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ABSTRACT: Three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis of large, flexible molecules, such as the dopamine reuptake inhibitor GBR 12909 (1), is complicated by the fact that they can take on a wide range of closely-related conformations. The first step in the analysis is to classify the conformers into groups. Over 600 conformers each of a piperazine (2) and piperidine (3) analog of 1 were generated by random search conformational analysis using the Merck Molecular Force Field (MMFF94). Singular value decomposition (SVD) was used to group the conformers of 2 and 3 by the similarity of their non-ring torsional angles. SVD uncovered subtle differences in their conformer populations due to that fact that the conformers separate along different principal components, and ultimately to the fact that different torsional angles are the chief contributors to these components. The results were compared to our previous SVD analysis (Fiorentino, et al., Journal of Computational Chemistry, 2006, 27, 609-620) of conformer populations of 2 and 3 generated by the Tripos force field and Gasteiger-Hückel charges. Except for the dominant contribution of angle B3 to principal component 8 seen with both force fields, the angles which are chiefly responsible for the grouping of the conformers of 2 and 3 are different with both force fields. This illustrates that SVD is useful in identifying unique groupings of conformers in large data sets of flexible molecules-a first step in selecting representative conformers for 3D-QSAR modeling studies.
Journal of Molecular Modeling 06/2011; 17(6):1343-51. · 1.80 Impact Factor
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ABSTRACT: Analogs of the flexible dopamine reuptake inhibitor, GBR 12909 (1), may have potential utility in the treatment of cocaine abuse. As a first step in the 3D-QSAR modeling of the dopamine transporter (DAT)/serotonin transporter (SERT) selectivity of these compounds, we carried out conformational analyses of two analogs of 1: a piperazine (2) and a related piperidine (3). Ensembles of conformers consisting of local minima on the potential energy surface of the molecule were generated in the vacuum phase and in implicit solvent by random search conformational analysis using the Tripos and MMFF94 force fields. Some differences were noted in the conformer populations due to differences in the treatment of the tertiary amine nitrogen and ether oxygen atom types by the force fields. The force fields also differed in their descriptions of internal rotation around the C(sp³)-O(sp³) bond proximal to the bisphenyl moiety. Molecular orbital calculations at the HF/6-31G(d) and B3LYP/6-31G(d) levels of C-O internal rotation in model compound (5), designed to model the effect of the proximity of the bisphenyl group on C-O internal rotation, showed a broad region of low energy between -60° to 60° with minima at both -60° and 30° and a low rotational barrier at 0°, in closer agreement with the MMFF94 results than the Tripos results. Molecular mechanics calculations on model compound (6) showed that the MMFF94 force field was much more sensitive than the Tripos force field to the effects of the bisphenyl moiety on C-O internal rotation.
Journal of Molecular Modeling 04/2010; 17(1):181-200. · 1.80 Impact Factor
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ABSTRACT: Analysis of large, flexible molecules, such as the dopamine reuptake inhibitor GBR 12909 (1), is complicated by the fact that they can take on a wide range of closely related conformations. The first step in the analysis is to classify the conformers into groups. Here, Singular Value Decomposition (SVD) was used to group conformations of GBR 12909 analogs by the similarity of their nonring torsional angles. The significance of the present work, the first application of SVD to the analysis of very flexible molecules, lies in the development of a novel scaling technique for circular data and in the grouping of molecular conformations using a technique that is independent of molecular alignment. Over 700 conformers each of a piperazine (2) and piperidine (3) analog of 1 were studied. Analysis of the score and loading plots showed that the conformers of 2 separate into three large groups due to torsional angles on the naphthalene side of the molecule, whereas those of 3 separate into nine groups due to torsional angles on the bisphenyl side of the molecule. These differences are due to nitrogen inversion at the unprotonated piperazinyl nitrogen of 2, which results in a different ensemble of conformers than those of 3, where no inversion is possible at the corresponding piperidinyl carbon.
Journal of Computational Chemistry 04/2006; 27(5):609-20. · 4.58 Impact Factor
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ABSTRACT: Virtual screening (VS), if applied appropriately, could significantly shorten the hit identification and hit-to-lead processes in drug discovery. Recently, the version of VS that is based upon similarity to a pharmacophore has received increased attention. This is due to two major factors: first, the public availability of the ZINC1 conformational database has provided a large selection pool with high-quality and purchasable small molecules; second, new technology has enabled a more accurate and flexible definition of pharmacophore models coupled with an efficient search speed. The major goal of this study was to achieve improved specificity and sensitivity of pharmacophore-based VS by optimizing the variables used to generate conformations of small molecules and those used to construct pharmacophore models from known inhibitors or from inhibitor-protein complex structures. By using human immunodeficiency virus protease and its inhibitors (PIs) as a case study, the impact of the key variables, including the selection of chemical features, involvement of excluded volumes (EV), the tolerance radius of excluded volumes, energy windows, and the maximum number of conformers in conformation generation, was explored. Protein flexibility was simulated by adjusting the sizes of EV. Our best pharmacophore model, combining both chemical features and excluded volumes, was able to correctly identify 60 out of 75 structurally diverse known PIs, while misclassifying only 5 out of 75 similar compounds that are not inhibitors. To evaluate the specificity of the model, 1193 oral drugs on the market were screened, and 25 original hits were identified, including 5 out of 6 known PI drugs.
Journal of Chemical Information and Modeling 46(3):1236-44. · 4.68 Impact Factor
