Drug-Target Interaction Network Predictions for Drug Repurposing Using LASSO-Based Regularized Linear Classification Model

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)).

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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 ◽

10.3390/v12111325 ◽

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.

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Drug-Target Interaction Network Analysis of Gene-Phenotype Connectivity Maintained by Genistein

Journal of Computational Biology ◽

10.1089/cmb.2019.0443 ◽

2020 ◽

Vol 27 (12) ◽

pp. 1678-1687 ◽

Cited By ~ 1

Author(s):

Baoshan Li ◽

Yi Jiang ◽

Jingxin Chu ◽

Qian Zhou

Keyword(s):

Network Analysis ◽

Drug Target ◽

Interaction Network ◽

Target Interaction

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The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective

PLoS ONE ◽

10.1371/journal.pone.0165737 ◽

2016 ◽

Vol 11 (11) ◽

pp. e0165737 ◽

Cited By ~ 30

Author(s):

Ying Hong Li ◽

Pan Pan Wang ◽

Xiao Xu Li ◽

Chun Yan Yu ◽

Hong Yang ◽

...

Keyword(s):

Comparative Study ◽

Drug Target ◽

Interaction Network ◽

Target Interaction ◽

Human Kinome

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Drug-target interaction prediction for drug repurposing with probabilistic similarity logic

Proceedings of the 12th International Workshop on Data Mining in Bioinformatics - BioKDD '13 ◽

10.1145/2500863.2500870 ◽

2013 ◽

Cited By ~ 10

Author(s):

Shobeir Fakhraei ◽

Louiqa Raschid ◽

Lise Getoor

Keyword(s):

Drug Target ◽

Drug Repurposing ◽

Target Interaction ◽

Interaction Prediction

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A landscape for drug-target interactions based on network analysis

PLoS ONE ◽

10.1371/journal.pone.0247018 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0247018

Author(s):

Edgardo Galan-Vasquez ◽

Ernesto Perez-Rueda

Keyword(s):

Drug Target ◽

Target Genes ◽

Metabolic Diseases ◽

Drug Repositioning ◽

Topological Analysis ◽

Interaction Network ◽

Clustering Coefficient ◽

Connected Components ◽

Target Interaction ◽

Target Network

In this work, we performed an analysis of the networks of interactions between drugs and their targets to assess how connected the compounds are. For our purpose, the interactions were downloaded from the DrugBank database, and we considered all drugs approved by the FDA. Based on topological analysis of this interaction network, we obtained information on degree, clustering coefficient, connected components, and centrality of these interactions. We identified that this drug-target interaction network cannot be divided into two disjoint and independent sets, i.e., it is not bipartite. In addition, the connectivity or associations between every pair of nodes identified that the drug-target network is constituted of 165 connected components, where one giant component contains 4376 interactions that represent 89.99% of all the elements. In this regard, the histamine H1 receptor, which belongs to the family of rhodopsin-like G-protein-coupled receptors and is activated by the biogenic amine histamine, was found to be the most important node in the centrality of input-degrees. In the case of centrality of output-degrees, fostamatinib was found to be the most important node, as this drug interacts with 300 different targets, including arachidonate 5-lipoxygenase or ALOX5, expressed on cells primarily involved in regulation of immune responses. The top 10 hubs interacted with 33% of the target genes. Fostamatinib stands out because it is used for the treatment of chronic immune thrombocytopenia in adults. Finally, 187 highly connected sets of nodes, structured in communities, were also identified. Indeed, the largest communities have more than 400 elements and are related to metabolic diseases, psychiatric disorders and cancer. Our results demonstrate the possibilities to explore these compounds and their targets to improve drug repositioning and contend against emergent diseases.

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DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions

10.1101/2021.04.28.441733 ◽

2021 ◽

Author(s):

Tilman Hinnerichs ◽

Robert Hoehndorf

Keyword(s):

Drug Target ◽

Drug Targets ◽

Interaction Network ◽

Drug Repurposing ◽

Computational Method ◽

Interaction Networks ◽

Supplementary Information ◽

Prediction Methods ◽

Link Type ◽

Molecular Features

AbstractMotivationIn silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding potentials. Both approaches can be combined with information about interaction networks.ResultsWe developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major affects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.AvailabilityDTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO.Supplementary informationSupplementary data are available at https://github.com/ THinnerichs/DTI-VOODOO.

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

10.26434/chemrxiv.12340718 ◽

2020 ◽

Cited By ~ 2

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.

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

10.26434/chemrxiv.12340718.v1 ◽

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.

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