aiqbench analysis
MolSight addresses the critical gap in current molecular AI by integrating graph topology directly into vision-language models, enabling superior reasoning of complex chemical structures from images. This advancement is significant for drug discovery and molecular design workflows where accurate structural alignment between visual data and symbolic semantics is essential. By overcoming limitations in existing VLMs regarding topological modeling, this framework sets a new benchmark for unified understanding in computational chemistry.
MolSight: A Graph-Aware Vision-Language Model for Unified Chemical Image Understanding
arXiv:2607.01982v2 Announce Type: replace-cross Abstract: Using molecular large language models (LLMs) as a unified framework for understanding molecular structures and functions is emerging as a new trend in tasks such as molecular design and drug discovery. However, these models struggle to fully capture the visual representation of molecular structures, limiting their potential. While existing molecular vision-language models (VLMs) show promise, they still face challenges in structural alignment and lack the necessary topological modeling for accurate molecular understanding. To address this, we propose MolSight, a graph-aware vision-language model framework designed to enhance the understanding of molecular images by VLMs. MolSight integrates a Molecular Topology Module to inject chemical-bond adjacency information into vision tokens, and a Molecular Grounding Module to align visual features with chemical symbolic semantics. Our experiments demonstrate that MolSight significantly outperforms existing VLMs, molecular LLMs, and task-specific models across multiple chemical visual understanding tasks, achieving a new level of molecular image reasoning in complex chemical scenarios.
Abstract:Using molecular large language models (LLMs) as a unified framework for understanding molecular structures and functions is emerging as a new trend in tasks such as molecular design and drug discovery. However, these models struggle to fully capture the visual representation of molecular structures, limiting their potential. While existing molecular vision-language models (VLMs) show promise, they still face challenges in structural alignment and lack the necessary topological modeling for accurate molecular understanding. To address this, we propose MolSight, a graph-aware vision-language model framework designed to enhance the understanding of molecular images by VLMs. MolSight integrates a Molecular Topology Module to inject chemical-bond adjacency information into vision tokens, and a Molecular Grounding Module to align visual features with chemical symbolic semantics. Our experiments demonstrate that MolSight significantly outperforms existing VLMs, molecular LLMs, and task-specific models across multiple chemical visual understanding tasks, achieving a new level of molecular image reasoning in complex chemical scenarios.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
arXiv:2607.01982 [cs.CV]
(or arXiv:2607.01982v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.01982 Focus to learn more arXiv-issued DOI via DataCite
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