Barabasi-Lab/AI-Bind
Interpretable AI pipeline improving binding predictions for novel protein targets and ligands
AI-Bind helps researchers quickly and accurately predict how well novel proteins and chemical compounds will bind together. You provide the amino acid sequence of a protein and the SMILES string of a ligand, and it outputs the probability of them binding. This tool is ideal for scientists in drug discovery, materials science, or biochemistry who need to screen many new protein-ligand pairs.
No commits in the last 6 months.
Use this if you need to rapidly screen large libraries of never-before-seen proteins and ligands to prioritize candidates for further (and often costly) experimental validation or detailed auto-docking simulations.
Not ideal if you already have 3D protein structures readily available and are primarily interested in predicting interactions for proteins and ligands that have previously been studied.
Stars
41
Forks
3
Language
Jupyter Notebook
License
MIT
Category
Last pushed
May 06, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Barabasi-Lab/AI-Bind"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
pritampanda15/PandaDock
PandaDock: Physics based Molecular Docking with GNN Scoring
kexinhuang12345/DeepPurpose
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
BioinfoMachineLearning/PoseBench
Comprehensive benchmarking of protein-ligand structure prediction methods. (Nature Machine Intelligence)
maranasgroup/CatPred
Machine Learning models for in vitro enzyme kinetic parameter prediction
kamerlinlab/KIF
KIF - Key Interactions Finder. A python package to identify the key molecular interactions that...