Evotec (UK) Ltd., Abingdon, Oxfordshire, UK.
Methods Mol Biol. 2022;2390:125-151. doi: 10.1007/978-1-0716-1787-8_5.
Within the context of the latest resurgence in the application of artificial intelligence approaches, deep learning has undergone a renaissance over recent years. These methods have been applied to a number of problems in computational chemistry. Compared to other machine learning approaches, the practical performance advantages of deep neural networks are often unclear. However, deep learning does appear to offer a number of other advantages such as the facile incorporation of multitask learning and the enhancement of generative modeling. The high complexity of contemporary network architectures represents a potentially significant barrier to their future adoption due to the costs of training such models and challenges in interpreting their predictions. When combined with the relative paucity of very large datasets, it is interesting to reflect on whether deep learning is likely to have the kind of transformational impact on computational chemistry that it is commonly held to have had in other domains such as image recognition.
在人工智能方法最新复兴的背景下，深度学习近年来经历了一次复兴。这些方法已经被应用于计算化学中的许多问题。与其他机器学习方法相比，深度学习网络的实际性能优势通常不明确。然而，深度学习似乎确实提供了一些其他优势，例如易于结合多任务学习和增强生成建模。由于训练这些模型的成本以及解释其预测的挑战，当代网络架构的高复杂性可能成为其未来采用的一个潜在重大障碍。当与相对较少的非常大数据集结合使用时，人们有兴趣思考深度学习是否有可能对计算化学产生变革性影响，就像它在图像识别等其他领域那样。
