Newest papers

ProtModernBERT: Compute-Efficient Protein Language Modeling

Alberto Martínez, Natalia Flechas Manrique, Llorenç Espinosa-Portalés, Luc Andrea · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.19738 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

大規模なタンパク質言語モデルは、セクション機能関係を学習し、実験的な設計を指導するための強力なデータフレームワークを提供します。しかし、建築的改善は、特にリソース制限された研究ラボのための低コンピューティングの関連性のモデルでは未調査されています。我々は、エンコーダーだけのトランスフォーマーアーキテクチャのためのトレーニングコストは、推奨的なパフォーマンス/コンピューティング効率の交換を提供する、現代の建築的改良を活用することによって削減することができることを示します。我々は、アミノ酸セクションで事前に訓練された近代化されたBERTスタイルのモデルであるProtModernBERTを導入し、プロトベンGymのゼロショット機能ベ

Machine translation (Meta M2M100); the English original follows.

Abstract

Large protein language models provide a powerful framework for learning sequence-function relationships and guiding experimental design. However, architectural improvements are underexplored, especially in low-compute regimes of relevance for resource-constrained research laboratories. We show that the training cost for encoder-only transformer architectures can be reduced by leveraging modern architectural refinements, offering an encouraging performance / compute-efficiency trade-off. We introduce ProtModernBERT, a modernized BERT-style model pre-trained on amino acid sequences, and compare it with ProtBert, Amplify, and ESM2 across a set of supervised structural tasks -encompassing secondary structure prediction, disorder prediction, and subcellular localization- and ProteinGym's zero-shot functional benchmark. We find that ProtModernBERT matches or closely tracks the performance of Amplify-120M and ProtBert with up to 80-90\% reduction in pre-training FLOPs, and narrows the gap to ESM2 when controlling for pre-training. Our analysis using a cluster-based validation protocol for hyperparameter selection and randomly initialized models disentangles the contributions of architecture and data, showing that modern components provide stronger inductive biases that are more consequential than parameter count in low-compute regimes.

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Topics: Computer science, Artificial intelligence, Hyperparameter, Machine learning, Relevance (law), Selection (genetic algorithm)

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