Discovery of new MurF inhibitors via pharmacophore modeling and QSAR analysis followed by in-silico screening

Discovery of DPP IV Inhibitors by Pharmacophore Modeling and QSAR Analysis followed by in silico Screening

“…We recently reported similar strategies for the discovery novel lead inhibitors against GSK-3b, [46] pseudomonal quorum sensing, [47] h-PTP 1B, [48] and bacterial MurF enzyme. [49] Results and Discussion CATALYST-HYPOGEN models drug-receptor interaction using information derived from ligand structures. [45,[50][51][52][53][54][55][56][57] HYPOGEN identifies a 3D array of a maximum of five chemical features common to active training molecules, which provides a relative alignment for each input molecule consistent with their binding to a proposed common receptor site.…”

Elaborate ligand-based modeling reveals new human neutrophil elastase inhibitors

“…Despite that pharmacophoric hypotheses provide excellent insights into ligand-macromolecule recognition and can be used to mine for new biologically interesting scaffolds, their predictive value as 3D-QSAR models is limited by steric shielding and bioactivity-enhancing or -reducing auxiliary groups (Taha et al, , 2008aAl-Masri et al, 2008;Taha et al, 2008c;Al-Nadaf et al, 2010;Abu Hammad and Taha, 2009;Abu Khalaf et al, 2010;AlSha'er and Taha, 2010a, b;Habash and Taha, 2011). This point combined with the fact that pharmacophore modeling of HNE inhibitors furnished several binding hypotheses of comparable success criteria prompted us to employ classical QSAR analysis to search for the best combination of pharmacophore(s) and other 2D descriptors capable of explaining bioactivity variation across the whole list of collected inhibitors (1-115, Table A in Supplementary Materials).…”

“…We employed genetic function approximation (GFA) and MLR QSAR (GFA-MLR-QSAR) analysis to search for an optimal QSAR equation(s). The fit values obtained by mapping representative hypotheses (56 models) against collected HNE inhibitors (1-115, Table A in Supplementary Materials) were enrolled, together with around 100 other physicochemical descriptors, as independent variables (genes) in GFA-MLR-QSAR analysis (see ''QSAR modeling'' section in experimental) (Abu Hammad and Taha, 2009;Abu Khalaf et al, 2010;Al-Masri et al, 2008;Al-Nadaf et al, 2010;AlSha'er and Taha, 2010a;Bersuker et al, 2000;Habash and Taha, 2011;Taha et al, , 2008aTaha et al, , b, 2008c. However, since it is essential to access the predictive power of the resulting QSAR models on an external set of inhibitors, we randomly selected 23 molecules and employed them as external test molecules for validating the QSAR models (r PRESS 2 ).…”

Mixing Pharmacophore Modeling and Classical QSAR Analysis as Powerful Tool for Lead Discovery

“…CATALYST pharmacophores have been used as 3D queries for database searching and in 3D-QSAR studies. [54][55][56][57][58][59][60][61][62][63][64][65][66] Although pharmacophore modeling employing HYPOGEN has been heavily reviewed in the literature, [68][69][70][71][72][73][74][75][76] a brief discussion of this algorithm is provided herein to allow better readability of the chapter.…”

“…We previously reported the successful use of this combination to probe the induced fit flexibilities of activated factor X 53 and towards the discovery of new inhibitory leads against glycogen synthase kinase-3β, 54 bacterial MurF, 55 protein tyrosine phosphatase, 56 DPP IV, 57 hormone sensitive lipase, 58 β-secretase, 59 influenza neuraminidase, 60 cholesteryl ester transfer protein, 61 cycline dependent kinase, 62 Heat shock protein, 63 estrogen receptor β, 64 β-D-Glucosidase, 65 and β-D-Galactosidase. 66 The author intends in this chapter to discuss the basic theoretical principles of this successful ligand-based approach and to provide interested audiences with experimental details related to this approach.…”