Fig 1.
Schematic workflow of the drug-target network analysis.
See text for details.
Fig 2.
Topological estimations of the network of drugs and targets.
A) Input-degree distribution [P(Kin)]. B) Output-degree distribution [P(Kout)]. C) Distribution of the number of nodes in connected components [P(n)], where n represents the number of nodes and P(n) is the probability of finding a component with a specific size. D) Distribution of size communities in the networks, where P(Community) is the probability of finding a community with a specific size.
Table 1.
The top 10 most influential nodes according to their centralities.
Fig 3.
The top metabolic pathways related to the targets affected by hub drugs.
The 10 richest metabolic pathways were selected for each hub and were hierarchically clustered based on Euclidean distance and Ward’s method for linkage analysis. Each row represents the KEGG pathways and each column represents hub drugs.
Fig 4.
Communities in the drugs-targets network.
A network of 4862 nodes and 9286 edges were obtained from the DrugBank database that was clustering in communities. Each community is represented in a different color, and the labels are proportional to the output degree.
Table 2.
The top 10 largest communities according to their number of elements.
Table 3.
Top 10 drugs that most co-regulated.
Fig 5.
A total of 1140 nodes and 1386 edges were related to viral diseases. Targets are indicated as blue nodes, and drugs are the red nodes; Drugs related to Coronavirus infections are indicated as yellow nodes.