Name
Title | ||
|---|---|---|
Neural Network Based Quantitative Structural Property Relations (QSPRs) for Predicting Boiling Points of Aliphatic Hydrocarbons. |
Abstract | ||
|---|---|---|
Quantitative structural property relations (QSPRs) for boiling points of aliphatic hydrocarbons were derived using a back-propagation neural network and a modified Fuzzy ARTMAP architecture. With the backpropagation model, the selected molecular descriptors were capable of distinguishing between diastereomers. The QSPRs were obtained from four valance molecular connectivity indices ((1)chi(v),(2)chi(v),(3)chi(v),(4)chi(v)), a second-order Kappa shape index ((2)kappa), dipole moment, and molecular weight. The inclusion of dipole moment proved to be particularly useful for distinguishing between cis and trans isomers. A back-propagation 7-4-1 architecture predicted boiling points for the test, validation, and overall data sets of alkanes with average absolute errors of 0.37% (1.65 K), 0.42% (1.73 K), and 0.37% (1.54 K), respectively. The error for the test and overall data sets decreased to 0.19% (0.81 K) and 0.31% (1.30 K), respectively, using the modified Fuzzy ARTMAP network. A back-propagation alkene model, with a 7-10-1 architecture, yielded predictions with average absolute errors for the test, validation, and overall data sets, of 1.96% (6.79 K), 1.83% (6.45 K), and 1.25% (4.42 K), respectively. Fuzzy ARTMAP reduced the errors for the test and overall data sets to 0.19% (0.73 K) and 0.25% (0.95 K), respectively. The back-propagation composite model for aliphatic hydrocarbons; with a 7-9-1 architecture, yielded boiling points with average absolute errors for the test, validation, and overall set of 1.74% (6.09 K), 1.25% (4.68 K), and 1.37% (4.85 K), respectively. The error for the test and overall data sets using the Fuzzy ARTMAP composite model decreased to 0.84% (1.15 K) and 0.35% (1.35 K), respectively. Performance of the QSPRs, developed from a simple set of molecular descriptors, displayed accuracy well within the range of expected experimental errors and of better accuracy than other regression analysis and neural network-based boiling points QSPRs previously reported in the literature. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1021/ci000442u | JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES |
Keywords | Field | DocType |
molecular descriptor,regression analysis,neural network,back propagation | Molecular descriptor,Boiling point,Kappa,Thermodynamics,Computational chemistry,Fuzzy logic,Cis–trans isomerism,Structural property,Artificial neural network,Statistics,Dipole,Mathematics | Journal |
Volume | Issue | ISSN |
40 | 3 | 0095-2338 |
Citations | PageRank | References |
8 | 1.02 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| G Espinosa | 1 | 39 | 6.67 |
| D Yaffe | 2 | 27 | 5.65 |
| Yoram Cohen | 3 | 17 | 2.97 |
| A Arenas | 4 | 623 | 38.38 |
| Francesc Giralt | 5 | 42 | 8.36 |