Prioritize anti-SUD drug candidates

We prioritized candidate drugs using the network-based ranking algorithms that we previously used for drug repurposing, gene discovery, and gut microbial metabolite discovery for the disease [16–23, 27–29]. In brief, given an input or seeds (drug abuse, drug dependence, and drug withdrawal syndrome in our study), the ranking score for each drug on the entire network was iteratively updated by:

in which α (α = 0.15 in this study) denotes the probability of restarting from the seed nodes at each step. The algorithm was iterated until convergence (pk+1−pk<10−8).

We used D and G to represent DPN and PPIN, respectively. T denotes the transition matrix of DSEG network:

In Eq. (2), the diagonal sub-matrices T_xx (x ∈ {D, G}) were calculated through normalizing the adjacency matrix of D and G, the off-diagonal sub-matrices T_xy (x, y ∈ {D, G}) were calculated through normalizing the adjacency matrix of the bipartite network connecting D and G.

We evaluated how the drug prediction algorithm ranked the four drugs (methadone, buprenorphine, naltrexone, and naloxone) approved for the treatment of OUD or opioid overdose reversal. Lofexidine, an alpha 2 adrenergic agonists that was recently approved for the treatment of acute opioid withdrawal [10], was not included since it was not in the SIDER database and on the DPN. The average ranking of these four drugs among all FDA-approved drugs was calculated.