scholarly journals SmartGraph: A Network Pharmacology Investigation Platform

2019 ◽  
Author(s):  
Gergely Zahoránszky-Kőhalmi ◽  
Timothy Sheils ◽  
Tudor I. Oprea
Keyword(s):  
Drug Target ◽  
State Of The Art ◽  
Target Prediction ◽  
Drug Repurposing ◽  
Network Pharmacology ◽  
Biomedical Data ◽  
Graph Database ◽  
High Quality ◽  
Complex Landscape ◽  
Unique Compound

AbstractMotivationDrug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate high-quality biomedical data, combined with analytic and predictive workflows as well as efficient visualization. SmartGraph is an innovative platform that utilizes state-of-the-art technologies such as a Neo4j graph-database, Angular web framework, RxJS asynchronous event library and D3 visualization to accomplish these goals.ResultsThe SmartGraph framework integrates high quality bioactivity data and biological pathway information resulting in a knowledgebase comprised of 420,526 unique compound-target interactions defined between 271,098 unique compounds and 2,018 targets. SmartGraph then performs bioactivity predictions based on the 63,783 Bemis-Murcko scaffolds extracted from these compounds. Through several use-cases, we illustrate the use of SmartGraph to generate hypotheses for elucidating mechanism-of-action, drug-repurposing and off-target prediction.Availabilityhttps://smartgraph.ncats.io/

scholarly journals Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

2018 ◽  
Vol 9 (24) ◽  
pp. 5441-5451 ◽  
Author(s):  
Andreas Mayr ◽  
Günter Klambauer ◽  
Thomas Unterthiner ◽  
Marvin Steijaert ◽  
Jörg K. Wegner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Comparative Study ◽  
Drug Target ◽  
Large Scale ◽  
State Of The Art ◽  
Target Prediction ◽  
Prediction Methods ◽  
Machine Learning Methods ◽  
Drug Target Prediction

The to date largest comparative study of nine state-of-the-art drug target prediction methods finds that deep learning outperforms all other competitors. The results are based on a benchmark of 1300 assays and half a million compounds.

scholarly journals Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach

2020 ◽  
Vol 36 (16) ◽  
pp. 4490-4497
Author(s):  
Siqi Liang ◽  
Haiyuan Yu
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Drug Targets ◽  
Class Imbalance ◽  
Target Prediction ◽  
Drug Repurposing ◽  
New Drugs ◽  
Supplementary Information ◽  
Training Scheme ◽  
Drug Target Prediction

Abstract Motivation 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. Results 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 potentially 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. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Bioinformatics Investigation into the Pharmacological Mechanisms of Sodium-Glucose Co-transporter 2 Inhibitors on Diabetes Mellitus and Heart Failure based on Network Pharmacology

2021 ◽  
Author(s):  
Ziling Mai ◽  
Huanqiang Li ◽  
Guanzhong Chen ◽  
Enzhao Chen ◽  
Liwei Liu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Signaling Pathway ◽  
Drug Target ◽  
Kinase Inhibitor ◽  
Target Prediction ◽  
Mapk Signaling ◽  
Mendelian Inheritance ◽  
Sglt2 Inhibitors ◽  
Network Pharmacology ◽  
Pharmacological Mechanism

Abstract BackgroundDiabetes mellitus (DM) is a major risk factor for the development of heart failure (HF). Sodium-glucose co-transporter 2 (SGLT2) inhibitors have been demonstrated consistent benefits in the reduction of hospitalization for HF in patients with DM. However, the pharmacological mechanism is not clear. To investigate the mechanisms of SGLT2 inhibitors on HF and DM, we performed target prediction and network analysis by network pharmacology method.Material/MethodsWe selected targets of SGLT2 inhibitors according to SwissTargetPrediction and DrugBank databases and collected therapeutic targets on HF and DM from the Human Gene (GeneCards) and Human Mendelian Inheritance (OMIM) databases. The “Drug-Target” and “Drug-Target-Disease” networks were constructed by using Cytoscape_v3.6.1. Then the protein-protein interaction (PPI) was analyzed by using the String database. Gene Ontology (GO) biological functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed to investigate by using Bioconductor tool for analysis.ResultsThere were 125 effective targets among SGLT2 inhibitors, HF and DM. Through further screening and analyzing, 33 core targets were obtained, such as SRC, MAPK1, NARS, MAPK3 and EGFR. And it is predicted that Rap1 signaling pathway, MAPK signaling pathway, EGFR tyrosine kinase inhibitor resistance, AGE-RAGE signaling pathway in diabetic complications and other signaling pathways were involved in the treatment of HF and DM by SGLT2 inhibitors.ConclusionsOur study elucidated the possible mechanisms of SGLT2 inhibitors from a systemic and holistic perspective based on pharmacological networks. The key targets and pathways will provide new insights for further research on the pharmacological mechanism of SGLT2 inhibitors in the therapy of HF and DM.

scholarly journals Network-Assisted Prediction of Potential Drugs for Addiction

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jingchun Sun ◽  
Liang-Chin Huang ◽  
Hua Xu ◽  
Zhongming Zhao
Keyword(s):  
Drug Addiction ◽  
Drug Target ◽  
Addiction Treatment ◽  
Complex Disease ◽  
Drug Repurposing ◽  
Network Pharmacology ◽  
Network Analyses ◽  
Addictive Drugs ◽  
Target Network ◽  
Addictive Drug

Drug addiction is a chronic and complex brain disease, adding much burden on the community. Though numerous efforts have been made to identify the effective treatment, it is necessary to find more novel therapeutics for this complex disease. As network pharmacology has become a promising approach for drug repurposing, we proposed to apply the approach to drug addiction, which might provide new clues for the development of effective addiction treatment drugs. We first extracted 44 addictive drugs from the NIDA and their targets from DrugBank. Then, we constructed two networks: an addictive drug-target network and an expanded addictive drug-target network by adding other drugs that have at least one common target with these addictive drugs. By performing network analyses, we found that those addictive drugs with similar actions tended to cluster together. Additionally, we predicted 94 nonaddictive drugs with potential pharmacological functions to the addictive drugs. By examining the PubMed data, 51 drugs significantly cooccurred with addictive keywords than expected. Thus, the network analyses provide a list of candidate drugs for further investigation of their potential in addiction treatment or risk.

scholarly journals GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures

2021 ◽  
Vol 11 (7) ◽  
pp. 3239
Author(s):  
Shicheng Cheng ◽  
Liang Zhang ◽  
Bo Jin ◽  
Qiang Zhang ◽  
Xinjiang Lu ◽  
...  
Keyword(s):  
Mutual Information ◽  
Link Prediction ◽  
Drug Target ◽  
State Of The Art ◽  
Target Prediction ◽  
Representation Learning ◽  
Graph Representation ◽  
Operating Characteristics ◽  
Information Index ◽  
Drug Target Prediction

The prediction of drug–target interactions is always a key task in the field of drug redirection. However, traditional methods of predicting drug–target interactions are either mediocre or rely heavily on data stacking. In this work, we proposed our model named GraphMS. We merged heterogeneous graph information and obtained effective node information and substructure information based on mutual information in graph embeddings. We then learned high quality representations for downstream tasks, and proposed an end–to–end auto–encoder model to complete the task of link prediction. Experimental results show that our method outperforms several state–of–the–art models. The model can achieve the area under the receiver operating characteristics (AUROC) curve of 0.959 and area under the precise recall curve (AUPR) of 0.847. We found that the mutual information between the substructure and graph–level representations contributes most to the mutual information index in a relatively sparse network. And the mutual information between the node–level and graph–level representations contributes most in a relatively dense network.

scholarly journals A Bioinformatics Investigation into the Pharmacological Mechanisms of Sodium-Glucose Co-transporter 2 Inhibitors in Diabetes Mellitus and Heart Failure Based on Network Pharmacology

2021 ◽  
Author(s):  
Ziling Mai ◽  
Huanqiang Li ◽  
Guanzhong Chen ◽  
Enzhao Chen ◽  
Liwei Liu ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Heart Failure ◽  
Signaling Pathway ◽  
Drug Target ◽  
Kinase Inhibitor ◽  
Target Prediction ◽  
Mapk Signaling ◽  
Sglt2 Inhibitors ◽  
Network Pharmacology ◽  
Pharmacological Mechanism

Abstract Purpose Diabetes mellitus (DM) is a major risk factor for the development of heart failure (HF). Sodium-glucose co-transporter 2 (SGLT2) inhibitors have demonstrated consistent benefits in the reduction of hospitalization for HF in patients with DM. However, the pharmacological mechanism is not clear. To investigate the mechanisms of SGLT2 inhibitors in DM with HF, we performed target prediction and network analysis by a network pharmacology method. Methods We selected targets of SGLT2 inhibitors and DM status with HF from databases and studies. The “Drug-Target” and “Drug-Target-Disease” networks were constructed using Cytoscape. Then the protein–protein interaction (PPI) was analyzed using the STRING database. Gene Ontology (GO) biological functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed to investigate using the Bioconductor tool for analysis. Results There were 125 effective targets between SGLT2 inhibitors and DM status with HF. Through further screening, 33 core targets were obtained, including SRC, MAPK1, NARS, MAPK3 and EGFR. It was predicted that the Rap1 signaling pathway, MAPK signaling pathway, EGFR tyrosine kinase inhibitor resistance, AGE-RAGE signaling pathway in diabetic complications and other signaling pathways were involved in the treatment of DM with HF by SGLT2 inhibitors. Conclusion Our study elucidated the possible mechanisms of SGLT2 inhibitors from a systemic and holistic perspective based on pharmacological networks. The key targets and pathways will provide new insights for further research on the pharmacological mechanism of SGLT2 inhibitors in the treatment of DM with HF.

scholarly journals A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

2020 ◽  
Author(s):  
Gergely Zahoránszky-Kőhalmi ◽  
Vishal B. Siramshetty ◽  
Praveen Kumar ◽  
Manideep Gurumurthy ◽  
Busola Grillo ◽  
...  
Keyword(s):  
Drug Target ◽  
Drug Repositioning ◽  
Host Protein ◽  
Network Pharmacology ◽  
Graph Database ◽  
Web Interface ◽  
Emerging Pathogen ◽  
Research Setting ◽  
Critical Data ◽  
Life Saving

AbstractMotivationIn the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.ResultsHere, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named “Neo4COVID19” is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.Availabilityhttps://neo4covid19.ncats.io

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

2019 ◽  
Author(s):  
Siqi Liang ◽  
Haiyuan Yu
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Drug Targets ◽  
Class Imbalance ◽  
Target Prediction ◽  
Drug Repurposing ◽  
New Drugs ◽  
Learning Framework ◽  
Training Scheme ◽  
Drug Target Prediction

AbstractIn silicodrug 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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