ESM All-Atom: Multi-Scale Protein Language Model for Unified Molecular Modeling
Kangjie Zheng, Siyu Long, Tianyu Lu, Junwei Yang, Xinyu Dai, Ming Zhang, Zaiqing Nie, Wei-Ying Ma, Hao Zhou
摘要
Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ESM-AA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling. ESM-AA achieves this by pretraining on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ESM-AA surpasses previous methods in proteinmolecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins.1
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引用它的顶会 Paper4
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它引用的顶会 Paper20
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- Motif-based Graph Self-Supervised Learning for Molecular Property PredictionZaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu 等NeurIPS 2021 · 被引用 385 次
- Transformer protein language models are unsupervised structure learnersRoshan Rao, Joshua Meier, Tom Sercu, Sergey Ovchinnikov 等ICLR 2021 · 被引用 366 次
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