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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Deep Learning in Chemistry.

Adam C Mater¹, Michelle L Coote¹

¹ARC Centre of Excellence for Electromaterials Science, Research School of Chemistry , Australian National University , Canberra , Australian Capital Territory 2601 , Australia.

Journal of Chemical Information and Modeling

|June 14, 2019

Summary

Deep learning, a subset of machine learning, uses hierarchical data patterns for problem-solving. This review explores its diverse applications in chemistry, aiming to accelerate discovery and innovation.

Keywords:

Cheminformatics Computational chemistry Deep learning Drug design Machine learning Materials design Open sourcing Quantum mechanical calculations Representation learning Synthesis planning

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Published on: September 25, 2021

Area of Science:

Computational Chemistry
Artificial Intelligence in Chemistry
Data-Driven Chemical Discovery

Background:

Machine learning empowers computers to learn from data.
Deep learning employs hierarchical feature recombination for pattern recognition.
Recent advancements show deep learning's utility across chemical disciplines.

Purpose of the Study:

To elucidate deep learning concepts for chemists of all backgrounds.
To provide a comprehensive overview of deep learning applications in chemistry.
To encourage broader engagement with deep learning in the chemical community.

Main Methods:

Review of existing literature on deep learning in chemistry.
Explanation of fundamental deep learning principles.
Categorization and discussion of diverse chemical applications.

Main Results:

Deep learning has demonstrated significant impact in computational chemistry.
Applications span drug design, materials science, and synthesis planning.
The field is rapidly evolving with new methodologies and successes.

Conclusions:

Deep learning offers powerful tools for addressing complex chemical challenges.
Increased adoption can accelerate innovation and discovery in chemistry.
Empowering chemists with deep learning knowledge is crucial for future advancements.