Investigation into the Quantitative Structure‐Biotoxicity Relationship of Antibiotics and their Estrogenic Receptor Disruption Effects

Abstract

In light of antibiotics being classified as environmental hormone‐like compounds, their interference with the endocrine system has significantly impacted human health and ecological environments. This study employed Gaussian09 software's Density Functional Theory (DFT) to structurally optimize and perform frequency calculations on 23 representative antibiotic molecules, aiming to obtain microscopic quantum mechanical structural parameters.Physicochemical property parameters were acquired through the RDKit database in the ChemDes platform. Through multiple linear regression analysis, the primary factors affecting antibiotic biotoxicity (pLD50) were identified, leading to the establishment of a QSAR model. The predictive capability of the model was analyzed using leave‐one‐out cross‐validation, and molecular docking was used to investigate the binding mode and mechanism of action between estrogen receptors (ER) and antibiotics. Research outcomes indicate that the established QSAR model C has regression coefficients R2 and leave‐one‐out cross‐validation coefficients Q2 of 0.92474 and 0.74913, respectively, demonstrating good stability and predictive power. Analysis through molecular surface electrostatic potential, frontier molecular orbitals, molecular docking, and molecular dynamics revealed that the potential estrogenic disrupting effects are primarily due to hydrogen bonds and hydrophobic interactions between antibiotics and estrogen receptors. This provides a valuable exploration for identifying and screening PPCPs with potential estrogenic disrupting effects.