期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 48, 期 3, 页码 550-555出版社
AMER CHEMICAL SOC
DOI: 10.1021/ci700363r
关键词
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The validation of the performance of a neural network based C-13 NMR prediction algorithm using a test set available from an open source publicly available database, NMRShiftDB, is described. The validation was performed using a version of the database containing ca. 214 000 chemical shifts as well as for two subsets of the database to compare performance when overlap with the training set is taken into account. The first subset contained ca. 93 000 chemical shifts that were absent from the ACD\CNMR DB, the excluded shift set used for training of the neural network and the ACD\CNMR prediction algorithm, while the second contained ca. 121000 shifts that were present in the ACD\CNMR DB training set, the included shift set. This work has shown that the mean error between experimental and predicted shifts for the entire database is 1.59 ppm, while the mean deviation for the subset with included shifts is 1.47 and 1.74 ppm for excluded shifts. Since similar work has been reported online for another algorithm we compared the results with the errors determined using Robien's CNMR Neural Network Predictor using the entire NMRShiftDB for program validation.
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