[44] [46] [46]-1.9 -1.5 -1.5 -2.4 -1.Int. J. Mol. Sci. 2021, 22,6 ofTable 1. Cont.
[44] [46] [46]-1.9 -1.five -1.5 -2.4 -1.Int. J. Mol. Sci. 2021, 22,6 ofTable 1. Cont.Benzene Phosphate Derivatives (Class C)Comp. No. C1 C2 CR2 PO3 -2 PO-R2 — PO-R3 PO3 -2 — –R4 PO3 -2 PO-R4 — PO-R5 –PO-R5 PO3 -2 PO-R6 PO3 -2 — –Key Name BiPh(two,3 ,4,5 ,six)P5 BiPh(2,2 4,4 ,five,five )P6 1,two,4-Dimer Biph(two,two ,four,four ,five,5 )PIC50 ( ) 0.42 0.19 0.logPclogPpIC50 six.three six.7 six.LipE 14.9 17.two 14.Ref. [47] [47] [47]-1.two -2.8 -3.-4.two -6.1 -8.PO3 -PO3 -PO3 -PO3 -PO3 -PO3 -Int. J. Mol. Sci. 2021, 22,7 ofBy careful inspection of the activity landscape with the information, the activity threshold was defined as 160 (Table S1). The inhibitory potencies (IC50 ) of most actives in the dataset ranged from 0.0029 to 160 , whereas inhibitory potency (IC50 ) of least actives was PI3Kα Inhibitor web inside the array of 340 to 20,000 . The LipE values of the dataset were mAChR4 Antagonist Accession calculated ranging from -2.four to 17.two. The physicochemical properties with the dataset are illustrated in Figure S1. 2.two. Pharmacophore Model Generation and Validation Previously, distinct studies proposed that a array of clogP values among two.0 and three.0 in mixture with lipophilic efficiency (LipE) values greater than five.0 are optimal for an typical oral drug [481]. By this criterion, ryanodine (IC50 : 0.055 ) using a clogP value of 2.71 and LipE worth of four.6 (Table S1) was selected as a template for the pharmacophore modeling (Figure 2). A lipophilic efficacy graph among clogP versus pIC50 is offered in Figure S2.Figure 2. The 3D molecular structure of ryanodine (template) molecule.Briefly, to produce ligand-based pharmacophore models, ryanodine was selected as a template molecule. The chemical functions inside the template, e.g., the charged interactions, lipophilic regions, hydrogen-bond acceptor and donor interactions, and steric exclusions, have been detected as crucial pharmacophoric functions. Thus, 10 pharmacophore models had been generated by utilizing the radial distribution function (RDF) code algorithm [52]. As soon as models were generated, each model was validated internally by performing the pairing in between pharmacophoric attributes from the template molecule along with the rest of your data to make geometric transformations primarily based upon minimal squared distance deviations [53]. The generated models together with the chemical features, the distances within these attributes, and the statistical parameters to validate each and every model are shown in Table two.Int. J. Mol. Sci. 2021, 22,eight ofTable 2. The identified pharmacophoric features and mutual distances (A), along with ligand scout score and statistical evaluation parameters. Model No. Pharmacophore Model (Template) Model Score Hyd Hyd HBA1 1. 0.68 HBA2 HBD1 HBD2 0 two.62 4.79 5.56 7.68 Hyd Hyd HBA1 2. 0.67 HBD1 HBD2 HBD3 0 2.48 3.46 5.56 7.43 Hyd Hyd HBA three. 0.66 HBD1 HBD2 HBD3 0 three.95 3.97 7.09 7.29 0 3.87 four.13 3.41 0 two.86 7.01 0 2.62 0 TP: TN: FP: FN: MCC: 72 29 12 33 0.02 0 four.17 3.63 five.58 HBA 0 6.33 7.8 HBD1 0 7.01 HBD2 0 HBD3 0 2.61 3.64 5.58 HBA1 0 four.57 3.11 HBD1 0 six.97 HBD2 0 HBD3 TP: TN: FP: FN: MCC: 51 70 14 18 0.26 TP: TN: FP: FN: MCC: 87 72 06 03 0.76 Model Distance HBA1 HBA2 HBD1 HBD2 Model StatisticsInt. J. Mol. Sci. 2021, 22,9 ofTable two. Cont. Model No. Pharmacophore Model (Template) Model Score Hyd Hyd HBA four. 0.65 HBD1 HBD2 Hyd 0 two.32 3.19 7.69 6.22 Hyd 0 two.32 four.56 two.92 7.06 Hyd Hyd HBA1 6. 0.63 HBA2 HBD1 HBD2 0 4.32 four.46 six.87 4.42 0 2.21 three.07 six.05 0 five.73 5.04 0 9.61 0 TP: TN: FP: FN: MCC: 60 29 57 45 -0.07 0 1.62 6.91 4.41 HBA 0 3.01 1.05 5.09 HBA1 0 3.61 7.53 HBA2 0 5.28 HBD1.