J Chem Inf Model - Exploring uncharted territories: predicting activity cliffs in structure-activity landscapes.

Tópicos

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Resumo

The notion of activity cliffs is an intuitive approach to characterizing structural features that play a key role in modulating biological activity of a molecule. A variety of methods have been described to quantitatively characterize activity cliffs, such as SALI and SARI. However, these methods are primarily retrospective in nature; highlighting cliffs that are already present in the data set. The current study focuses on employing a pairwise characterization of a data set to train a model to predict whether a new molecule will exhibit an activity cliff with one or more members of the data set. The approach is based on predicting a value for pairs of objects rather than the individual objects themselves (and thus allows for robust models even for small structure-activity relationship data sets). We extracted structure-activity data for several ChEMBL assays and developed random forest models to predict SALI values, from pairwise combinations of molecular descriptors. The models exhibited reasonable RMSE's though, surprisingly, performance on the more significant cliffs tended to be better than on the lesser ones. While the models do not exhibit very high levels of accuracy, our results indicate that they are able to prioritize molecules in terms of their ability to activity cliffs, thus serving as a tool to prospectively identify activity cliffs.

Resumo Limpo

notion activ cliff intuit approach character structur featur play key role modul biolog activ molecul varieti method describ quantit character activ cliff sali sari howev method primarili retrospect natur highlight cliff alreadi present data set current studi focus employ pairwis character data set train model predict whether new molecul will exhibit activ cliff one member data set approach base predict valu pair object rather individu object thus allow robust model even small structureact relationship data set extract structureact data sever chembl assay develop random forest model predict sali valu pairwis combin molecular descriptor model exhibit reason rmses though surpris perform signific cliff tend better lesser one model exhibit high level accuraci result indic abl priorit molecul term abil activ cliff thus serv tool prospect identifi activ cliff

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