Drug Repositioning Pipeline Integrating Community Analysis in Drug-Drug Similarity Networks and Automated ATC Community Labeling to Foster Molecular Docking Analysis

Daiana Colibășanu¹Vlad Groza²Maria Antonietta Occhiuzzi³Fedora Grande^3*Mihai Udrescu^2*Lucretia Udrescu¹

• ¹Universitatea de Medicina si Farmacie Victor Babes din Timisoara, Timișoara, Romania
• ²Politehnica University of Timișoara, Timișoara, Romania
• ³Universita della Calabria, Arcavacata di Rende, Italy

Introduction Drug repositioning—finding new therapeutic uses for existing drugs—can dramatically reduce development time and cost, but requires efficient computational frameworks to generate and validate repositioning hypotheses. Network-based methods can uncover drug communities with shared pharmacological properties, while molecular docking offers mechanistic insights by predicting drug–target binding. Methods We introduce an end-to-end, fully automated pipeline that (1) constructs a tripartite drug-gene-disease network from DrugBank and DisGeNET, (2) projects it into a drug-drug similarity network for community detection, (3) labels communities via Anatomical Therapeutic Chemical (ATC) codes to generate repositioning hints and identify relevant targets, (4) validates hints through automated literature searches, and (5) prioritizes candidates via targeted molecular docking. Results After filtering for connectivity and size, 12 robust communities emerged from the initial 34 clusters. The pipeline correctly matched 53.4% of drugs to their ATC level 1 community label via database entries; literature validation confirmed an additional 20.2%, yielding 73.6% overall accuracy. The remaining 26.4% of drugs were flagged as repositioning candidates. To illustrate the advantages of our pipeline, molecular docking studies of chloramphenicol demonstrated stable binding and interaction profiles similar to those of known inhibitors, reinforcing its potential as an anticancer agent. Conclusions Our integrated pipeline effectively integrates network-based community analysis and automated ATC labeling with literature and docking analysis, narrowing the search space for in silico and experimental follow-up. The chloramphenicol example illustrates its utility for uncovering non-obvious repositioning opportunities. Future work will extend similarity definitions (e.g., to higher-order network motifs) and incorporate wet-lab validation of top candidates.