J Chem Inf Model - Structural similarity based kriging for quantitative structure activity and property relationship modeling.


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Structurally similar molecules tend to have similar properties, i.e. closer molecules in the molecular space are more likely to yield similar property values while distant molecules are more likely to yield different values. Based on this principle, we propose the use of a new method that takes into account the high dimensionality of the molecular space, predicting chemical, physical, or biological properties based on the most similar compounds with measured properties. This methodology uses ordinary kriging coupled with three different molecular similarity approaches (based on molecular descriptors, fingerprints, and atom matching) which creates an interpolation map over the molecular space that is capable of predicting properties/activities for diverse chemical data sets. The proposed method was tested in two data sets of diverse chemical compounds collected from the literature and preprocessed. One of the data sets contained dihydrofolate reductase inhibition activity data, and the second molecules for which aqueous solubility was known. The overall predictive results using kriging for both data sets comply with the results obtained in the literature using typical QSPR/QSAR approaches. However, the procedure did not involve any type of descriptor selection or even minimal information about each problem, suggesting that this approach is directly applicable to a large spectrum of problems in QSAR/QSPR. Furthermore, the predictive results improve significantly with the similarity threshold between the training and testing compounds, allowing the definition of a confidence threshold of similarity and error estimation for each case inferred. The use of kriging for interpolation over the molecular metric space is independent of the training data set size, and no reparametrizations are necessary when more compounds are added or removed from the set, and increasing the size of the database will consequentially improve the quality of the estimations. Finally it is shown that this model can be used for checking the consistency of measured data and for guiding an extension of the training set by determining the regions of the molecular space for which new experimental measurements could be used to maximize the model's predictive performance.

Resumo Limpo

structur similar molecul tend similar properti ie closer molecul molecular space like yield similar properti valu distant molecul like yield differ valu base principl propos use new method take account high dimension molecular space predict chemic physic biolog properti base similar compound measur properti methodolog use ordinari krige coupl three differ molecular similar approach base molecular descriptor fingerprint atom match creat interpol map molecular space capabl predict propertiesact divers chemic data set propos method test two data set divers chemic compound collect literatur preprocess one data set contain dihydrofol reductas inhibit activ data second molecul aqueous solubl known overal predict result use krige data set compli result obtain literatur use typic qsprqsar approach howev procedur involv type descriptor select even minim inform problem suggest approach direct applic larg spectrum problem qsarqspr furthermor predict result improv signific similar threshold train test compound allow definit confid threshold similar error estim case infer use krige interpol molecular metric space independ train data set size reparametr necessari compound ad remov set increas size databas will consequenti improv qualiti estim final shown model can use check consist measur data guid extens train set determin region molecular space new experiment measur use maxim model predict perform

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