🤖 AI Summary
This work proposes VQ-Atom, a novel molecular representation framework that addresses the limited chemical semantics of existing notations such as SMILES, which struggle to effectively capture local structural patterns. VQ-Atom introduces, for the first time, semantic-driven discretization into molecular representation by leveraging graph neural networks to learn atomic embeddings and integrating vector quantization to construct a chemistry-aware atomic codebook. This yields discrete semantic tokens with explicit chemical meaning, enabling the formulation of a molecular language suitable for Transformer-based pretraining. Notably, the method operates without requiring 3D structural information and demonstrates significant performance gains over conventional tokenization strategies in protein–ligand interaction prediction tasks, thereby validating the efficacy and advantage of semantically informed token representations in molecular language modeling.
📝 Abstract
Molecular representation learning has become a central approach in AI-driven drug discovery, yet existing molecular tokenizations such as SMILES remain largely syntactic and do not naturally align with chemically meaningful substructures. In this work, we introduce VQ-Atom, a semantic discretization framework that converts continuous atom-level graph representations into discrete tokens corresponding to local chemical environments. Using graph neural network embeddings and vector quantization, atoms are assigned to codebook entries representing chemically meaningful atomic contexts. These discrete tokens define a molecular language suitable for Transformer-based pretraining.
We evaluate VQ-Atom in protein-ligand interaction prediction under a protein-cold split setting without relying on 3D structural information. Experimental results show that VQ-Atom consistently improves predictive performance compared to conventional tokenization approaches, suggesting that semantically grounded discretization can substantially enhance molecular representation learning. Our findings indicate that token design itself plays a critical role in enabling effective language modeling for chemistry.