QSAR, molecular docking, ADMET properties in silico studies for a series of 7-propanamide benzoxaboroles as potent anti-cancer agents

The danger of ovarian cancer does not give any visible sign at the first stage of symptoms, for this reason, it is necessary to identify and discover effective drugs for this disease. In this work, we have performed 2D-QSAR and molecular docking studies for a series of 7-propanamide benzoxaboroles.The QSAR model was constructed using the seven descriptors, showed satisfactory internal and external vali-dation parameters (R2 train = 0.76, R2 adjusted = 0.69, Q2 cv = 0.62, R 2 test = 0.64) which passed the model acceptability criteria, the artificial neural network method showed a correlation coefficient of 0.88 with a 7-3-1. Molecular docking analysis was performed for the most active compound in the dataset, the resolved crystal structure of T. thermophilus LeuRS was obtained from the Protein Data Bank (PDB: 2V0G), and the obtained docking results showed that the most important hydrogen interactions are ARG 719 and ASP 808, and two alkyl bonds (TYR 722, and ALA711), and a single Pi-anion bond GLU720 have a favorable effect on the biological activity. Finally, we made a study of ADMET properties on the ten selected most active compounds, the obtained results show that compounds number 1, 23, 26, 28, 32, and 37 have verified all the pharmacokinetic properties and toxicity, so we can select these different molecules as anti-cancer drug candidates.

Automated summary

This study identified potential anti-ovarian cancer drugs through 2D-QSAR and molecular docking studies, and validated the pharmacokinetic and toxicity properties of some candidate drugs through ADMET evaluation.