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Two sets of heterogeneous organic compounds were analyzed with artificial neural networks using atom type electrotopological state indices. The first set contains the boiling point for 298 compounds; 30 were placed in a testing set. The neural network model used atom type E-state indices for the 19 atom types present in the data set; the actual network used for prediction had a 19:5:1 architecture. This model produced a mean absolute error (MAE) of 3.93 K for the overall set, 3.86 for the training set, and 4.57 for the test set. The average relative percent error for 10 runs is 0.94% for the whole data set and 1.12% for the test set. The second set contains critical temperatures for 165 compounds; 18 were placed in the testing set. The neural network possessed a 19:4:1 architecture and produced an MAE of 4.52 K for the whole set, 4.39 K for the training set, and 5.59 K for the test set. The average relative percent error for 5 runs is 0.77% for the whole data set and 0.95% for the test set. |
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