Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison
Clustering of chemical databases has tremendous significance in the process of compound selection, virtual screening and in the drug designing and discovery process as a whole. Traditionally, hierarchical methods like Ward’s and Group Average (Gave) and nonhierarchical methods like Jarvis Patrick’s...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2006
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| Online Access: | http://eprints.utm.my/7493/1/NaomiSalim2006_ClusteringofChemicalCompoundsusingUnsupervised.pdf |
| _version_ | 1825910045836050432 |
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| author | Zeb Shah, Jehan Salim, Naomie |
| author_facet | Zeb Shah, Jehan Salim, Naomie |
| author_sort | Zeb Shah, Jehan |
| collection | ePrints |
| description | Clustering of chemical databases has tremendous significance in the process of compound selection, virtual screening and in the drug designing and discovery process as a whole. Traditionally, hierarchical methods like Ward’s and Group Average (Gave) and nonhierarchical methods like Jarvis Patrick’s and k-means are preferred methods to cluster a diverse set of compounds for a number of drug targets (using fingerprints based descriptors). In this work the applications of a number of self-organizing map (SOM) neural network algorithms to the clustering of chemical datasets are investigated. The results of the SOM neural networks, Wards and Group-Average methods are evaluated for the clustering of different biologically active chemical molecules that can be used as drug like compounds based on topological descriptors. The results show that the Wards and Group Average methods are equally good; however, the performance of Kohonen neural selforganizing maps (SOM) is also important due to its almost similar performance as the hierarchical clustering methods with the advantage of its efficiency |
| first_indexed | 2024-03-05T18:11:14Z |
| format | Conference or Workshop Item |
| id | utm.eprints-7493 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:11:14Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | utm.eprints-74932010-10-11T08:07:00Z http://eprints.utm.my/7493/ Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison Zeb Shah, Jehan Salim, Naomie QA75 Electronic computers. Computer science Clustering of chemical databases has tremendous significance in the process of compound selection, virtual screening and in the drug designing and discovery process as a whole. Traditionally, hierarchical methods like Ward’s and Group Average (Gave) and nonhierarchical methods like Jarvis Patrick’s and k-means are preferred methods to cluster a diverse set of compounds for a number of drug targets (using fingerprints based descriptors). In this work the applications of a number of self-organizing map (SOM) neural network algorithms to the clustering of chemical datasets are investigated. The results of the SOM neural networks, Wards and Group-Average methods are evaluated for the clustering of different biologically active chemical molecules that can be used as drug like compounds based on topological descriptors. The results show that the Wards and Group Average methods are equally good; however, the performance of Kohonen neural selforganizing maps (SOM) is also important due to its almost similar performance as the hierarchical clustering methods with the advantage of its efficiency 2006-05 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/7493/1/NaomiSalim2006_ClusteringofChemicalCompoundsusingUnsupervised.pdf Zeb Shah, Jehan and Salim, Naomie (2006) Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison. In: Postgraduate Annual Research Seminar 2006 (PARS 2006), 24 - 25 May 2006, Postgraduate Studies Department FSKSM, UTM Skudai. |
| spellingShingle | QA75 Electronic computers. Computer science Zeb Shah, Jehan Salim, Naomie Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison |
| title | Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison |
| title_full | Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison |
| title_fullStr | Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison |
| title_full_unstemmed | Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison |
| title_short | Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison |
| title_sort | clustering of chemical compounds using unsupervised neural networks algorithms a comparison |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/7493/1/NaomiSalim2006_ClusteringofChemicalCompoundsusingUnsupervised.pdf |
| work_keys_str_mv | AT zebshahjehan clusteringofchemicalcompoundsusingunsupervisedneuralnetworksalgorithmsacomparison AT salimnaomie clusteringofchemicalcompoundsusingunsupervisedneuralnetworksalgorithmsacomparison |