maranasgroup/CatPred
Machine Learning models for in vitro enzyme kinetic parameter prediction
This tool helps biochemists and researchers quickly estimate enzyme kinetic parameters like kcat, Km, and Ki. You provide information about an enzyme (its amino acid sequence and optionally a PDB structure) and its substrate molecule, and the tool predicts how efficiently the enzyme will process that substrate or inhibit a reaction. It's designed for scientists studying enzyme behavior and reaction rates.
Use this if you need to predict in vitro enzyme kinetic parameters (kcat, Km, or Ki) for various enzymes and substrates without extensive lab experiments.
Not ideal if you require kinetic parameters for multi-protein complexes or for scenarios where precise 3D structural data for every single enzyme-substrate interaction is unavailable.
Stars
77
Forks
23
Language
Python
License
MIT
Category
Last pushed
Feb 28, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/maranasgroup/CatPred"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
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)
kamerlinlab/KIF
KIF - Key Interactions Finder. A python package to identify the key molecular interactions that...
BioinfoMachineLearning/FlowDock
A geometric flow matching model for generative protein-ligand docking and affinity prediction....