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Updated: Sep 28, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Protein design via deep learning.

Wenze Ding^1,2,3,4, Kenta Nakai⁵, Haipeng Gong^3,4

¹School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.

Briefings in Bioinformatics

|March 29, 2022

Summary

This summary is machine-generated.

Deep learning is revolutionizing de novo protein design, enabling the creation of novel proteins for nanotechnology and biomedicine. This review explores advances in deep learning for protein design, highlighting future opportunities.

Keywords:

deep learning deep reinforcement learning protein design protein sequence protein structure

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

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Last Updated: Sep 28, 2025

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Biotechnology
Computational Biology
Protein Engineering

Background:

Proteins with specific functions are crucial for nanotechnology and biomedicine.
De novo protein design allows the creation of novel proteins from scratch.
Deep learning has recently emerged as a transformative approach in protein design.

Purpose of the Study:

To review current advances in deep learning-based de novo protein design.
To compare deep learning methods with traditional knowledge-based approaches.
To discuss future perspectives in protein design.

Main Methods:

Review of recent literature on deep learning applications in protein design.
Analysis of structure-based protein design and direct sequence design using deep learning.
Highlighting applications of deep reinforcement learning in protein design.

Main Results:

Deep learning methods offer novel capabilities compared to conventional approaches.
Significant progress has been made in structure-based and sequence-based protein design.
Deep reinforcement learning shows promise for advanced protein design tasks.

Conclusions:

Deep learning represents a promising future direction for de novo protein design.
Further research is needed to address challenges and explore opportunities in the field.
Advances in protein design have broad implications for science and technology.