Network-based clustering and statistical evaluation to elucidate structure-activity relationships of EZH2 inhibitors

Danishuddin*, M A Haque, G Madhukar, S Khan, Q M S Jamal, S Srivastava, J J Kim, K Ahmad

*Corresponding author af dette arbejde

Abstract

Enhancer of Zeste Homolog 2 (EZH2) inhibitors have demonstrated selective efficacy, but their broader therapeutic potential remains limited, highlighting the need to clarify the structural basis of their activity. The central aim of our study is to systematically analyse the structural diversity and activity patterns of known EZH2 inhibitors to provide insights that may guide incremental scaffold optimization. We examined 531 potential EZH2 inhibitors retrieved from ChEMBL through a cheminformatics workflow encompassing clustering, scaffold identification, activity cliff detection, and chemical space visualization. Using RDKit and NetworkX, 94 clusters were generated, of which 13 contained ten or more compounds. Notably, clusters 6, 16, 20, 21, and 31 exhibited favourable balances of structural homogeneity and enrichment scores, suggesting chemical cohesiveness and biological relevance for structure - activity relationship (SAR) prioritization. Statistical analyses revealed significant differences in mean pIC50 values across clusters, underscoring distinct activity distributions linked to structural groups. Scaffold analysis highlighted pyrrole - benzamide derivatives, particularly those incorporating morpholine and piperidine motifs, as enriched among potent inhibitors. Substructure evaluation further indicated that aromatic rings and aromatic amine groups were positively correlated with bioactivity. These findings delineate key SAR features of EZH2 inhibitors and provide guidance for scaffold refinement, hit identification, and lead optimization.

OriginalsprogEngelsk
TidsskriftSAR and QSAR in environmental research
Vol/bind36
Udgave nummer9
Sider (fra-til)827-851
Antal sider25
ISSN1026-776X
DOI
StatusUdgivet - 2025

Fingeraftryk

Dyk ned i forskningsemnerne om 'Network-based clustering and statistical evaluation to elucidate structure-activity relationships of EZH2 inhibitors'. Sammen danner de et unikt fingeraftryk.

Citationsformater