CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters
收藏NIAID Data Ecosystem2026-05-02 收录
数据链接：
官方服务：
资源简介：
Contains the processed datasets of CatPred-DB
应用场景：
创建时间：
2025-01-30
相关数据集
The raw data set for enzyme kinetic parameter prediction
The data distribution is shown above.. Due to overfitting, existing models for predicting enzyme kinetic parameters either suffer from poor generalization ability or lack accuracy.To ensure
IEEE90
A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-Dependent kcat/Km Prediction in β‑Glucosidases
The catalytic activity of enzymes is intricately determined by their amino acid sequences and assay conditions, particularly temperature. Navigating the complex interplay among sequence, temperature,
Figshare2025-10-02 更新40
Estimated parameters for Bayesian Multilevel Models of KM and kcat values
RData (.rds) files containing brmsfit model objects estimated with the brms R package from KM and kcat values reported in BRENDA and SABIO-RK. These models are used by the ENKIE python package to pred
Zenodo2023-02-22 更新00
Additional file 5 of GraphKM: machine and deep learning for KM prediction of wildtype and mutant enzymes
Additional file 5. The one-hot encodings of Kegg compound ID.
Figshare2024-08-15 更新60
Artificial intelligence-based parametrization of Michaelis–Menten maximal velocity: Toward in silico New Approach Methodologies (NAMs)
The development of mechanistic systems biology models necessitates the utilization of numerous kinetic parameters once the enzymatic mode of action has been identified. Moreover, wet lab experimentati
NIAID Data Ecosystem10
