arXiv 2001.019242020-01-07stat.APstat.ML

定量构效回归模型的半监督学习框架

A semi-supervised learning framework for quantitative structure-activity regression modelling

Oliver P Watson, Isidro Cortes-Ciriano, James A Watson

备注：: 17 pages, 4 Figures

摘要：

监督学习模型，也称为定量结构活性回归（QSAR）模型，越来越多地用于辅助临床前小分子药物发现过程。这些模型是在由分子结构的有限维表示及其相应的目标特异性活性组成的数据上训练的。然后，这些模型可以用于预测以前未测量的新化合物的活性。在这项工作中，我们解决两个问题，这种方法。第一个是估计对于与训练数据中的化合物非常不同的化合物，模型预测的质量降低的程度。第二个是调整许多训练数据集固有的筛选依赖选择偏差。在最极端的情况下，仅报告通过活性依赖性筛选的化合物。通过使用半监督学习框架，我们证明了可以进行预测，其中考虑到测试化合物与训练数据中的化合物的相似性，并调整报告选择偏差。我们说明了这种方法使用公开的结构活性数据的一个大的化合物由葛兰素史克公司（Tres Cantos抗疟疾集），以抑制体外恶性疟原虫的生长。

英文摘要：

Supervised learning models, also known as quantitative structure-activity regression (QSAR) models, are increasingly used in assisting the process of preclinical, small molecule drug discovery. The models are trained on data consisting of a finite dimensional representation of molecular structures and their corresponding target specific activities. These models can then be used to predict the activity of previously unmeasured novel compounds. In this work we address two problems related to this approach. The first is to estimate the extent to which the quality of the model predictions degrades for compounds very different from the compounds in the training data. The second is to adjust for the screening dependent selection bias inherent in many training data sets. In the most extreme cases, only compounds which pass an activity-dependent screening are reported. By using a semi-supervised learning framework, we show that it is possible to make predictions which take into account the similarity of the testing compounds to those in the training data and adjust for the reporting selection bias. We illustrate this approach using publicly available structure-activity data on a large set of compounds reported by GlaxoSmithKline (the Tres Cantos AntiMalarial Set) to inhibit in vitro P. falciparum growth.

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