TY  - JOUR
T1  - Enhancing Molecular Understanding in LLMs through Multimodal Graph-SMILES Representations
JO  - Journal of KIISE, JOK
PY  - 2025
DA  - 2025/1/14
DO  - 10.5626/JOK.2025.52.5.379
KW  - Molecule
KW  - graph
KW  - Multimodal
KW  - Large Language Models (LLMs)
AB  - Recent advancements in large language models (LLMs) have shown remarkable performace across various tasks, with increasing focus on multimodal research. Notably, BLIP-2 can enhance performance by efficiently aligning images and text using a Q-Former, aided by an image encoder pre-trained on multimodal data. Inspired by this, the MolCA model extends BLIP-2 to the molecular domain to improve performance. However, the graph encoder in MolCA is pre-trained on unimodal data, necessitating updates during model training, which is a limitation. Therefore, this paper replaced it with a graph encoder pre-trained on multimodal data and frozen while training the model. Experimental results showed that using the graph encoder pre-trained on multimodal data generally enhanced performance. Additionally, unlike the graph encoder pre-trained on unimodal data, which performed better when updated, the graph encoder pre-trained on multimodal data achieved superior results across all metrics when frozen.