J Chem Inf Model - Pragmatic approaches to using computational methods to predict xenobiotic metabolism.

Tópicos

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Resumo

In this study the performance of a selection of computational models for the prediction of metabolites and/or sites of metabolism was investigated. These included models incorporated in the MetaPrint2D-React, Meteor, and SMARTCyp software. The algorithms were assessed using two data sets: one a homogeneous data set of 28 Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and paracetamol (DS1) and the second a diverse data set of 30 top-selling drugs (DS2). The prediction of metabolites for the diverse data set (DS2) was better than for the more homogeneous DS1 for each model, indicating that some areas of chemical space may be better represented than others in the data used to develop and train the models. The study also identified compounds for which none of the packages could predict metabolites, again indicating areas of chemical space where more information is needed. Pragmatic approaches to using metabolism prediction software have also been proposed based on the results described here. These approaches include using cutoff values instead of restrictive reasoning settings in Meteor to reduce the output with little loss of sensitivity and for directing metabolite prediction by preselection based on likely sites of metabolism.

Resumo Limpo

studi perform select comput model predict metabolit andor site metabol investig includ model incorpor metaprintdreact meteor smartcyp softwar algorithm assess use two data set one homogen data set nonsteroid antiinflammatori drug nsaid paracetamol ds second divers data set topsel drug ds predict metabolit divers data set ds better homogen ds model indic area chemic space may better repres other data use develop train model studi also identifi compound none packag predict metabolit indic area chemic space inform need pragmat approach use metabol predict softwar also propos base result describ approach includ use cutoff valu instead restrict reason set meteor reduc output littl loss sensit direct metabolit predict preselect base like site metabol

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