Title: Deep Learning for Computational Chemistry,
Abstract: 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.