Predicting the active sites of quinolone antibiotics interacting with organisms by deep learning and molecular docking.

Quinolones (QNs) antibiotics have become one of the most commonly used antibacterial drugs for human and animals in the world. In this study, we focused on 19 common quinolone (QN) antibiotics and collected their bioassay activity data from the PubChem website. Subsequently, using deep learning techniques, we constructed 45 biological activity prediction models based on the PubChem BioAssay dataset. The prediction accuracy of all models exceeded 95%, with the exception of the model for CCRIS mutagenicity studies, which achieved an accuracy of 85.22 ± 0.17%. Collectively, these deep learning models can serve as reliable tools for the prediction and evaluation of quinolone antibiotics. The bioassay activity of 19 QNs antibiotics was predicted by developed models to fill in the missing activity data. It was found that QNs antibiotics were generally active against bacterial DNA repair enzymes and neurobehavioral related protein, including hypothetical protein HP1089, recBCD - exodeoxyribonuclease V subunit RecBCD, recombination protein RecB and SLC5A7. Molecular dynamics simulation results showed that all fluoroquinolone complexes with HP1089, recBCD, RecB, and SLC5A7 reached stable conformations after an initial 0-10 ns relaxation, Our research provides a theoretical basis and technical support for elucidating the regulatory mechanisms of organisms in response to environmental exogenous chemicals, the formulation of environmental protection and food safety policies, the risk assessment of novel compounds, and the development of eco-friendly pharmaceuticals.