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Prediction of new bioactive molecules of chemical compound using boosting ensemble methods

Virtual screening (VS) methods can be categorized into structure-based virtual screening (SBVS) that involves knowledge about the target’s 3D structure and ligand-based virtual screening (LBVS) approaches that utilize information from at least one identified ligand. However, the activity prediction...

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Main Authors: Hashim, Haslinda, Saeed, Faisal
Format: Conference or Workshop Item
Published: 2017
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/97282/
http://dx.doi.org/10.1007/978-981-10-7242-0_22
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spelling my.utm.972822022-09-26T03:30:08Z http://eprints.utm.my/id/eprint/97282/ Prediction of new bioactive molecules of chemical compound using boosting ensemble methods Hashim, Haslinda Saeed, Faisal QA75 Electronic computers. Computer science Virtual screening (VS) methods can be categorized into structure-based virtual screening (SBVS) that involves knowledge about the target’s 3D structure and ligand-based virtual screening (LBVS) approaches that utilize information from at least one identified ligand. However, the activity prediction of new bioactive molecules in highly diverse data set is still less accurate and the result is not comprehensive enough since only one approach is applied at one time. This paper aims to recommend the boosting ensemble method, MultiBoost, into LBVS using the well-known chemoinformatics database, the MDL Drug Data Report (MDDR). The experimental results were compared with Support Vector Machines (SVM). The final outcomes showed that MultiBoost ensemble classifiers had improved the effectiveness of the prediction of new bioactive molecules in high diverse data. 2017 Conference or Workshop Item PeerReviewed Hashim, Haslinda and Saeed, Faisal (2017) Prediction of new bioactive molecules of chemical compound using boosting ensemble methods. In: 3rd International Conference on Soft Computing in Data Science, SCDS 2017, 27 - 28 November 2017, Yogyakarta, Indonesia. http://dx.doi.org/10.1007/978-981-10-7242-0_22
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Hashim, Haslinda
Saeed, Faisal
Prediction of new bioactive molecules of chemical compound using boosting ensemble methods
description Virtual screening (VS) methods can be categorized into structure-based virtual screening (SBVS) that involves knowledge about the target’s 3D structure and ligand-based virtual screening (LBVS) approaches that utilize information from at least one identified ligand. However, the activity prediction of new bioactive molecules in highly diverse data set is still less accurate and the result is not comprehensive enough since only one approach is applied at one time. This paper aims to recommend the boosting ensemble method, MultiBoost, into LBVS using the well-known chemoinformatics database, the MDL Drug Data Report (MDDR). The experimental results were compared with Support Vector Machines (SVM). The final outcomes showed that MultiBoost ensemble classifiers had improved the effectiveness of the prediction of new bioactive molecules in high diverse data.
format Conference or Workshop Item
author Hashim, Haslinda
Saeed, Faisal
author_facet Hashim, Haslinda
Saeed, Faisal
author_sort Hashim, Haslinda
title Prediction of new bioactive molecules of chemical compound using boosting ensemble methods
title_short Prediction of new bioactive molecules of chemical compound using boosting ensemble methods
title_full Prediction of new bioactive molecules of chemical compound using boosting ensemble methods
title_fullStr Prediction of new bioactive molecules of chemical compound using boosting ensemble methods
title_full_unstemmed Prediction of new bioactive molecules of chemical compound using boosting ensemble methods
title_sort prediction of new bioactive molecules of chemical compound using boosting ensemble methods
publishDate 2017
url http://eprints.utm.my/id/eprint/97282/
http://dx.doi.org/10.1007/978-981-10-7242-0_22
_version_ 1745562361701335040
score 13.160551

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