计算化学的深度学习
Deep Learning for Computational Chemistry
摘要：
人工神经网络的兴起和衰落在计算机科学和计算化学的科学文献中都有很好的记载。然而近二十年后，我们现在看到对深度学习的兴趣重新抬头，深度学习是一种基于多层神经网络的机器学习算法。在过去的几年里，我们已经看到了深度学习在许多领域的变革性影响，特别是在语音识别和计算机视觉领域，以至于这些领域的大多数专家从业者现在经常避开先前建立的模型，而倾向于深度学习模型。在这篇综述中，我们对深度神经网络的理论及其独特的特性进行了介绍性概述，这些特性将它们与化学信息学中使用的传统机器学习算法区分开来。通过概述深度神经网络的各种新兴应用，我们强调了它的普遍性和广泛适用性，以应对该领域的广泛挑战，包括QSAR，虚拟筛选，蛋白质结构预测，量子化学，材料设计和性能预测。在审查深度神经网络的性能时，我们观察到在不同的研究主题中，与非神经网络最先进的模型相比，基于深度神经网络的模型的性能始终优于非神经网络模型，并且基于深度神经网络的模型通常超过其各自任务的“玻璃天花板”预期。再加上GPU加速计算用于训练深度神经网络的成熟性以及训练这些网络的化学数据的指数增长，我们预计深度学习算法将成为计算化学的一个有价值的工具。
英文摘要：
The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure prediction, quantum chemistry, materials design and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry.
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