Machine Learning (ML) is changing how we study enzyme kinetics. But, there are still some big challenges:

• Data Quality: ML needs a lot of good data. Right now, we don’t have enough high-quality data in enzyme kinetics.

• Model Complexity: Sometimes, ML models can fit the data too closely. This makes it hard for them to work well with new data.

• Interpretability: Many ML models are like "black boxes." This means they’re complicated, and it’s hard to figure out what they’re actually doing.

To fix these problems, we can:

1. Improve how we collect data.
2. Use simpler models that are easier to understand.
3. Combine expert knowledge to make our models better.

By tackling these issues, ML can help us understand enzyme kinetics even better.