Central South University, China.
Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa070.
Machine learning-based scoring functions (MLSFs) have attracted extensive attention recently and are expected to be potential rescoring tools for structure-based virtual screening (SBVS). However, a major concern nowadays is whether MLSFs trained for generic uses rather than a given target can consistently be applicable for VS. In this study, a systematic assessment was carried out to re-evaluate the effectiveness of 14 reported MLSFs in VS. Overall, most of these MLSFs could hardly achieve satisfactory results for any dataset, and they could even not outperform the baseline of classical SFs such as Glide SP. An exception was observed for RFscore-VS trained on the Directory of Useful Decoys-Enhanced dataset, which showed its superiority for most targets. However, in most cases, it clearly illustrated rather limited performance on the targets that were dissimilar to the proteins in the corresponding training sets. We also used the top three docking poses rather than the top one for rescoring and retrained the models with the updated versions of the training set, but only minor improvements were observed. Taken together, generic MLSFs may have poor generalization capabilities to be applicable for the real VS campaigns. Therefore, it should be quite cautious to use this type of methods for VS.
基于机器学习的打分函数（MLSFs）最近引起了广泛关注，有望成为基于结构的虚拟筛选（SBVS）的潜在重打分工具。然而，目前人们主要关注的是，针对通用用途而不是特定目标训练的 MLSFs 是否能够始终如一地适用于 VS。在这项研究中，我们对 14 种已报道的 MLSFs 在 VS 中的有效性进行了系统评估。总体而言，这些 MLSFs 中的大多数对于任何数据集都很难获得令人满意的结果，它们甚至不能优于 Glide SP 等经典 SF 的基准。但在训练集来自 Directory of Useful Decoys-Enhanced 数据集的 RFscore-VS 上，观察到了一个例外，它在大多数目标上表现出优越性。然而，在大多数情况下，它清楚地表明在与相应训练集中的蛋白质不同的目标上的性能相当有限。我们还使用了前三个对接构象而不是前一个构象进行重打分，并使用更新后的训练集版本重新训练了模型，但只观察到了较小的改进。综上所述，通用 MLSFs 可能缺乏泛化能力，难以适用于真正的 VS 活动。因此，在进行 VS 时应该非常谨慎地使用这种类型的方法。
