J Chem Inf Model - Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Tópicos

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{ method(2212) result(1239) propos(1039) }
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Resumo

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Resumo Limpo

metabol xenobiot remain central challeng discoveri develop drug cosmet nutrit supplement agrochem metabol transform frequent relat incid toxic effect may result emerg reactiv speci system accumul metabolit induct metabol pathway experiment investig metabol small organ molecul particular resourc demand henc comput method consider interest complement experiment approach review provid broad overview structur ligandbas comput method predict xenobiot metabol current comput approach address xenobiot metabol discuss three major perspect predict site metabol som ii elucid potenti metabolit chemic structur iii predict direct indirect effect xenobiot metabol enzym focus cytochrom p cyp superfamili enzym cardin xenobiot metabol enzym domain varieti approach applic systemat review includ expert system data mine approach quantit structureact relationship qsar machin learningbas method pharmacophorebas algorithm shapefocus techniqu molecular interact field mif reactivityfocus techniqu proteinligand dock molecular dynam md simul combin method predict metabol develop area still enorm potenti improv howev clear combin rapid increas amount avail ligand structurerel experiment data particular quantit data novel divers simul model approach acceler develop effect tool predict vivo metabol reflect divers comprehens data sourc method metabol predict review review attempt survey rang scope comput method appli metabol predict also compar contrast applic perform

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