Speaking the Native Language of LLMs: A Discrete Architecture for Molecular Comprehension

Large Language Models (LLMs) have emerged as a powerful paradigm for scientific discovery, yet adapting them to natively comprehend complex molecular structures remains a fundamental challenge. To capture structural nuances, the community has increasingly shifted towards multimodal architectures. Existing molecular LLMs typically integrate 2D and 3D modalities via continuous projection, mapping pretrained molecular embeddings directly into the backbone LLM. However, mapping continuous embeddings from pretrained encoders into the discrete token space of LLMs creates an inherent modality gap, hindering effective semantic transfer and limiting their performance. To address these challenges, we introduce MolLingua, a token-centric, dual-modal framework designed for native molecular understanding. Specifically, we leverage a dual-branch Residual Vector Quantization (RVQ) engine to discretize these heterogeneous, high-dimensional spatial 2D and 3D features into compact code sequences rather than relying solely on continuous projections. By integrating them as learnable tokens within the LLM vocabulary, MolLingua translates structural chemistry into the native discrete language of LLMs. Extensive experiments demonstrate that our fully discrete approach effectively aligns spatial knowledge with language models, achieving state-of-the-art performance in complex chemical reasoning and competitive results in generative tasks. Our work contributes an effective, unified framework for propelling essential applications of LLMs in biomedicine.