A Binary Ant Colony Optimization Classifier for Molecular Activities
A Binary Ant Colony Optimization Classifier for Molecular Activities
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DOI：
10.1021/ci200186m
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发表时间：
2011-10-01
影响因子：
5.6
通讯作者：
Huwyler, Joerg
中科院分区：
文献类型：
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作者：
Hammann, Felix;Suenderhauf, Claudia;Huwyler, Joerg
化学指纹对分子特征的存在与否进行编码，在许多大型数据库中都可以找到。利用蚁群优化（蚁群优化）模式的一种变体，我们描述了一种基于指纹特征选择的二值分类器。我们讨论了算法和可能的交叉验证程序。作为一个现实世界的例子，我们使用我们的算法来分析恶性疟原虫抑制试验，并将其性能与当今使用的其他机器学习范式（决策树归纳、随机森林、支持向量机、人工神经网络）进行比较。我们的算法在预测能力方面与已建立的范例相匹配，但为药物化学家和基础研究人员提供了易于解释的结果。此外，用我们的范式生成的模型易于实现，并且可以通过额外利用预先计算的指纹信息来补充虚拟筛选。
Chemical fingerprints encode the presence or absence of molecular features and are available in many large databases. Using a variation of the Ant Colony Optimization (ACO) paradigm, we describe a binary classifier based on feature selection from fingerprints. We discuss the algorithm and possible cross-validation procedures. As a real-world example, we use our algorithm to analyze a Plasmodium falciparum inhibition assay and contrast its performance with other machine learning paradigms in use today (decision tree induction, random forests, support vector machines, artificial neural networks). Our algorithm matches established paradigms in predictive power, yet supplies the medicinal chemist and basic researcher with easily interpretable results. Furthermore, models generated with our paradigm are easy to implement and can complement virtual screenings by additionally exploiting the precalculated fingerprint information.