arXiv:2606.12113v1 Announce Type: new Abstract: Transformer-based language models for SMILES strings suffer from a locality gap: standard character-level tokenization fragments chemically meaningful motifs, forcing models to repeatedly learn local syntax at the expense of long-range dependencies. To address this without disrupting standard tokenizers, we propose MolGram, which integrates a conditional $n$-gram memory module into molecular language models. MolGram maps local string patterns to learned embeddings via scalable hash lookups and dynamically injects this regional context into hidden
The paper addresses a known limitation (locality gap) in current transformer-based molecular language models, indicating ongoing innovation in AI for specialized scientific domains.
This development could significantly enhance the capabilities and efficiency of AI models applied to molecular design and drug discovery, potentially accelerating innovation in synthetic biology and materials science.
Molecular language models may become more adept at understanding and generating complex molecular structures without requiring fundamental changes to existing tokenization methods.
- · Pharmaceutical companies
- · Biotech firms
- · AI model developers
- · Materials science research
- · Traditional drug discovery methods
Improved accuracy and efficiency of molecular language models for chemical tasks.
Faster discovery and optimization of new drug candidates and advanced materials.
Reduced costs and accelerated timelines for research and development in chemistry and biology, leading to new commercial products.
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Read at arXiv cs.CL