Advances in Machine Learning for Directed Evolution: A Five-Year Retrospective

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.

Biomolecules (q-bio.BM); Machine Learning (cs.LG)

arXiv:2609.03046 [q-bio.BM]

(or arXiv:2609.03046v1 [q-bio.BM] for this version)

https://doi.org/10.48550/arXiv.2609.03046 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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