J Chem Inf Model - ??C NMR-distance matrix descriptors: optimal abstract 3D space granularity for predicting estrogen binding.

Tópicos

{ compound(1573) activ(1297) structur(1058) }
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{ bind(1733) structur(1185) ligand(1036) }
{ data(3963) clinic(1234) research(1004) }
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{ ehr(2073) health(1662) electron(1139) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

An improved three-dimensional quantitative spectral data-activity relationship (3D-QSDAR) methodology was used to build and validate models relating the activity of 130 estrogen receptor binders to specific structural features. In 3D-QSDAR, each compound is represented by a unique fingerprint constructed from (13)C chemical shift pairs and associated interatomic distances. Grids of different granularity can be used to partition the abstract fingerprint space into congruent "bins" for which the optimal size was previously unexplored. For this purpose, the endocrine disruptor knowledge base data were used to generate 50 3D-QSDAR models with bins ranging in size from 2 ppm ? 2 ppm ? 0.5 ? to 20 ppm ? 20 ppm ? 2.5 ?, each of which was validated using 100 training/test set partitions. Best average predictivity in terms of R(2)test was achieved at 10 ppm ?10 ppm ? Z ? (Z = 0.5, ..., 2.5 ?). It was hypothesized that this optimum depends on the chemical shifts' estimation error (?4.13 ppm) and the precision of the calculated interatomic distances. The highest ranked bins from partial least-squares weights were found to be associated with structural features known to be essential for binding to the estrogen receptor.

Resumo Limpo

improv threedimension quantit spectral dataact relationship dqsdar methodolog use build valid model relat activ estrogen receptor binder specif structur featur dqsdar compound repres uniqu fingerprint construct c chemic shift pair associ interatom distanc grid differ granular can use partit abstract fingerprint space congruent bin optim size previous unexplor purpos endocrin disruptor knowledg base data use generat dqsdar model bin rang size ppm ppm ppm ppm valid use trainingtest set partit best averag predict term rtest achiev ppm ppm z z hypothes optimum depend chemic shift estim error ppm precis calcul interatom distanc highest rank bin partial leastsquar weight found associ structur featur known essenti bind estrogen receptor

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