Azolium analogues as CDK4 inhibitors: Pharmacophore modeling, 3D QSAR study and new lead drug discovery

3D QSAR Pharmacophore Based Virtual Screening, ADME Analysis and Estimation of MM/GBSA binding Free Energies of Azoles as a Potential Inhibitor of DprE1 for Mycobacterium Tuberculosis

“…The model AHRRR 1 also recorded with high predictive correlation value of 0.5492 (Q 2 ), a good correlation coe cient value of 0.712(R 2 ) along a high Pearson R value of 0.8899 speci es that there is a correlation between actual and predicted biological activity. The difference between correlation coe cient R 2 and predictive correlation valueQ 2 is less than 0.3 (R 2 -Q 2 = 0.165), this low value suggested that the obtained model is accurate and reliable [50][51](Table 3). Figure 2 illustrated that the hydrogen bond acceptor A3 (pink color sphere) mapped with oxygen atom which is located between triazole and benzene ring, whereas the hydrophobic H4 (green color sphere) mapped with an atom at para position of benzene ring present on the fourth position of triazole.…”

A SAR and QSAR study on cyclin dependent kinase 4 inhibitors using machine learning methods

“…Computer-aided drug design (CADD) as a convenient and lowcost approach 21 is used to discover CDK4 inhibitor lead compounds. As a method of CADD, a classication model [22][23][24][25][26] is used for structure-activity relationship research and virtual screening of inhibitors, and a QSAR model [27][28][29][30][31][32][33][34][35][36][37][38][39] is used to predict the inhibitory activity of a compound. Wu et al 22 built classication models and QSAR models to study the structure-activity relationship of tyrosinase inhibitors.…”