{"ID":2845275,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.04239","arxiv_id":"2511.04239","title":"seqme: a Python library for evaluating biological sequence design","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 single 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 and iterative computational design methods.","short_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 single software library implementin...","url_abs":"https://arxiv.org/abs/2511.04239","url_pdf":"https://arxiv.org/pdf/2511.04239v1","authors":"[\"Rasmus Møller-Larsen\",\"Adam Izdebski\",\"Jan Olszewski\",\"Pankhil Gawade\",\"Michal Kmicikiewicz\",\"Wojciech Zarzecki\",\"Ewa Szczurek\"]","published":"2025-11-06T10:24:31Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}