Enhancing Molecular Understanding in LLMs through Multimodal Graph-SMILES Representations

http://doi.org/10.5626/JOK.2025.52.5.379

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.

New Transformer Model to Generate Molecules for Drug Discovery

Yu-Bin Hong, Kyungjun Lee, DongNyenog Heo, Heeyoul Choi

http://doi.org/10.5626/JOK.2023.50.11.976

Among various generative models, recurrent neural networks (RNNs) based models have achieved state-of-the-art performance in the drug generation task. To overcome the long-term dependency problem that RNNs suffer from, Transformer-based models were proposed for the task. However, the Transformer models showed worse performances than the RNNs models in the drug generation task, and we believe it was because the Transformer models were over-parameterized with the over-fitting problem. To avoid the problem, in this paper, we propose a new Transformer model by replacing the large decoder with simple feed-forward layers. Experiments confirmed that our proposed model outperformed the previous state-of-the-art baseline in major evaluation metrics while preserving other minor metrics with a similar level of performance. Furthermore, when we applied our model to generate candidate molecules against SARs-CoV-2 (COVID-19) virus, the generated molecules were more effective than drugs in commercial market such as Paxlovid, Molnupiravir, and Remdesivir.