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Network-based drug repurposing for MYH9-related nephritis

Muhammad Ali, Tommaso Gili, Guido Caldarelli

TL;DR

The organization of a MYH9-oriented drug-like library in chemical space using a multi-descriptor framework is analyzed, providing a statistically validated network representation of chemical space and a principled strategy to extract consensus-stable compounds for downstream screening.

Abstract

Using tools from network theory, we analyze the organization of a MYH9-oriented drug-like library in chemical space using a multi-descriptor 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 co-clustering matrix over 1.25 X 10^7 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.

Network-based drug repurposing for MYH9-related nephritis

TL;DR

The organization of a MYH9-oriented drug-like library in chemical space using a multi-descriptor framework is analyzed, providing a statistically validated network representation of chemical space and a principled strategy to extract consensus-stable compounds for downstream screening.

Abstract

Using tools from network theory, we analyze the organization of a MYH9-oriented drug-like library in chemical space using a multi-descriptor 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 co-clustering matrix over 1.25 X 10^7 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.
Paper Structure (28 sections, 11 equations, 6 figures, 14 tables)

This paper contains 28 sections, 11 equations, 6 figures, 14 tables.

Table of Contents

  1. Introduction
  2. Results
  3. Topology
  4. Communities
  5. Cross-descriptor consensus via co-clustering
  6. Global distribution of co-clustering values
  7. Descriptor Complementarity Overview
  8. Per-compound descriptor stability
  9. Unique Cluster Membership:
  10. Number of Molecules Sharing $\geq$ 1 Cluster:
  11. Total Co-Clustering Strength:
  12. Consolidated Interpretation
  13. Structural and consensus backbones via minimum spanning trees
  14. Identification of backbone key compounds
  15. Discussion
  16. ...and 13 more sections

Figures (6)

  • Figure 1: Descriptor-specific KNN networks for SMILES, xLogP, HBD, HBA, MW, and ROTB.
  • Figure 2: Clustered similarity matrices (SMILES, xLogP, HBD, HBA, MW, ROTB).
  • Figure 3: Observed versus null modularity distributions for the six descriptor-specific kNN networks (SMILES, xLogP, HBD, HBA, MW, and ROTB).
  • Figure 4: Distribution of co-clustering frequency across 6 descriptors.
  • Figure 5: Co-clustering heatmap for a representative subset of molecules.
  • ...and 1 more figures