Pharmaceuticals, Research and Development, Computational Molecular Design, Bayer AG, Wuppertal, Germany.
Pharmaceuticals, Research and Development, Computational Molecular Design, Bayer AG, Berlin, Germany.
Methods Mol Biol. 2022;2390:61-101. doi: 10.1007/978-1-0716-1787-8_2.
The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug discovery processes with in silico methods. Significant advances in all three areas are the reason for the regained interest in these models. In this book chapter we review various machine learning (ML) approaches that make use of measured in vitro/in vivo data of many compounds. We put these in context with other digital drug discovery methods and present some application examples.
近年来，定量构效关系（QSAR）这一广为人知的概念引起了人们的浓厚兴趣。数据、描述符和算法是构建有用模型的主要支柱，这些模型支持通过计算方法更有效地进行药物发现。这三个领域的显著进展是这些模型重新受到关注的原因。在本章中，我们回顾了各种机器学习（ML）方法，这些方法利用了许多化合物的体外/体内测量数据。我们将这些方法与其他数字药物发现方法进行了比较，并介绍了一些应用实例。
