This repository serves as a curated, collaborative, and continuously updated list of research papers focused on the application of machine learning to protein-related problems. It aims to be a comprehensive resource for researchers, engineers, and practitioners in bioinformatics, computational biology, and protein engineering, providing a structured overview of the rapidly evolving field.

How It Works

The repository categorizes papers based on their primary application area (e.g., protein engineering, biophysics, representation learning) and the machine learning models employed. Papers are listed in reverse chronological order within each category, with links to the original publications provided where available. This organization facilitates efficient discovery and tracking of relevant research.

Quick Start & Requirements

This is a curated list of papers; there are no installation or execution requirements.

Highlighted Details

• Comprehensive Coverage: Encompasses a wide range of ML applications in proteins, from design and engineering to prediction of structure, function, and interactions.
• Categorized Structure: Organizes papers by application and model type for easy navigation.
• Reverse Chronological Order: Ensures users can quickly access the latest research.
• Collaborative Nature: Encourages community contributions via pull requests and issues to maintain up-to-date information.

Maintenance & Community

The project is maintained by yangkky and welcomes community contributions. There are no specific community channels or roadmaps linked in the README.

Licensing & Compatibility

The repository itself is not software and thus not subject to software licensing. The linked papers are subject to their respective publisher's licenses.

Limitations & Caveats

As a curated list, the quality and completeness of the information depend on community contributions. The scope is limited to papers explicitly found and categorized by the maintainers.