Deep Learning for Computational Chemistry

Garrett B. Goh, Nathan Oken Hodas, Abhinav Vishnu

Published in Journal of Computational… 2017

DOI:10.1002/jcc.24764

Arxiv Free Download:
https://arxiv.org/abs/1701.04503

Paywall:
https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.24764

#deeplearning #AI #artificialintelligence #chemistry #computationalchemistry

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 quantitative structure activity relationship, 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.