A landscape for drug-target interactions based on network analysis

Abstract

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.

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

“…However, the methods consider that high-degree nodes can be lethal, as drugging them could disrupt multiple critical molecular pathways. By using a linear combination of node metrics, one can utilize a combination of scores to capture different aspects of these graph theoretic metrics as outlined previously ( Galan-Vasquez and Perez-Rueda, 2021 ; Viacava Follis, 2021 ). For instance, even though how many connections a protein has is important, targeting high degree proteins can also cause toxicity.…”

BETA: a comprehensive benchmark for computational drug–target prediction

“…negatives). Conventionally, three characteristics of datasets were widely used for evaluations: (i) the connectivity pattern of the drugs and targets that underlie topological context and inherent connection profiles [ 21 , 52 , 53 ], (ii) the categories of drugs and targets in real scenarios [ 54–56 ] and (iii) the validation of the associations internally and externally [ 55 , 57 , 58 ]. As such, we designed the seven evaluation Tests that generated the training and testing associations based on the two Perspectives —validations and data spaces.…”

Community Detection Algorithms in Healthcare Applications: A Systematic Review

“…Community detection can reveal effective in analyzing the relationships between exiting drugs and/or target indicators. For instance, Galan-Vasquez and Perez-Rueda [201] examined the relationships between drugs and their targets using the DrugBank dataset and annotations from the United States Food and Drug Administration. CDDI (Core Drug Discovery for Indications) is currently a popular research topic in Traditional Chinese Medicine.…”

“…Galan-Vasquez and Perez-Rueda [201] Drug discovery Analysis of drug-target interactions Generate an improved understanding of how drugs interact with their targets.…”