ArticleJournal of molecular modeling2007
Comparative QSAR studies on peptide deformylase inhibitors.
Article in Journal of molecular modeling, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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0 citing papers in PubMed, 10 citations in OpenAlex.
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8 authors at 4 institutions in 1 country.
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Abstract
Comparative quantitative structure-activity relationship (QSAR) analyses of peptide deformylase (PDF) inhibitors were performed with a series of previously published (British Biotech Pharmaceuticals, Oxford, UK) reverse hydroxamate derivatives having antibacterial activity against Escherichia coli PDF, using 2D and 3D QSAR methods, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR). Statistically reliable models with good predictive power were generated from all three methods (CoMFA r (2) = 0.957, q (2) = 0.569; CoMSIA r (2) = 0.924, q (2) = 0.520; HQSAR r (2) = 0.860, q (2) = 0.578). The predictive capability of these models was validated by a set of compounds that were not included in the training set. The models based on CoMFA and CoMSIA gave satisfactory predictive r (2) values of 0.687 and 0.505, respectively. The model derived from the HQSAR method showed a low predictability of 0.178 for the test set. In this study, 3D prediction models showed better predictive power than 2D models for the test set. This might be because 3D information is more important in the case of datasets containing compounds with similar skeletons. Superimposition of CoMFA contour maps in the active site of the PDF crystal structure showed a meaningful correlation between receptor-ligand binding and biological activity. The final QSAR models, along with information gathered from 3D contour and 2D contribution maps, could be useful for the design of novel active inhibitors of PDF.
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