
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/
Pathway analysis, Network visualization, Potent chemical pattern, Information technology, Protein-protein interactions (PPIs), T58.5-58.64, neo4j, Scaffold, Chemistry, DRUGS, Protein–protein interactions (PPIs), Network perturbation, Target deconvolution, Bioactivity prediction, QD1-999, Network pharmacology, Research Article
Pathway analysis, Network visualization, Potent chemical pattern, Information technology, Protein-protein interactions (PPIs), T58.5-58.64, neo4j, Scaffold, Chemistry, DRUGS, Protein–protein interactions (PPIs), Network perturbation, Target deconvolution, Bioactivity prediction, QD1-999, Network pharmacology, Research Article
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 18 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
