Graylab/DL4Proteins-notebooks
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
This project provides an accessible, hands-on introduction to using deep learning for protein design and structure prediction. It takes fundamental machine learning concepts and applies them to state-of-the-art tools like AlphaFold and RFDiffusion, allowing users to learn how to predict protein structures or design new ones. Researchers, educators, and students in fields like synthetic biology and therapeutics would use this resource.
659 stars. Actively maintained with 36 commits in the last 30 days.
Use this if you want to learn how to apply cutting-edge deep learning techniques to understand, predict, and design protein structures.
Not ideal if you are looking for a pre-built tool for direct protein simulation without learning the underlying deep learning principles.
Stars
659
Forks
109
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 09, 2026
Commits (30d)
36
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Graylab/DL4Proteins-notebooks"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
Peldom/papers_for_protein_design_using_DL
List of papers about Proteins Design using Deep Learning
rdk/p2rank
P2Rank: Protein-ligand binding site prediction from protein structure based on machine learning.
llnl/protlib-designer
Integer Linear Programming for Protein Library Design
samsinai/FLEXS
Fitness landscape exploration sandbox for biological sequence design.
LBM-EPFL/CARBonAra
Deep learning framework for protein sequence design from a backbone scaffold that can leverage...