clustering algorithm

2019 ◽  
Vol 23 (6) ◽  
pp. 1335-1353 ◽  
Author(s):  
Maozu Guo ◽  
Donghua Yu ◽  
Guojun Liu ◽  
Xiaoyan Liu ◽  
Shuang Cheng
Keyword(s):  
Cluster Analysis ◽  
Drug Target ◽  
Clustering Algorithm ◽  
Interaction Data ◽  
Target Interaction ◽  
Density Peaks ◽  
Density Peaks Clustering

scholarly journals Drug–target interaction prediction: databases, web servers and computational models

2015 ◽  
Vol 17 (4) ◽  
pp. 696-712 ◽  
Author(s):  
Xing Chen ◽  
Chenggang Clarence Yan ◽  
Xiaotian Zhang ◽  
Xu Zhang ◽  
Feng Dai ◽  
...  
Keyword(s):  
Drug Target ◽  
Computational Models ◽  
Web Servers ◽  
Target Interaction ◽  
Interaction Prediction

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

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.

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

2013 ◽  
Vol 13 (14) ◽  
pp. 1636-1649 ◽  
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

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

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