← 研踪 PaperTrailarXiv:2609.37675 · v2
LEMON-ZEST: Evolution-Informed Tokenization for Efficient Protein Language Modeling
首次提交 2026-09-29 22:27
版本更新 2026-11-07 16:29
雷达观测 2026-11-10 11:14
摘要
Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and structure-based tasks, yet the potential of tokenization remains underexploited. Unlike human language, proteins preserve structure despite extensive sequence variation, a property standard tokenization strategies fundamentally fail to capture. We introduce ZEST (Zoned Encoding of Sequence Traits), an evolution-informed vocabulary derived from conserved regions of multiple sequence alignments. ZEST allows embedding domain-level biological priors directly at the tokenization stage rather than learning them implicitly through scale. ZEST natively compresses sequences to an average token length of 3.3 residues, enabling our model to process $\geq3380$ residues within a standard 1024-token context window. Building on this, we present LEMON (Layered Extraction of Molecular Ordering from Nature), a compact 200M-parameter sequence-based model for detection of remote homology between protein sequences trained on a single H100 GPU for one week. Despite its modest size, LEMON outperforms models of 600M to 3B parameters and is on-par with TM-Vec. Our results demonstrate that evolution-informed tokenization can substitute for massive parameter scaling, opening a new direction for efficient, biologically-grounded protein representation learning. All code and model weights are publicly available (at \href{https://github.com/LDWLab/ZEST}{github.com/LDWLab/ZEST}) under the MIT license.
作者备注：Accepted to the 40th Annual Conference on Neural Information Processing Systems (NeurIPS 2026). Camera-ready version
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v2 · 2026-11-07 16:29 · 最新观测版本
LEMON-ZEST: Evolution-Informed Tokenization for Efficient Protein Language Modeling
Protein Language Models (PLMs) have made remarkable progress following scaling laws established in natural language processing across sequence- and structure-based tasks, yet the potential of tokenization remains underexploited. Unlike human language, proteins preserve structure despite extensive sequence variation, a property standard tokenization strategies fundamentally fail to capture. We introduce ZEST (Zoned Encoding of Sequence Traits), an evolution-informed vocabulary derived from conserved regions of multiple sequence alignments. ZEST allows embedding domain-level biological priors directly at the tokenization stage rather than learning them implicitly through scale. ZEST natively compresses sequences to an average token length of 3.3 residues, enabling our model to process $\geq3380$ residues within a standard 1024-token context window. Building on this, we present LEMON (Layered Extraction of Molecular Ordering from Nature), a compact 200M-parameter sequence-based model for detection of remote homology between protein sequences trained on a single H100 GPU for one week. Despite its modest size, LEMON outperforms models of 600M to 3B parameters and is on-par with TM-Vec. Our results demonstrate that evolution-informed tokenization can substitute for massive parameter scaling, opening a new direction for efficient, biologically-grounded protein representation learning. All code and model weights are publicly available (at \href{https://github.com/LDWLab/ZEST}{github.com/LDWLab/ZEST}) under the MIT license.
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