VQ-Atom: Semantic Discretization of Local Atomic Environments for Molecular Representation Learning
arXiv:2605.16823v2 Announce Type: replace Abstract: Large language models succeed by combining large-scale pretraining with meaningful discrete tokens. In molecular machine learning, SMILES is widely used as a token representation, but it is primarily a linearization format for molecular graphs rather than a semantic decomposition of chemistry. We propose VQ-Atom, a semantic tokenization framework that assigns discrete atom-level tokens based on local chemical environments via vector quantization. Unlike SMILES tokens, VQ-Atom tokens encode graph-local chemical context and are aligned with mol
The increasing success of large language models is inspiring researchers to apply similar tokenization principles to other complex data domains like molecular machine learning.
This development could significantly enhance the ability of AI to understand and design molecules, accelerating discovery in drug development and material science.
Molecular representation learning gains a more semantically rich and context-aware tokenization method, moving beyond simple linearization for improved model performance.
- · Drug discovery companies
- · Material science research
- · AI for chemistry platforms
- · Pharmaceutical industry
- · Traditional molecular simulation methods
- · SMILES-centric molecular ML approaches
Improved molecular property prediction and generative model efficacy.
Faster identification and synthesis of novel compounds for various applications.
Potential for autonomous molecular design agents reducing human-in-the-loop requirements for discovery.
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Read at arXiv cs.LG