Integrated computational screening of Ageratina adenophora metabolites for anticancer activity via ADMET, molecular docking, and cytotoxicity predictions

Ageratina adenophora is a plant rich in secondary metabolites with potential pharmacological applications. In this study, GC-MS and UHPLC-QTOF-MS analyses revealed a diverse array of phytochemicals, including sesquiterpenes, flavonoids, alkaloids, saponins, coumarins, sterols, and phenolic acids, highlighting the species as a promising source of bioactive compounds. To evaluate their therapeutic potential, an integrated in silico screening pipeline was employed, incorporating physicochemical characterization, drug-likeness evaluation, and ADMET profiling. Among the identified metabolites, Acon1_001440 emerged as the most promising candidate, exhibiting favorable pharmacokinetic properties, minimal PAINS alerts, and compliance with multiple drug-likeness filters. Molecular docking against key cancer-associated targets - EGFR, p53, MMP7, and CDK8/Cyclin C - revealed strong binding affinities of up to − 8.2 kcal/mol, along with stable non-covalent interactions. Molecular dynamics simulations of the EGFR - Acon1_001440 complex further confirmed structural stability through consistent RMSD, RMSF, radius of gyration, solvent-accessible surface area (SASA), and hydrogen-bonding patterns. MM/GBSA binding free energy calculations (-12.28 kcal/mol) supported favorable binding energetics and suggested multitarget anticancer potential. Toxicity predictions indicated low acute oral toxicity, although potential carcinogenicity signals warrant further experimental validation. Collectively, these findings identify A. adenophora as a valuable reservoir of pharmacologically active metabolites and highlight Acon1_001440 as a promising lead compound for anticancer drug discovery. The integrated computational framework presented here provides a basis for future experimental validation and the development of multitarget anticancer agents.