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Network-based drug repurposing for MYH9-related nephritis

Muhammad Ali1, Tommaso Gili2, and Guido Caldarelli1,3,4

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Phys. Rev. E 114, 024309 – Published 17 August, 2026

DOI: https://doi.org/10.1103/v4k3-49dx

Abstract

Using tools from network theory, we analyze the organization of a MYH9-oriented druglike library in chemical space using a multidescriptor framework. The dataset is drawn from ZINC, a publicly available database of commercially accessible compounds curated for virtual screening and drug discovery. Starting from 6004 molecules, preprocessing yields 5000 structurally valid and descriptor-complete compounds. Similarity is defined via Tanimoto distance on Morgan fingerprints and single-descriptor distances for xLogP, HBD, HBA, molecular weight, and rotatable bonds. For each representation we construct k-nearest-neighbor networks and identify communities using the Louvain–Leiden algorithm. All networks exhibit highly significant modularity (Q=0.91−0.99) relative to degree-preserving null models, demonstrating pronounced nonrandom chemical organization across descriptors. Cross-descriptor robustness is quantified through a coclustering matrix over 1.25×107 molecular pairs, measuring how consistently compound pairs co-occur within the same community across descriptor-specific networks. Although most pairs show limited agreement, a sparse high-consensus core emerges, highlighting the complementarity of the descriptors. Minimum spanning trees derived from structural and consensus similarities reveal distinct backbone topologies: a scaffold-driven, sparse structure versus a compact, hub-rich consensus network. Betweenness centrality on these backbones identifies compounds that are both structurally central and descriptor-balanced. These results provide a statistically validated network representation of chemical space and a principled strategy to extract consensus-stable compounds for downstream screening.

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Phys. Rev. E 114, 024309– Published 17 August, 2026
  • Received 11 March 2026
  • Accepted 17 June 2026
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