Best Practices for Machine Learning-Assisted Protein Engineering

Area of Science:

• Computational Biology
• Biochemistry
• Machine Learning

Background:

• Machine learning (ML) is increasingly integral to protein engineering workflows.
• Developing effective, reliable, and reproducible ML models is crucial for advancing the field.

Purpose of the Study:

• To present essential elements and guidelines for robust ML model development in protein engineering.
• To promote transparency and credibility in ML-based protein engineering research.

Main Methods:

• Discussion of software engineering best practices for ML development and evaluation.
• Emphasis on supervised learning approaches.
• Guidance covering the entire ML development lifecycle, from data acquisition to deployment.

Main Results:

• A comprehensive set of guidelines for ML development in protein engineering.
• Practical resources for implementing these guidelines.
• Recommendations for scientific journals and editors to enforce good practices.

Conclusions:

• Adoption of these guidelines will improve ML transparency and credibility in protein engineering.
• Standardized best practices will foster informed application of ML to real-world protein engineering challenges.