Quantitative Structure-Activity Relationship Modeling and Bayesian Networks: Optimality of Naive Bayes Model

O. Kupervasser
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引用次数: 2

Abstract

Previously, computational drag design was usually based on simplified laws of molecular physics, used for calculation of ligand ’ s interaction with an active site of a protein-enzyme. However, currently, this interaction is widely estimated using some statistical properties of known ligand-protein complex properties. Such statistical properties are described by quantitative structure-activity relationships (QSAR). Bayesian networks can help us to evaluate stability of a ligand-protein complex using found statistics. Moreover, we are possible to prove optimality of Naive Bayes model that makes these evaluations simple and easy for practical realization. We prove here optimality of Naive Bayes model using as an illustration ligand-protein interaction.
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定量构效关系建模与贝叶斯网络:朴素贝叶斯模型的最优性
以前,计算阻力设计通常基于简化的分子物理定律,用于计算配体与蛋白酶活性位点的相互作用。然而,目前,这种相互作用被广泛地使用已知配体-蛋白质复合物性质的一些统计性质来估计。这种统计性质是用定量构效关系(QSAR)来描述的。贝叶斯网络可以帮助我们利用已知的统计量来评估配体-蛋白质复合物的稳定性。此外,我们可以证明朴素贝叶斯模型的最优性,使这些评估简单,易于实际实现。本文以配体-蛋白相互作用为例,证明了朴素贝叶斯模型的最优性。
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