mlsanityData to 7 Oct 2026

seqme: a Python library for evaluating biological sequence design

arXiv 2511.04239 ↗First posted 6 Nov 2025cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 42 references that are in the map. First posted on arXiv 6 Nov 2025.

Builds on

15 of 42 references.

References · 15

  1. 2018Demystifying MMD GANscited by 1,268
  2. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 925
  3. 2023Rethinking FID: Towards a Better Evaluation Metric for Image Generationcited by 303
  4. 2022The Vendi Score: A Diversity Evaluation Metric for Machine Learningcited by 299
  5. 2015A note on the evaluation of generative modelscited by 291
  6. 2021How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Modelscited by 193
  7. 2018Fr'echet ChemNet Distance: A metric for generative models for molecules in drug discoverycited by 191
  8. 2024Dirichlet Flow Matching with Applications to DNA Sequence Designcited by 110
  9. 2023Protein Discovery with Discrete Walk-Jump Samplingcited by 38
  10. 2024On the Challenges and Opportunities in Generative AIcited by 31
  11. 2024A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generationcited by 29
  12. 2024Towards a Scalable Reference-Free Evaluation of Generative Modelscited by 25
3 more
  1. 2025Position: All Current Generative Fidelity and Diversity Metrics are Flawedcited by 12
  2. 2025Synergistic Benefits of Joint Molecule Generation and Property Predictioncited by 5
  3. 2025ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoodscited by 3

Built on by

Not cited yet.