J Chem Inf Model - Dependence of QSAR models on the selection of trial descriptor sets: a demonstration using nanotoxicity endpoints of decorated nanotubes.

Tópicos

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Resumo

Little attention has been given to the selection of trial descriptor sets when designing a QSAR analysis even though a great number of descriptor classes, and often a greater number of descriptors within a given class, are now available. This paper reports an effort to explore interrelationships between QSAR models and descriptor sets. Zhou and co-workers (Zhou et al., Nano Lett. 2008, 8 (3), 859-865) designed, synthesized, and tested a combinatorial library of 80 surface modified, that is decorated, multi-walled carbon nanotubes for their composite nanotoxicity using six endpoints all based on a common 0 to 100 activity scale. Each of the six endpoints for the 29 most nanotoxic decorated nanotubes were incorporated as the training set for this study. The study reported here includes trial descriptor sets for all possible combinations of MOE, VolSurf, and 4D-fingerprints (FP) descriptor classes, as well as including and excluding explicit spatial contributions from the nanotube. Optimized QSAR models were constructed from these multiple trial descriptor sets. It was found that (a) both the form and quality of the best QSAR models for each of the endpoints are distinct and (b) some endpoints are quite dependent upon 4D-FP descriptors of the entire nanotube-decorator complex. However, other endpoints yielded equally good models only using decorator descriptors with and without the decorator-only 4D-FP descriptors. Lastly, and most importantly, the quality, significance, and interpretation of a QSAR model were found to be critically dependent on the trial descriptor sets used within a given QSAR endpoint study.

Resumo Limpo

littl attent given select trial descriptor set design qsar analysi even though great number descriptor class often greater number descriptor within given class now avail paper report effort explor interrelationship qsar model descriptor set zhou cowork zhou et al nano lett design synthes test combinatori librari surfac modifi decor multiwal carbon nanotub composit nanotox use six endpoint base common activ scale six endpoint nanotox decor nanotub incorpor train set studi studi report includ trial descriptor set possibl combin moe volsurf dfingerprint fp descriptor class well includ exclud explicit spatial contribut nanotub optim qsar model construct multipl trial descriptor set found form qualiti best qsar model endpoint distinct b endpoint quit depend upon dfp descriptor entir nanotubedecor complex howev endpoint yield equal good model use decor descriptor without decoratoron dfp descriptor last import qualiti signific interpret qsar model found critic depend trial descriptor set use within given qsar endpoint studi

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