An activity prediction model using shape-based descriptor method
Similarity searching, the activity of an unknown compound (target) is predicted through the comparison of an unknown compound with a set of known activities of compounds. The known activities of the most similar compounds are assigned to the unknown compound. Different machine learning methods and M...
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| 主要な著者: | , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Penerbit UTM Press
2016
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| 主題: | |
| オンライン･アクセス: | http://eprints.utm.my/id/eprint/71283/1/HentabliHamza2016_Anactivitypredictionmodelusing.pdf http://eprints.utm.my/id/eprint/71283/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84976518253&doi=10.11113%2fjt.v78.9245&partnerID=40&md5=e69504b98ed73418a720d5cc04702c7c |
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| 要約: | Similarity searching, the activity of an unknown compound (target) is predicted through the comparison of an unknown compound with a set of known activities of compounds. The known activities of the most similar compounds are assigned to the unknown compound. Different machine learning methods and Multilevel Neighborhoods of Atoms (MNA) structure descriptors have been applied for the activities prediction. In this paper, we introduced a new activity prediction model with Shape-Based Descriptor Method (SBDM). Experimental results show that SBDM-MNA provides a useful method of using the prior knowledge of target class information (active and inactive compounds) of predicting the activity of orphan compounds. To validate our method, we have applied the SBDM-MNA to different established data sets from literature and compare its performance with the classical MNA descriptor for activity prediction. |
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