Abstract：
Molecular docking tools are regularly used to computationally identify new molecules in virtual screening for drug discovery. However, docking tools suffer from inaccurate scoring functions with widely varying performance on different proteins. To enable more accurate ranking of active over inactive ligands in virtual screening, we created a machine learning consensus docking tool, MILCDock, that uses predictions from five traditional molecular docking tools to predict the probability a ligand binds to a protein. MILCDock was trained and tested on data from both the DUD-E and LIT-PCBA docking datasets and shows improved performance over traditional molecular docking tools and other consensus docking methods on the DUD-E dataset. LIT-PCBA targets proved to be difficult for all methods tested. We also find that DUD-E data, although biased, can be effective in training machine learning tools if care is taken to avoid DUD-E\'s biases during training.
摘要：
分子对接工具通常用于在虚拟筛选中以计算方式识别新分子，以发现药物。然而,对接工具遭受不准确的评分功能，在不同蛋白质上的性能差异很大。为了在虚拟筛选中更准确地对活性配体与非活性配体进行排序，我们创建了一个机器学习共识对接工具，MILCDock,使用五种传统分子对接工具的预测来预测配体与蛋白质结合的概率。MILCDock在来自DUD-E和LIT-PCBA对接数据集的数据上进行了训练和测试，并且在DUD-E数据集上显示出优于传统分子对接工具和其他共识对接方法的性能。LIT-PCBA靶标被证明对于所有测试的方法都是困难的。我们还发现DUD-E数据，尽管有偏见，如果在训练期间注意避免DUD-E的偏见，可以有效地训练机器学习工具。
