Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
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DOI：
10.1007/s10994-017-5685-x
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发表时间：
2018-01-01
期刊：
影响因子：
7.5
通讯作者：
King, Ross D.
中科院分区：
文献类型：
--
作者：
Olier, Ivan;Sadawi, Noureddin;King, Ross D.
我们研究学习的定量构效关系（QSAR）作为元学习的案例研究。该应用领域具有最高的社会重要性，因为它是新药开发的关键一步。标准的QSAR学习问题是：给定一个靶标（通常是蛋白质）和一组具有相关生物活性（例如抑制靶标）的化合物（小分子），学习从分子表示到活性的预测映射。虽然几乎所有类型的机器学习方法都已应用于QSAR学习，但没有公认的单一最佳学习QSAR的方法，因此该问题领域非常适合元学习。我们首先对QSAR学习的机器学习方法进行了有史以来最全面的比较：18种回归方法，3种分子表示，应用于2700多个QSAR问题。（这些结果已在OpenML上公开，是测试新型元学习方法的宝贵资源。然后，我们研究了QSAR问题的算法选择的效用。我们发现，这种元学习方法比最好的个体QSAR学习方法（使用分子指纹表示的随机森林）平均高出13%。我们的结论是，元学习优于基础学习方法的QSAR学习，并且由于这项调查是有史以来最广泛的基础和元学习方法的比较之一，它提供了元学习优于基础学习的一般有效性的证据。
We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance, as it is a key step in the development of new medicines. The standard QSAR learning problem is: given a target (usually a protein) and a set of chemical compounds (small molecules) with associated bioactivities (e.g. inhibition of the target), learn a predictive mapping from molecular representation to activity. Although almost every type of machine learning method has been applied to QSAR learning there is no agreed single best way of learning QSARs, and therefore the problem area is well-suited to meta-learning. We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 3 molecular representations, applied to more than 2700 QSAR problems. (These results have been made publicly available on OpenML and represent a valuable resource for testing novel meta-learning methods.) We then investigated the utility of algorithm selection for QSAR problems. We found that this meta-learning approach outperformed the best individual QSAR learning method (random forests using a molecular fingerprint representation) by up to 13%, on average. We conclude that meta-learning outperforms base-learning methods for QSAR learning, and as this investigation is one of the most extensive ever comparisons of base and meta-learning methods ever made, it provides evidence for the general effectiveness of meta-learning over base-learning.