This repository is a curated list of research papers on Protein Representation Learning (PRL), categorized by publication year. It serves as a valuable resource for researchers and practitioners in bioinformatics and computational biology seeking to understand the state-of-the-art in learning representations from protein sequences and structures for various downstream tasks.

How It Works

The project compiles and organizes academic literature on PRL, highlighting the evolution of techniques from early sequence-based methods to more recent structure-aware and multimodal approaches. It aims to provide a comprehensive overview of the field, tracking advancements and emerging trends in how protein data is encoded for machine learning.

Quick Start & Requirements

This repository is a list of papers and does not have a direct installation or execution command. It requires no specific software to view.

Highlighted Details

• Comprehensive listing of papers from major conferences (NeurIPS, ICML, ICLR) and journals (Nature, Science, PNAS).
• Categorization by publication year, allowing users to track the historical development of PRL.
• Includes links to papers and, where available, associated code repositories.
• Notes a trend towards more complex, domain-specific problems since 2023.

Maintenance & Community

The list is maintained by Lirong Wu, with contributions welcomed via issues or pull requests. Contact information for the authors is provided for inquiries.

Licensing & Compatibility

The repository itself is not associated with a specific software license. The content consists of links to external research papers, each with its own licensing and usage terms.

Limitations & Caveats

This is a curated list of papers and not a software library or framework. The "awesome" list format means it is a collection of external resources, and the quality or availability of linked code or papers is not guaranteed by the repository itself.