Article (Scientific journals)
Prediction of new bioactive molecules using a Bayesian belief network.
Abdo, Ammar; Leclere, Valerie; Jacques, Philippe et al.
2014In Journal of Chemical Information and Modeling, 54 (1), p. 30-6
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Abstract :
[en] Natural products and synthetic compounds are a valuable source of new small molecules leading to novel drugs to cure diseases. However identifying new biologically active small molecules is still a challenge. In this paper, we introduce a new activity prediction approach using Bayesian belief network for classification (BBNC). The roots of the network are the fragments composing a compound. The leaves are, on one side, the activities to predict and, on another side, the unknown compound. The activities are represented by sets of known compounds, and sets of inactive compounds are also used. We calculated a similarity between an unknown compound and each activity class. The more similar activity is assigned to the unknown compound. We applied this new approach on eight well-known data sets extracted from the literature and compared its performance to three classical machine learning algorithms. Experiments showed that BBNC provides interesting prediction rates (from 79% accuracy for high diverse data sets to 99% for low diverse ones) with a short time calculation. Experiments also showed that BBNC is particularly effective for homogeneous data sets but has been found to perform less well with structurally heterogeneous sets. However, it is important to stress that we believe that using several approaches whenever possible for activity prediction can often give a broader understanding of the data than using only one approach alone. Thus, BBNC is a useful addition to the computational chemist's toolbox.
Disciplines :
Microbiology
Author, co-author :
Abdo, Ammar
Leclere, Valerie
Jacques, Philippe ;  Université de Liège - ULiège > Chimie et bio-industries > Bio-industries
Salim, Naomie
Pupin, Maude
Language :
English
Title :
Prediction of new bioactive molecules using a Bayesian belief network.
Publication date :
2014
Journal title :
Journal of Chemical Information and Modeling
ISSN :
1549-9596
eISSN :
1549-960X
Publisher :
American Chemical Society, Washington, United States - District of Columbia
Volume :
54
Issue :
1
Pages :
30-6
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 21 October 2014

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