HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens
arXiv:2512.15133v3 Announce Type: replace-cross Abstract: Proteins inherently possess a consistent sequence-structure duality. The abundance of protein sequence data, which can be readily represented as discrete tokens, has driven fruitful developments in protein language models (pLMs). A key remaining challenge, however, is how to effectively integrate continuous structural knowledge into pLMs. Current methods often discretize protein structures to accommodate the language modeling framework, which inevitably results in the loss of fine-grained information and limits the performance potential
The rapid advancement in language models offers new paradigms for integrating complex biological data, pushing the boundaries of protein modeling beyond sequence-only approaches.
Improved protein language models that integrate sequence and structure more effectively will accelerate drug discovery, materials science, and synthetic biology applications, creating new economic opportunities.
The ability to model protein sequence and structure jointly with fine-grained continuous data will overcome limitations of current discrete representations, leading to more accurate and predictive protein models.
- · Biopharmaceutical companies
- · Synthetic biology startups
- · AI-driven drug discovery platforms
- · Materials science innovators
- · Traditional protein modeling methods
- · Drug discovery reliant on high-throughput screening without advanced computation
More accurate and efficient protein design will become possible, reducing development cycles for new biologics and enzymes.
This acceleration could lead to a wave of novel therapeutic compounds and biomaterials entering the market faster and more cost-effectively.
The enhanced capability to engineer proteins could reshape entire industries, from medicine to manufacturing, through biomimicry and synthetic biological processes.
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Read at arXiv cs.AI