a Department of Chemistry , Tallinn University of Technology , Tallinn , Estonia.
b Institute of Chemistry , University of Tartu , Tartu , Estonia.
Expert Opin Drug Discov. 2016 Jul;11(7):627-39. doi: 10.1080/17460441.2016.1186876. Epub 2016 May 30.
Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery.
In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field.
The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.
人工神经网络（ANNs）是高度自适应的非线性优化算法，已应用于许多不同的科学领域，包括经济学、工程学、物理学、化学和医学。值得注意的是，在过去的二十年中，ANNs 在药物发现过程中得到了广泛的应用。
在这篇综述中，作者讨论了将人工神经网络（ANNs）纳入定量构效关系（QSAR）框架中在药物发现方面的优缺点。此外，作者还研究了最近的研究，这些研究涵盖了药物发现中各种疾病的广泛领域。此外，作者试图回答关于人工神经网络（ANNs）在药物发现中的预期的问题，并讨论该领域的趋势。
ANNs 仍然存在过拟合和可解释性的旧问题。然而，尽管存在这些问题，作者认为人工神经网络（ANNs）可能已经满足了研究人员的许多期望，并且仍然被认为是 QSAR 中非线性数据建模的优秀工具。未来，人工神经网络（ANNs）很可能继续用于药物开发。
