J Chem Inf Model - Quantitative structure-activity relationship models of chemical transformations from matched pairs analyses.

Tópicos

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Resumo

The concepts of activity cliffs and matched molecular pairs (MMP) are recent paradigms for analysis of data sets to identify structural changes that may be used to modify the potency of lead molecules in drug discovery projects. Analysis of MMPs was recently demonstrated as a feasible technique for quantitative structure-activity relationship (QSAR) modeling of prospective compounds. Although within a small data set, the lack of matched pairs, and the lack of knowledge about specific chemical transformations limit prospective applications. Here we present an alternative technique that determines pairwise descriptors for each matched pair and then uses a QSAR model to estimate the activity change associated with a chemical transformation. The descriptors effectively group similar transformations and incorporate information about the transformation and its local environment. Use of a transformation QSAR model allows one to estimate the activity change for novel transformations and therefore returns predictions for a larger fraction of test set compounds. Application of the proposed methodology to four public data sets results in increased model performance over a benchmark random forest and direct application of chemical transformations using QSAR-by-matched molecular pairs analysis (QSAR-by-MMPA).

Resumo Limpo

concept activ cliff match molecular pair mmp recent paradigm analysi data set identifi structur chang may use modifi potenc lead molecul drug discoveri project analysi mmps recent demonstr feasibl techniqu quantit structureact relationship qsar model prospect compound although within small data set lack match pair lack knowledg specif chemic transform limit prospect applic present altern techniqu determin pairwis descriptor match pair use qsar model estim activ chang associ chemic transform descriptor effect group similar transform incorpor inform transform local environ use transform qsar model allow one estim activ chang novel transform therefor return predict larger fraction test set compound applic propos methodolog four public data set result increas model perform benchmark random forest direct applic chemic transform use qsarbymatch molecular pair analysi qsarbymmpa

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