This repository serves as a curated list of academic papers focused on deep learning applications in protein design. It aims to consolidate research for scientists, engineers, and researchers in computational biology and bioinformatics, providing a comprehensive overview of the field's advancements.

How It Works

The repository categorizes papers based on the specific protein design task (e.g., function-to-sequence, scaffold-to-sequence) and the deep learning methodologies employed (e.g., GANs, Transformers, Diffusion Models). This structured approach allows users to quickly identify relevant research and understand the landscape of current techniques.

Quick Start & Requirements

No installation or specific software is required. The repository is a static list of papers, accessible via a web browser.

Highlighted Details

• Extensive categorization of papers by design task and DL method.
• Regular updates with recent publications in the field.
• Links to papers, code repositories, and supplementary materials where available.
• Inclusion of benchmark datasets and review articles for broader context.

Maintenance & Community

The repository is maintained by Peldom/papers_for_protein_design_using_DL and welcomes contributions and suggestions from the community.

Licensing & Compatibility

The repository itself does not host code or data, thus licensing is not applicable. It merely links to external academic publications.

Limitations & Caveats

This repository is a curated list and does not provide any tools or code for protein design. Users must access the linked papers and their associated resources independently.