ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
This work addresses the problem of integrating protein and small molecule modeling for researchers in computational biology and drug discovery, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackles the limitation of protein language models operating only at the residue scale by proposing ESM-AA, a multi-scale model that enables atom-scale and residue-scale unified molecular modeling, achieving superior performance in protein-molecule tasks compared to previous methods.
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 pre-training 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 protein-molecule 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. The source codes of ESM-AA are publicly released at https://github.com/zhengkangjie/ESM-AA.