Deep Learning for Computational Chemistry

01/17/2017
by   Garrett B. Goh, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 21

page 22

04/11/2018

Deep Learning For Computer Vision Tasks: A review

Deep learning has recently become one of the most popular sub-fields of ...
06/20/2017

Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models

In the last few years, we have seen the transformative impact of deep le...
07/23/2018

NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference

Deep neural networks have been successfully deployed in a wide variety o...
11/24/2016

Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a...
01/31/2018

Deep Learning Works in Practice. But Does it Work in Theory?

Deep learning relies on a very specific kind of neural networks: those s...
11/16/2020

Deep Learning – A first Meta-Survey of selected Reviews across Scientific Disciplines and their Research Impact

Deep learning belongs to the field of artificial intelligence, where mac...
11/13/2019

The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design

The past decade has seen a remarkable series of advances in machine lear...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.