Biometrics Research, Merck & Co., Inc., Rahway, NJ, USA.
Methods Mol Biol. 2022;2390:233-260. doi: 10.1007/978-1-0716-1787-8_10.
Quantitative structure-activity relationship (QSAR) models are routinely applied computational tools in the drug discovery process. QSAR models are regression or classification models that predict the biological activities of molecules based on the features derived from their molecular structures. These models are usually used to prioritize a list of candidate molecules for future laboratory experiments and to help chemists gain better insights into how structural changes affect a molecule's biological activities. Developing accurate and interpretable QSAR models is therefore of the utmost importance in the drug discovery process. Deep neural networks, which are powerful supervised learning algorithms, have shown great promise for addressing regression and classification problems in various research fields, including the pharmaceutical industry. In this chapter, we briefly review the applications of deep neural networks in QSAR modeling and describe commonly used techniques to improve model performance.
定量构效关系 (QSAR) 模型是药物发现过程中常用的计算工具。QSAR 模型是基于分子结构衍生出的特征来预测分子生物活性的回归或分类模型。这些模型通常用于对候选分子进行优先级排序，以便进行未来的实验室实验，并帮助化学家更好地了解结构变化如何影响分子的生物活性。因此，在药物发现过程中，开发准确且可解释的 QSAR 模型至关重要。深度神经网络是一种强大的监督学习算法，在包括制药行业在内的各个研究领域的回归和分类问题中显示出了巨大的应用潜力。在本章中，我们简要回顾了深度神经网络在 QSAR 建模中的应用，并描述了常用的提高模型性能的技术。
