seqme: a Python library for evaluating biological sequence design

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.

Unified Molecule Generation and Property Prediction

Modeling the joint distribution of the data samples and their properties allows to construct a single model for both data generation and property prediction, with synergistic capabilities reaching beyond purely generative or predictive models. However, training joint models presents daunting architectural and optimization challenges. Here, we propose Hyformer, a transformer-based joint model that successfully blends the generative and predictive functionalities, using an alternating attention mask together with a unified pre-training scheme. We show that Hyformer rivals other joint models, as well as state-of-the-art molecule generation and property prediction models. Additionally, we show the benefits of joint modeling in downstream tasks of molecular representation learning, hit identification and antimicrobial peptide design.