Breast cancer is a common kind of cancer affecting women with a fatal outcome. Due to extensive treatment cycles, breast cancer resistance has now become a worldwide issue. Therefore, the only realistic treatment is the rapid development anti-breast cancer medications. To improve and propose new anti-breast cancer drugs, three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking studies on thioquinazolinone derivatives with aromatase enzyme (PDB: 3S7S) were attempted. Comparative Molecular Similarity Indices Analysis (CoMSIA) was utilized to develop the 3D-QSAR model in this study. The best CoMSIA model (with considerable values of Q2, R2 and R2pred) was also utilized in an effort to get the high predictability. External validation that uses a test set has been utilized to validate the predictive ability of the fitted model. According to the findings, the Electrostatic, Hydrophobic, Hydrogen Bond Donor and Acceptor fields had a serious influence on anti-breast cancer activities. Thus, we designed a variety of novel effective aromatase inhibitors based on prior findings and predicted their inhibitory activities using the best model. Moreover, ADMET investigations were employed to analyze the pharmacokinetic properties of drug-candidates.
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In silico drug discovery of new anti-breast cancer inhibitors based on 3D-QSAR, molecular docking and ADMET investigation
Published: 01 November 2022 by MDPI in 8th International Electronic Conference on Medicinal Chemistry session Emerging technologies in drug discovery
Keywords: Breast cancer; Thioquinazolinone derivatives; QSAR; Molecular docking; ADMET.