scholarly journals Interactive visual analysis of drug–target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing

2018 ◽  
Author(s):  
Ziaurrehman Tanoli ◽  
Zaid Alam ◽  
Aleksandr Ianevski ◽  
Krister Wennerberg ◽  
Markus Vähä-Koskela ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Drug Target ◽  
Visual Analysis ◽  
Drug Repurposing ◽  
Interaction Networks ◽  
Target Interaction ◽  
Interactive Visual Analysis

scholarly journals Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model

Viruses ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1325
Author(s):  
Yoonjung Choi ◽  
Bonggun Shin ◽  
Keunsoo Kang ◽  
Sungsoo Park ◽  
Bo Ram Beck
Keyword(s):  
Deep Learning ◽  
Angiotensin Converting Enzyme ◽  
Drug Target ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
Target Interaction ◽  
Approved Drugs ◽  
Fda Approved Drugs

Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI). Unfortunately, additional clinically significant treatment options since the approval of remdesivir are scarce. To overcome the current coronavirus disease 2019 (COVID-19) more efficiently, a treatment strategy that controls not only SARS-CoV-2 replication but also the host entry step should be considered. In this study, we used MT-DTI to predict FDA approved drugs that may have strong affinities for the angiotensin-converting enzyme 2 (ACE2) receptor and the transmembrane protease serine 2 (TMPRSS2) which are essential for viral entry to the host cell. Of the 460 drugs with Kd of less than 100 nM for the ACE2 receptor, 17 drugs overlapped with drugs that inhibit the interaction of ACE2 and SARS-CoV-2 spike reported in the NCATS OpenData portal. Among them, enalaprilat, an ACE inhibitor, showed a Kd value of 1.5 nM against the ACE2. Furthermore, three of the top 30 drugs with strong affinity prediction for the TMPRSS2 are anti-hepatitis C virus (HCV) drugs, including ombitasvir, daclatasvir, and paritaprevir. Notably, of the top 30 drugs, AT1R blocker eprosartan and neuropsychiatric drug lisuride showed similar gene expression profiles to potential TMPRSS2 inhibitors. Collectively, we suggest that drugs predicted to have strong inhibitory potencies to ACE2 and TMPRSS2 through the DTI model should be considered as potential drug repurposing candidates for COVID-19.

scholarly journals Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

PLoS ONE ◽  
2010 ◽  
Vol 5 (3) ◽  
pp. e9603 ◽  
Author(s):  
Zhisong He ◽  
Jian Zhang ◽  
Xiao-He Shi ◽  
Le-Le Hu ◽  
Xiangyin Kong ◽  
...  
Keyword(s):  
Functional Groups ◽  
Drug Target ◽  
Interaction Networks ◽  
Target Interaction ◽  
Biological Features

scholarly journals Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures

Molecules ◽  
2017 ◽  
Vol 22 (7) ◽  
pp. 1119 ◽  
Author(s):  
Fan-Rong Meng ◽  
Zhu-Hong You ◽  
Xing Chen ◽  
Yong Zhou ◽  
Ji-Yong An
Keyword(s):  
Drug Target ◽  
Protein Sequences ◽  
Interaction Networks ◽  
Target Interaction ◽  
Chemical Structures

scholarly journals DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246920
Author(s):  
Sk Mazharul Islam ◽  
Sk Md Mosaddek Hossain ◽  
Sumanta Ray
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Repositioning ◽  
Geometric Mean ◽  
Imbalanced Data ◽  
Drug Repurposing ◽  
Search Space ◽  
Classification Model ◽  
Target Interaction ◽  
Rough Approximation

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

scholarly journals In-Silico FDA-Approved Drug Repurposing to Find the Possible Treatment of Coronavirus Disease-19 (COVID-19)

2020 ◽  
Author(s):  
Kumar Sharp ◽  
Dr. Shubhangi Dange
Keyword(s):  
In Silico ◽  
Drug Target ◽  
Drug Repurposing ◽  
Target Interaction ◽  
Compound Libraries ◽  
Anti Cancer ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Large Compound ◽  
Better Than

Identification of potential drug-target interaction for approved drugs serves as the basis of repurposing drugs. Studies have shown polypharmacology as common phenomenon. In-silico approaches help in screening large compound libraries at once which could take years in a laboratory. We screened a library of 1050 FDA-approved drugs against spike glycoprotein of SARS-CoV2 in-silico. Anti-cancer drugs have shown good binding affinity which is much better than hydroxychloroquine and arbidol. We have also introduced a hypothesis named “Bump” hypothesis which and be developed further in field of computational biology.

scholarly journals In-Silico FDA-Approved Drug Repurposing to Find the Possible Treatment of Coronavirus Disease-19 (COVID-19)

2020 ◽  
Author(s):  
Kumar Sharp ◽  
Dr. Shubhangi Dange
Keyword(s):  
In Silico ◽  
Drug Target ◽  
Drug Repurposing ◽  
Target Interaction ◽  
Compound Libraries ◽  
Anti Cancer ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Large Compound ◽  
Better Than

Identification of potential drug-target interaction for approved drugs serves as the basis of repurposing drugs. Studies have shown polypharmacology as common phenomenon. In-silico approaches help in screening large compound libraries at once which could take years in a laboratory. We screened a library of 1050 FDA-approved drugs against spike glycoprotein of SARS-CoV2 in-silico. Anti-cancer drugs have shown good binding affinity which is much better than hydroxychloroquine and arbidol. We have also introduced a hypothesis named “Bump” hypothesis which and be developed further in field of computational biology.

Predicting drug–target interaction networks of human diseases based on multiple feature information

2013 ◽  
Vol 14 (14) ◽  
pp. 1701-1707 ◽  
Author(s):  
Weiming Yu ◽  
Yan Yan ◽  
Qing Liu ◽  
Junxiang Wang ◽  
Zhenran Jiang
Keyword(s):  
Drug Target ◽  
Interaction Networks ◽  
Human Diseases ◽  
Target Interaction ◽  
Multiple Feature ◽  
Feature Information
Sign in / Sign up

Export Citation Format

Share Document