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Bayesian Modeling in Virtual High Throughput Screening

机译：虚拟高通量筛选中的贝叶斯建模

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摘要

Naive Bayesian classifiers are a relatively recent addition to the arsenal of tools available to computational chemists. These classifiers fall into a class of algorithms referred to broadly as machine learning algorithms. Bayesian classifiers may be used in conjunction with classical modeling techniques to assist in the rapid virtual screening of large compound libraries in a systematic manner with a minimum of human intervention. This approach allows computational scientists to concentrate their efforts on their core strengths of model building. Bayesian classifiers have an added advantage of being able to handle a variety of numerical or binary data such as physicochemical properties or molecular fingerprints, making the addition of new parameters to existing models a relatively straightforward process. As a result, during a drug discovery project these classifiers can better evolve with the needs of the projects from general models in the lead finding stages to increasingly precise models in the lead optimization stages that are of particular interest to a specific medicinal chemistry team. Although other machine learning algorithms abound, Bayesian classifiers have been shown to compare favorably under most working conditions and have been shown to be tolerant of noisy experimental data.
机译：朴素贝叶斯分类器是计算化学家可使用的工具库中相对较新的一种。这些分类器属于一类算法，广泛地称为机器学习算法。贝叶斯分类器可以与经典的建模技术结合使用，以系统的方式快速虚拟筛选大型化合物库，而无需人工干预。这种方法使计算科学家可以将精力集中在模型构建的核心优势上。贝叶斯分类器的另一个优势是能够处理各种数字或二进制数据，例如物理化学性质或分子指纹，从而使向现有模型中添加新参数成为一个相对简单的过程。结果，在药物发现项目期间，这些分类器可以更好地随着项目的需要而发展，从潜在客户发现阶段的通用模型到潜在客户优化阶段的日益精确的模型，这些都是特定药物化学团队特别感兴趣的。尽管其他机器学习算法比比皆是，但贝叶斯分类器已被证明在大多数工作条件下具有良好的比较性，并且已被证明可以耐受嘈杂的实验数据。

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