CoPeP: Benchmarking Continual Pretraining for Protein Language Models
arXiv:2603.00253v1 Announce Type: new Abstract: Protein language models (pLMs) have recently gained significant attention for their ability to uncover relationships between sequence, structure, and function from evolutionary statistics, thereby accelerating therapeutic drug discovery. These models learn from large protein databases that are continuously updated by the biology community and whose dynamic nature motivates the application of continual learning, not only to keep up with the ever-growing data, but also as an opportunity to take advantage of the temporal meta-information that is created during this process. As a result, we introduce the Continual Pretraining of Protein Language Models (CoPeP) benchmark, a novel benchmark for evaluating continual learning approaches on pLMs. Specifically, we curate a sequence of protein datasets derived from the UniProt Knowledgebase spanning a decade and define metrics to assess pLM performance across 31 protein understanding tasks. We eval
arXiv:2603.00253v1 Announce Type: new Abstract: Protein language models (pLMs) have recently gained significant attention for their ability to uncover relationships between sequence, structure, and function from evolutionary statistics, thereby accelerating therapeutic drug discovery. These models learn from large protein databases that are continuously updated by the biology community and whose dynamic nature motivates the application of continual learning, not only to keep up with the ever-growing data, but also as an opportunity to take advantage of the temporal meta-information that is created during this process. As a result, we introduce the Continual Pretraining of Protein Language Models (CoPeP) benchmark, a novel benchmark for evaluating continual learning approaches on pLMs. Specifically, we curate a sequence of protein datasets derived from the UniProt Knowledgebase spanning a decade and define metrics to assess pLM performance across 31 protein understanding tasks. We evaluate several methods from the continual learning literature, including replay, unlearning, and plasticity-based methods, some of which have never been applied to models and data of this scale. Our findings reveal that incorporating temporal meta-information improves perplexity by up to 7% even when compared to training on data from all tasks jointly. Moreover, even at scale, several continual learning methods outperform naive continual pretraining. The CoPeP benchmark offers an exciting opportunity to study these methods at scale in an impactful real-world application.
Executive Summary
This article introduces CoPeP, a novel benchmark for evaluating continual learning approaches on protein language models (pLMs). The CoPeP benchmark comprises a decade-long sequence of protein datasets from the UniProt Knowledgebase and evaluates the performance of several continual learning methods across 31 protein understanding tasks. The study finds that incorporating temporal meta-information improves perplexity by up to 7% and that several continual learning methods outperform naive continual pretraining. This breakthrough has significant implications for the development of therapeutic drugs and highlights the importance of continually updating protein databases to leverage temporal meta-information. The study's findings demonstrate the potential of continual learning for protein language models and underscore the need for further research in this area.
Key Points
- ▸ CoPeP introduces a novel benchmark for evaluating continual learning approaches on pLMs.
- ▸ The benchmark comprises a decade-long sequence of protein datasets from the UniProt Knowledgebase.
- ▸ Several continual learning methods outperform naive continual pretraining, demonstrating the potential of continual learning for pLMs.
Merits
Strength in Methodology
The study employs a rigorous methodology, curating a comprehensive dataset and evaluating the performance of various continual learning methods across multiple tasks.
Strength in Impact
The study's findings have significant implications for the development of therapeutic drugs and highlight the importance of continually updating protein databases to leverage temporal meta-information.
Demerits
Limitation in Scope
The study focuses on a specific application (protein language models) and may not be generalizable to other domains or tasks.
Limitation in Data
The study relies on a decade-long sequence of protein datasets from the UniProt Knowledgebase, which may not be representative of the broader protein landscape.
Expert Commentary
The study's findings are a significant breakthrough in the field of protein language models and highlight the potential of continual learning for this application. The CoPeP benchmark offers a rigorous framework for evaluating the performance of various continual learning methods and provides a valuable resource for researchers and practitioners. However, further research is needed to fully explore the implications of this study and to develop more effective approaches for incorporating temporal meta-information into protein language models. Additionally, the study's focus on a specific application may limit its generalizability to other domains or tasks. Nevertheless, this study has the potential to drive significant advancements in the field of protein research and therapeutic drug discovery.
Recommendations
- ✓ Researchers should explore the application of continual learning and temporal meta-information to other domains and tasks.
- ✓ The development of more effective approaches for incorporating temporal meta-information into protein language models is a priority area for research.