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

Abstract

Summary: 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.

Availability and implementation: seqme is released at https://github.com/szczurek-lab/seqme under the BSD 3-Clause license.