Structure-guided target prioritization of heterocyclic carboxamides for hyperlipidemia and obesity via cheminformatics, network biology, and docking studies

Abstract

Background Target identification for hyperlipidemia and obesity remains challenging due to limitations in data quality, target validation, and clinical translation. Methods To overcome these barriers, the study developed an integrative computational framework combining cheminformatics, structural biology, and network pharmacology. Heterocyclic carboxamide derivatives were systematically evaluated as potential modulators of metabolic disorders using: (1) similarity searching across PubChem, CDDI, and SEA databases; (2) protein-protein interaction network analysis of prioritized targets; and (3) induced-fit docking against key metabolic enzymes. Results Diacylglycerol O-acyltransferase 1 (DGAT1) emerged as the top candidate, showing consistent identification across all databases, perfect disease relevance (6/6), and a key role in triglyceride biosynthesis. Additional prioritized targets included ADORA2A, EPHX2, and HDAC1/6, supported by cheminformatics and network-level evidence. Molecular docking confirmed strong interactions between lead carboxamides and DGAT1 (PDB: 8ESM), with binding energies ranging from –7.88 to –11.57 kcal/mol and key contacts at W374, H382, and S411. Notably, DGAT1’s therapeutic relevance was supported by clinical-stage inhibitors (pradigastat and VK-1430) for hyperlipidemia and NASH. Conclusion: This integrative framework improves early-stage drug discovery by enabling efficient target prioritization and validation. Findings support the development of carboxamide-based DGAT1 inhibitors as promising multi-target agents for metabolic disorders and present a scalable approach for target discovery in complex diseases.