seqme: a Python library for evaluating biological sequence design
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912690522292224 |
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| author | Møller-Larsen, Rasmus Izdebski, Adam Olszewski, Jan Gawade, Pankhil Kmicikiewicz, Michal Zarzecki, Wojciech Szczurek, Ewa |
| author_facet | Møller-Larsen, Rasmus Izdebski, Adam Olszewski, Jan Gawade, Pankhil Kmicikiewicz, Michal Zarzecki, Wojciech Szczurek, Ewa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04239 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | seqme: a Python library for evaluating biological sequence design Møller-Larsen, Rasmus Izdebski, Adam Olszewski, Jan Gawade, Pankhil Kmicikiewicz, Michal Zarzecki, Wojciech Szczurek, Ewa Machine Learning Artificial Intelligence 68T01 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. |
| title | seqme: a Python library for evaluating biological sequence design |
| topic | Machine Learning Artificial Intelligence 68T01 |
| url | https://arxiv.org/abs/2511.04239 |