Best Practices for Machine Learning-Assisted Protein Engineering

TL;DR

This paper outlines best practices and guidelines for developing reliable, reproducible, and transparent machine learning models in protein engineering, emphasizing software engineering principles and practical resources.

Contribution

It provides a comprehensive set of guidelines and resources to improve ML development, evaluation, and publication standards in protein engineering.

Findings

Guidelines cover data acquisition to model deployment.

Emphasizes software engineering best practices.

Aims to promote transparency and reproducibility.

Abstract

Data-driven modeling based on Machine Learning (ML) is becoming a central component of protein engineering workflows. This perspective presents the elements necessary to develop effective, reliable, and reproducible ML models, and a set of guidelines for ML developments for protein engineering. This includes a critical discussion of software engineering good practices for development and evaluation of ML-based protein engineering projects, emphasizing supervised learning. These guidelines cover all the necessary steps for ML development, from data acquisition to model deployment. Additionally, the present perspective provides practical resources for the implementation of the outlined guidelines. These recommendations are also intended to support editors and scientific journals in enforcing good practices in ML-based protein engineering publications, promoting high standards across the community. With this, the aim is to further contribute to improved ML transparency and credibility by easing the adoption of software engineering best practices into ML development for protein engineering. We envision that the wide adoption and continuous update of best practices will encourage informed use of ML on real-world problems related to protein engineering.