Neural networks and support vector machines based bio-activity classification
Classification of various compounds into their respective biological activity classes is important in drug discovery applications from an early phase virtual compound filtering and screening point of view. In this work two types of neural networks, multi layer perceptron (MLP) and radial basis funct...
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2006
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/270/1/JehanZebShah2006_Neuralnetworksandsupportvector.pdf |
| _version_ | 1796853056704348160 |
|---|---|
| author | Zeb Shah, Jehan Salim, Naomie |
| author_facet | Zeb Shah, Jehan Salim, Naomie |
| author_sort | Zeb Shah, Jehan |
| collection | ePrints |
| description | Classification of various compounds into their respective biological activity classes is important in drug discovery applications from an early phase virtual compound filtering and screening point of view. In this work two types of neural networks, multi layer perceptron (MLP) and radial basis functions (RBF), and support vector machines (SVM) were employed for the classification of three types of biologically active enzyme inhibitors. Both of the networks were trained with back propagation learning method with chemical compounds whose active inhibition properties were previously known. A group of topological indices, selected with the help of principle component analysis (PCA) were used as descriptors. The results of all the three classification methods show that the performance of both the neural networks is better than the SVM. |
| first_indexed | 2024-03-05T17:53:57Z |
| format | Conference or Workshop Item |
| id | utm.eprints-270 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T17:53:57Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | utm.eprints-2702011-06-30T07:30:21Z http://eprints.utm.my/270/ Neural networks and support vector machines based bio-activity classification Zeb Shah, Jehan Salim, Naomie TP Chemical technology Classification of various compounds into their respective biological activity classes is important in drug discovery applications from an early phase virtual compound filtering and screening point of view. In this work two types of neural networks, multi layer perceptron (MLP) and radial basis functions (RBF), and support vector machines (SVM) were employed for the classification of three types of biologically active enzyme inhibitors. Both of the networks were trained with back propagation learning method with chemical compounds whose active inhibition properties were previously known. A group of topological indices, selected with the help of principle component analysis (PCA) were used as descriptors. The results of all the three classification methods show that the performance of both the neural networks is better than the SVM. 2006-07 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/270/1/JehanZebShah2006_Neuralnetworksandsupportvector.pdf Zeb Shah, Jehan and Salim, Naomie (2006) Neural networks and support vector machines based bio-activity classification. In: 1st International Conference on Natural Resources Engineering & Technology 2006, 24-25th July 2006, Putrajaya, Malaysia. |
| spellingShingle | TP Chemical technology Zeb Shah, Jehan Salim, Naomie Neural networks and support vector machines based bio-activity classification |
| title | Neural networks and support vector machines based bio-activity classification
|
| title_full | Neural networks and support vector machines based bio-activity classification
|
| title_fullStr | Neural networks and support vector machines based bio-activity classification
|
| title_full_unstemmed | Neural networks and support vector machines based bio-activity classification
|
| title_short | Neural networks and support vector machines based bio-activity classification
|
| title_sort | neural networks and support vector machines based bio activity classification |
| topic | TP Chemical technology |
| url | http://eprints.utm.my/270/1/JehanZebShah2006_Neuralnetworksandsupportvector.pdf |
| work_keys_str_mv | AT zebshahjehan neuralnetworksandsupportvectormachinesbasedbioactivityclassification AT salimnaomie neuralnetworksandsupportvectormachinesbasedbioactivityclassification |