1st International Conference on Chemo and BioInformatics, ICCBIKG 2021, (439-441)
AUTHOR(S) / АУТОР(И): Natalia E. Boboriko, He Liying, Yaraslau U. Dzichenka
E-ADRESS / Е-АДРЕСА: boboriko@bsu.by, dichenko@iboch.by
DOI: 10.46793/ICCBI21.439B
ABSTRACT / САЖЕТАК:
Cytochrome P450 17A1 (CYP17A1) is a critically important enzyme in humans that catalyzes the formation of all endogenous androgens. This enzyme is often considered a molecular target for the development of novel high efficient drugs against prostate cancer. In the present work, the random forest algorithm was used to conduct a QSAR study on 370 CYP17A1 ligands with different structures that were collected from the literature and databases, and a QSAR model was created based on the five important descriptors screened out – 2D adjacency and distance matrix descriptors, 2D atom counts and bond counts and 3D surface area, volume and shape descriptors. The model was verified by the test set (accuracy, specificity, sensitivity, F-measure, MCC, and AUC were calculated). It was revealed that the hydrophobic properties of the vdW surface of the ligand have a significant contribution to the activity prediction. The hydrophobic effect of the molecules may be aroused by the presence of the hydrophobic groups or aromatic rings in the molecules. The created QSAR model shows that the molecules with more aromatic rings have better activity. The accuracy of the model on the test set was 84%, precision – 81%, sensitivity – 93%, specificity – 72%, F-measure – 0.87, MCC – 0.67, AUC – 0.88. The model has good robustness and predictive ability and can be used to screen and discover new highly active CYP17A1 inhibitors.
KEY WORDS / КЉУЧНЕ РЕЧИ:
CYP17A1, inhibitor, QSAR, random forest.
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