seqme: a Python library for evaluating biological sequence design from generative models.

Authors

Rasmus Møller-Larsen, Adam Izdebski, Jan Olszewski, Pankhil Gawade, Michal Kmicikiewicz, Wojciech Zarzecki, Ewa Szczurek

Published in

Bioinformatics advances. Volume 6. Issue 1. Pages vbag212. Epub Aug 07, 2026.

Abstract

Recent advances in computational methods for designing biological sequences have sparked the development of metrics to evaluate these methods performance in terms of the fidelity of the designed sequences to a target distribution and their attainment of desired properties. However, a software library implementing these metrics was lacking. In this work we introduce seqme, a modular and highly extendable open-source Python library, containing model-agnostic metrics for evaluating computational methods for biological sequence design. seqme considers three groups of metrics: sequence-based, embedding-based, and property-based, and is applicable to a wide range of biological sequences: small molecules, DNA, ncRNA, mRNA, peptides and proteins. The library offers a number of embedding and property models for biological sequences, as well as diagnostics and visualization functions to inspect the results. seqme can be used to evaluate both one-shot generation and iterative optimization. We show the utility of seqme by performing an antimicrobial peptide benchmark and acquiring mRNA data.
seqme is released at https://github.com/szczurek-lab/seqme under the BSD 3-Clause license.

PMID:
42633207
Bibliographic data and abstract were imported from PubMed on 23 Aug 2026.

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