J Chem Inf Model - Prediction of new bioactive molecules using a Bayesian belief network.

Tópicos

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Resumo

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.

Resumo Limpo

natur product synthet compound valuabl sourc new small molecul lead novel drug cure diseas howev identifi new biolog activ small molecul still challeng paper introduc new activ predict approach use bayesian belief network classif bbnc root network fragment compos compound leav one side activ predict anoth side unknown compound activ repres set known compound set inact compound also use calcul similar unknown compound activ class similar activ assign unknown compound appli new approach eight wellknown data set extract literatur compar perform three classic machin learn algorithm experi show bbnc provid interest predict rate accuraci high divers data set low divers one short time calcul experi also show bbnc particular effect homogen data set found perform less well structur heterogen set howev import stress believ use sever approach whenev possibl activ predict can often give broader understand data use one approach alon thus bbnc use addit comput chemist toolbox

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