AtharvaTilewale/boltz2-notebook
Boltz2 Notebook – A streamlined Colab-based pipeline for protein structure prediction and binding affinity analysis using the Boltz2 deep learning model.
This helps biologists, chemists, and drug discovery researchers quickly predict the 3D structures of proteins, DNA, RNA, and their interactions with other molecules, like potential drug compounds. You provide the genetic sequence or chemical structure, and it outputs a visualized 3D molecular model and an estimated binding affinity. It's designed for scientists who need to understand how biomolecules interact without complex software installations.
Use this if you need to predict molecular structures and their binding affinities using AI models, without having to install specialized software or manage local computing resources.
Not ideal if you require highly customized model architectures, extremely large-scale simulations beyond typical cloud notebook limits, or prefer desktop-based, offline analysis tools.
Stars
8
Forks
2
Language
HTML
License
MIT
Category
Last pushed
Dec 01, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/AtharvaTilewale/boltz2-notebook"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Graylab/DL4Proteins-notebooks
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
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.