| dc.contributor.author |
Kannan, Balakrishnan |
|
| dc.contributor.author |
Rafidha Rahiman, K A |
|
| dc.contributor.author |
Sherly, K B |
|
| dc.date.accessioned |
2014-07-23T04:18:24Z |
|
| dc.date.available |
2014-07-23T04:18:24Z |
|
| dc.date.issued |
2011 |
|
| dc.identifier.uri |
http://dyuthi.cusat.ac.in/purl/4235 |
|
| dc.description |
PROCEEDINGS OF ICETECT 2011 |
en_US |
| dc.description.abstract |
In our study we use a kernel based classification
technique, Support Vector Machine Regression for predicting the
Melting Point of Drug – like compounds in terms of Topological
Descriptors, Topological Charge Indices, Connectivity Indices
and 2D Auto Correlations. The Machine Learning model was
designed, trained and tested using a dataset of 100 compounds
and it was found that an SVMReg model with RBF Kernel could
predict the Melting Point with a mean absolute error 15.5854 and
Root Mean Squared Error 19.7576 |
en_US |
| dc.description.sponsorship |
Cochin University of Science & Technology |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
IEEE |
en_US |
| dc.subject |
Data Mining |
en_US |
| dc.subject |
Machine Learning |
en_US |
| dc.subject |
Support Vector Machine |
en_US |
| dc.subject |
QSA |
en_US |
| dc.subject |
Melting Point. |
en_US |
| dc.title |
Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds |
en_US |
| dc.type |
Article |
en_US |