Large-scale comparison of machine learning methods for drug target prediction on ChEMBL†,Chemical Science

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Large-scale comparison of machine learning methods for drug target prediction on ChEMBL†
Chemical Science ( IF 7.6 ) Pub Date : 2018-06-06 00:00:00 , DOI: 10.1039/c8sc00148k
Andreas Mayr ₁ , Günter Klambauer ₁ , Thomas Unterthiner ₁ , Marvin Steijaert ₂ , Jörg K Wegner ₃ , Hugo Ceulemans ₃ , Djork-Arné Clevert ₄ , Sepp Hochreiter ₁

Affiliation

Deep learning is currently the most successful machine learning technique in a wide range of application areas and has recently been applied successfully in drug discovery research to predict potential drug targets and to screen for active molecules. However, due to (1) the lack of large-scale studies, (2) the compound series bias that is characteristic of drug discovery datasets and (3) the hyperparameter selection bias that comes with the high number of potential deep learning architectures, it remains unclear whether deep learning can indeed outperform existing computational methods in drug discovery tasks. We therefore assessed the performance of several deep learning methods on a large-scale drug discovery dataset and compared the results with those of other machine learning and target prediction methods. To avoid potential biases from hyperparameter selection or compound series, we used a nested cluster-cross-validation strategy. We found (1) that deep learning methods significantly outperform all competing methods and (2) that the predictive performance of deep learning is in many cases comparable to that of tests performed in wet labs (i.e., in vitro assays).

中文翻译：

ChEMBL 上药物靶标预测机器学习方法的大规模比较†

深度学习是目前应用领域最成功的机器学习技术，最近已成功应用于药物发现研究，以预测潜在的药物靶点并筛选活性分子。然而，由于（1）缺乏大规模研究，（2）药物发现数据集特有的化合物系列偏差，以及（3）大量潜在深度学习架构带来的超参数选择偏差，目前尚不清楚深度学习是否确实能够在药物发现任务中超越现有的计算方法。因此，我们评估了几种深度学习方法在大规模药物发现数据集上的性能，并将结果与其他机器学习和目标预测方法的结果进行了比较。为了避免超参数选择或复合系列的潜在偏差，我们使用了嵌套集群交叉验证策略。我们发现（1）深度学习方法显着优于所有竞争方法，（2）深度学习的预测性能在许多情况下与湿实验室（即体外测定）中进行的测试相当。

更新日期：2018-06-06

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