http://rdf.ncbi.nlm.nih.gov/pubchem/patent/KR-101325101-B1
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-12 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F19-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-12 |
| filingDate | 2011-10-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2013-11-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2013-11-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | KR-101325101-B1 |
| titleOfInvention | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Eccentric Factors of Pure Organic Compounds |
| abstract | The present invention consists of up to five elements such as hydrogen (H), carbon (C), nitrogen (N), oxygen (O), and sulfur (S), and is composed of pure molecules consisting of up to 25 atoms except hydrogen. A mathematical model for predicting the eccentric factors of organic compounds with high accuracy is provided. The model described above is based on a number of multiplexes, with several of the molecular descriptors as independent variables and eccentric factors as dependent variables, for a number of organic compounds satisfying the above conditions for which the experimental values of eccentric factors are known. An artificial neural network that obtains the best of the multiple linear regression models by using a genetic algorithm, and then outputs an eccentric factor by inputting the values of molecular descriptors included in the model. It is an example of a quantitative structure-property relationship (QSPR) model, which is a hybrid model of multiple linear regression-artificial neural network that further improves predictive performance by constructing a network. Any molecule predicts the eccentricity of a compound composed purely of this molecule. As such, the present invention provides a method for predicting the value of reliable eccentric factors even for a large number of organic compounds in the above-mentioned conditions where the experimental values are not known, thereby saving the cost and time required for the experiment, and thus promoting R & D activities in related industries. It produces effects such as facilitating. |
| priorityDate | 2011-10-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 26.