http://rdf.ncbi.nlm.nih.gov/pubchem/patent/KR-101289322-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 | 2011-10-05-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2013-07-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2013-07-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | KR-101289322-B1 |
| titleOfInvention | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Magnetic Susceptibility of Pure Organic Compound |
| 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. It provides a mathematical model for predicting the magnetic susceptibility of organic compounds with high accuracy. The model described above is based on a number of multiplexes, in which some of the various molecular descriptors are independent variables and magnetic susceptibility is the dependent variable, for a plurality of organic compounds satisfying the above-mentioned conditions for which the experimental value of magnetic susceptibility is known. An artificial neural network that obtains the best of the multiple linear regression models by using a genetic algorithm and then outputs the magnetic susceptibility by inputting the values of molecular descriptors included in the model. A multiple linear regression-artificial neural network hybrid model that further improves predictive performance by constructing a network, is an example of a QSPR model, and any molecule can be identified if only the specific values of the molecular presenters included in the model are known. It predicts the magnetic susceptibility of a compound composed purely of molecules. As such, the present invention provides a method for predicting a reliable value of self-resistance even for a large number of organic compounds in the above-mentioned conditions, in which the experimental value is unknown, 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 | 2011-10-05-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 23.