# Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters

## TL;DR

This paper reviews how machine learning can predict enzyme activity parameters, offering faster and cheaper alternatives to traditional methods.

## Contribution

The paper provides a comprehensive review of global and local ML models for enzyme kinetic parameter prediction.

## Key findings

- ML models can predict enzyme kinetic parameters like k_cat and K_m with increasing accuracy.
- Global models work across diverse enzyme classes, while local models focus on specific families.
- High-throughput data and semisupervised learning are proposed to address data scarcity issues.

## Abstract

Enzyme kinetic parameters,
including k
cat, K
m, k
cat/K
m, and K
i, are critical for guiding
applications in enzyme engineering, metabolic
modeling, and synthetic biology by providing quantitative information
on enzyme activity under various conditions. Experimental determination
of these parameters is often costly and time-consuming. Moreover,
traditional computational methods are not well-suited to estimating
these parameters. This motivated the development of machine learning
(ML) models for in silico predictions. Here, we review
recent advances in ML-based prediction of enzyme kinetic parameters
by highlighting global models trained on diverse enzyme classes and
local models catered toward specific enzyme families. These models
have been applied in myriads of applications including predicting
mutation effects, accelerating enzyme mining, and parametrizing genome-scale
metabolic models. While data scarcity remains the main limitation
for these models, we outline emerging opportunities such as high-throughput
data generation and semisupervised learning as means to overcome this
issue. In summary, this Review provides a roadmap for leveraging ML
to enhance the performance, robustness, and scope of enzyme kinetic
parameter prediction, leading to the accurate annotation of protein
sequences for target functions.