Fig. 2

RepTB prediction workflow. a DrugBank DTI network was downloaded. Molecular function GO were mapped to the targets from DrugBank DTIs. Network was enriched by adding GO mapped DTIs to the network. The final network consists of 26,404 unique DTIs. b Network based inference (NBI) was used to predict new interactions between the drugs and targets (GO). Given a bipartite graph \( G = \left( {N,E} \right) \) where \( NisDT \)(D is set of drug nodes, T is set of Target nodes), and E is edge between D and T. The green edges are the known DTIs and the red edges depict the predicted DTIs. A weight matrix is using NBI for the predicted and known DTIs. c Predicted edges were removed where predicted score \( R_{ji} \) (where, R is the final resource matrix and j and i are the drugs and targets, respectively) was either zero or less than 20% of maximum DTI score for each drug. d 49 Mtb targets from DTI network were prioritized using combined evidence approach. A binary matrix was created with green (true) and red (false) placed for 4 conditions: (1) If syn/nonsyn variations are not present in the GMTV database. (2) If a human homolog is absent. (3) If the target is a reported essential gene. *Represents the target is present in prioritized list of targets from study done by Ramakrishnan et al. Representatives from the top 10 prioritized targets are shown—panC is essential in vivo, inhA is a known TB target. DrugBank Ids of the predicted drugs for the targets are also shown