Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

中文翻译：

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机器学习评分功能可改善基于结构的结合亲和力预测和虚拟筛选。

如果此类目标的结构模型可用，则可以应用对接工具来预测小分子是否以及如何与目标结合。然而，对接的可靠性取决于所采用的评分函数 (SF) 的准确性。尽管多年来进行了深入的研究，但对于任何类型的方法来说，提高基于结构的结合亲和力预测或虚拟筛选的 SF 准确性已被证明是一项具有挑战性的任务。最近引入了基于现代机器学习回归模型的新 SF，它不强加预定的函数形式，因此能够有效地利用大量的实验数据。这些机器学习 SF 已被证明在结合亲和力预测和虚拟筛选方面都优于各种经典 SF。这些研究的新兴图景是，通过基于非线性回归的机器学习方法与全面的数据驱动特征选择相结合，可以极大地改进使用具有少量专家选择的结构特征的线性回归的经典方法。此外，经典 SF 的性能不会随着更大的训练数据集而增长，因此随着未来更多的训练数据可用，这种性能差距预计会扩大。本综述涵盖的其他主题包括预测 SF 在特定目标类别上的可靠性、生成合成数据以提高预测性能以及 SF 开发的建模指南。电线计算分子科学 2015，5：405-424。doi: 10.1002/wcms.1225 如需与本文相关的更多资源，请访问 WIREs 网站。