Awesome AI-based Protein Design
github.com/opendilab/awesome-ai-based-protein-designA collection of research papers for AI-based protein design.
Sourced from
- Awesome Bioinformatics — github.com/opendilab/awesome-ai-based-protein-design
- GitHub — github.com/opendilab/awesome-ai-based-protein-design
Related resources
Freely available tools for biological computing in Python, with included cookbook, packaging and thorough documentation. Part of the [Open Bioinformatics Foundation](http://open-bio.org/). Contains the very useful [Entrez](https://biopython.org/DIST/docs/api/Bio.Entrez-module.html) package for API access to the NCBI databases.
Structure-aware protein language model using 3D structural vocabulary (Foldseek) for joint sequence-structure pretraining, achieving SOTA on protein engineering and fitness prediction benchmarks (ICML 2024, Westlake University & Repl)
Unified benchmarking framework for protein representation learning, providing standardized interfaces for pre-training and diverse downstream tasks including structure prediction, fitness prediction, and property prediction across multiple protein datasets and model architectures (ICLR 2024, 273+ stars, MIT License)
A list of papers, data sets, and other resources for machine learning for small-molecule drug discovery.
A curated list of molecular docking software, datasets, and other closely related resources.
Curated, accuracy-first collection of benchmarks for evaluating LLMs on scientific reasoning and discovery across mathematics, physics, chemistry, materials science, biology, and agentic science (subinium, 29+ stars, MIT License, 2026)