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• W2014516257 abstract "Quantitative structure–activity relationship ( QSAR ) modeling was performed for imidazo[1,5‐a]pyrido[3,2‐e]pyrazines, which constitute a class of phosphodiesterase 10A inhibitors. Particle swarm optimization ( PSO ) and genetic algorithm ( GA ) were used as feature selection techniques to find the most reliable molecular descriptors from a large pool. Modeling of the relationship between the selected descriptors and the pIC 50 activity data was achieved by linear [multiple linear regression ( MLR )] and non‐linear [locally weighted regression ( LWR ) based on both Euclidean ( E ) and Mahalanobis ( M ) distances] methods. In addition, a stepwise MLR model was built using only a limited number of quantum chemical descriptors, selected because of their correlation with the pIC 50 . The model was not found interesting. It was concluded that the LWR model, based on the Euclidean distance, applied on the descriptors selected by PSO has the best prediction ability. However, some other models behaved similarly. The root‐mean‐squared errors of prediction ( RMSEP ) for the test sets obtained by PSO / MLR , GA / MLR , PSO / LWRE , PSO / LWRM , GA / LWRE , and GA / LWRM models were 0.333, 0.394, 0.313, 0.333, 0.421, and 0.424, respectively. The PSO ‐selected descriptors resulted in the best prediction models, both linear and non‐linear." @default.
• W2014516257 created "2016-06-24" @default.
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• W2014516257 date "2013-09-19" @default.
• W2014516257 modified "2023-10-17" @default.
• W2014516257 title "Particle swarm optimization and genetic algorithm as feature selection techniques for the QSAR modeling of imidazo[1,5-a]pyrido[3,2-e]pyrazines, inhibitors of phosphodiesterase 10A" @default.
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• W2014516257 doi "https://doi.org/10.1111/cbdd.12196" @default.
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