Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug–Gene Interactions

. 2021 Dec 8;13(12):2117. doi: 10.3390/pharmaceutics13122117

Algorithm 1 Find the parameter

λ

, such that the clustering

C

of nodes/drugs

D_{i}

G

with modularity resolution

λ

(i.e.,

Clustering (G, λ)

) produces the biggest number of repositionings confirmed with the level 1 ATC codes in DrugBank 5.1.8.

Input: Drug-drug similarity network $G = (V_{D}, E)$ based on drug-gene interaction data from DrugBank 5.0.9., ATC codes for drugs in DrugBank versions 5.0.9 and 5.1.8
Output: The $λ$ value that generates the highest number of confirmed repositionings.

1:
for $λ$ in range (0.1 to 5), with 0.1 steps do
2:
$C \Leftarrow Clustering (G, λ)$
3:
for all $C_{i} \in C$ do
4:
$P_{i} \Leftarrow A^{1} (C_{i})$
5:
$R_{i}^{c} \Leftarrow \emptyset$
6:
for all $D_{j} \in C_{i}$ do
7:
if $then P_{i} \notin A^{1} (D_{j}) & P_{i} \in A_{c}^{1} (D_{j})$
8:
$R_{i}^{c} \Leftarrow R_{i}^{c} \cup \{D_{j}\}$
9:
end if
10:
end for
11:
end for
12:
$R^{c} = \Leftarrow ⋃_{i} R_{i}^{c}$
13:
end for
14:
Return the value of $λ_{m a x}$ corresponding to $max \{| {ʹ R}^{c} |\}$