evedesign: accessible biosequence design with a unified framework

Hopf, T. A.; Gazizov, A.; Garcia Busto, S.; Eschbach, E.; Lee, S.; Mirdita, M.; Orenbuch, R.; Belahsen, K.; Ross, D.; Sander, C.; Steinegger, M.; d'Oelsnitz, S.; Marks, D.

doi:10.64898/2026.03.17.712115

bioRxiv · 10.64898/2026.03.17.712115

evedesign: accessible biosequence design with a unified framework

Hopf, T. A.·Gazizov, A.·Garcia Busto, S.·Eschbach, E.·Lee, S.·Mirdita, M.·Orenbuch, R.·Belahsen, K.·Ross, D.·Sander, C.·Steinegger, M.·d'Oelsnitz, S.·Marks, D.

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Abstract

Machine learning methods for protein engineering are rarely interoperable, require bespoke workflows, and remain inaccessible to non-experts. Yet the design problems that matter most - conditional design subject to real-world constraints, multi-objective optimization, and iterative lab-in-the-loop workflows where experimental data continuously refines successive design rounds - demand exactly the kind of flexible, composable infrastructure that no single tool provides. We present evedesign, a unified open-source framework that formalizes conditional biosequence design in a method-agnostic way, enabling complex multiobjective workflows combining supervised and unsupervised models from standardized specifications, and built from the outset to support iterative experimental integration. An interactive web interface facilitates end-to-end design for a broad scientific audience at https://evedesign.bio. We demonstrate evedesigns utility in antibody engineering, enzyme design, and natural enzyme discovery, and invite open-source community contributions.

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Hopf, T. A., Gazizov, A., Garcia Busto, S., Eschbach, E., Lee, S., Mirdita, M., Orenbuch, R., Belahsen, K., Ross, D., Sander, C., Steinegger, M., d'Oelsnitz, S., Marks, D.. 2026-03-19. evedesign: accessible biosequence design with a unified framework. https://doi.org/10.64898/2026.03.17.712115

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