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Deep learning for computational chemistry.

Garrett B Goh¹, Nathan O Hodas¹, Abhinav Vishnu¹

¹Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, Washington, 99354.

Journal of Computational Chemistry

|March 9, 2017

Summary

This summary is machine-generated.

Deep learning, a type of artificial neural network, is revolutionizing computational chemistry. These advanced models consistently outperform traditional methods in diverse applications, showing great promise for future research.

Keywords:

artificial intelligence cheminformatics deep learning machine learning materials genome molecular modeling protein structure prediction quantitative structure activity relationship quantum chemistry toxicology

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Area of Science:

Computational Chemistry
Machine Learning
Artificial Intelligence

Background:

Artificial neural networks (ANNs) have a documented history in computer science and computational chemistry.
Deep learning (DL), based on multilayer ANNs, is experiencing a resurgence and transformative impact across various domains.
DL models are increasingly favored over traditional methods in fields like speech recognition and computer vision.

Purpose of the Study:

To provide an introductory overview of deep neural network (DNN) theory.
To highlight the unique properties of DNNs compared to traditional machine learning algorithms in cheminformatics.
To review emerging applications and performance of DNNs in computational chemistry.

Main Methods:

Review of deep neural network theory and principles.
Exploration of diverse applications in computational chemistry.
Analysis of performance benchmarks against traditional models.

Main Results:

DNNs demonstrate consistent outperformance against state-of-the-art non-neural network models.
DNN-based models frequently exceed performance expectations in various research tasks.
Applications span quantitative structure-activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction.

Conclusions:

Deep learning algorithms are a valuable and broadly applicable tool for computational chemistry.
The maturity of GPU-accelerated computing and growing chemical data support DL adoption.
DNNs are poised to significantly advance computational chemistry research and applications.