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note
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PHASE procedure. Six built-in types of pharmacophoric features: hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic (H), negative ionizable (N), positive ionizable (P), and aromatic ring (R). Ligands were processed with the LigPrep program to assign protonation states appropriate for pH 7.0. Conformer generation was carried out with the MacroModel. Potentials were computed using the OPLS2005 force field. The default pharmacophore feature definitions were used in site generation. After the identification of the sites hypotheses were generated by a systematic variation of the number of sites. The number of matching active compounds was kept default of 39 (entire training set). All the molecules were considered as active no active/inactive threshold were assigned. The scoring was done using: Survival score formula = (Vector score) + (Site score) + (Volume score) + (Selectivity score) + (Number of matches-1)-(Reference ligand relative conformational energy) + (Ref ligand activity). Max intersite distance=2.00, initial and final box size is 20 and 1, respectively. The best hypothesis was chosen based on the survival score by PHASE. The top scored hypothesis was then used to build pharmacophore-based 3D QSAR models with 3 PLS factors and Grid spacing = 1.
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18
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79956074492
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note
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9), δ 8.67(s, 1H, CH), δ 9.25(s, 1H, NH), δ 10.52s, 1H, NH), δ 11.32 (s, 1H, OH).
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19
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