Advances in Machine Learning for Directed Evolution: A Five-Year Retrospective
q-bio.BM, cs.LG
Submitted: 2026-09-02
Updated: 2026-09-02
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: The last five-plus years have seen many protein engineering disciplines transformed by advances in machine learning (ML), but the same cannot be said for directed evolution.
Terminology
Abstract
The last five-plus years have seen many protein engineering disciplines transformed by advances in machine learning (ML), but the same cannot be said for directed evolution. Reflecting on a previously co-authored perspective, I discuss why I believe this to be the case, arguing that a disconnect between the goals of machine-learning-assisted directed evolution (MLDE) researchers--"identify an optimal protein"--and the goals of directed evolution more broadly--"identify a sufficient protein given time and resource constraints"--is a principal culprit. As an example, I highlight how nearly all current MLDE methods neglect to account for the cost of DNA synthesis, resulting in strategies that have limited practical applicability regardless of the underlying models' capabilities. I close by discussing recent works that are exceptions to this overarching trend, and emphasize that the last five years of efforts in ML-assisted protein engineering and the prescribed reframe of MLDE objectives need not be mutually exclusive.
Sources
- GPT-4 Technical Report
- Artificial Intelligence Index Report 2025
- Scaling and Data Saturation in Protein Language Models
- Generative AI for Controllable Protein Sequence Design: A Survey
- Scientists' Perspectives on the Potential for Generative AI in their Fields
- Foundation Models for AI-Enabled Biological Design
- Sparks of Artificial General Intelligence: Early experiments with GPT-4
- ProteinGuide: On-the-fly property guidance for protein sequence generative models
- Steering Generative Models with Experimental Data for Protein Fitness Optimization
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