ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913871230402560 |
|---|---|
| author | Chen, Zhuo Zheng, Yizhen Koh, Huan Yee Xiang, Hongxin Chen, Linjiang Du, Wenjie Wang, Yang |
| author_facet | Chen, Zhuo Zheng, Yizhen Koh, Huan Yee Xiang, Hongxin Chen, Linjiang Du, Wenjie Wang, Yang |
| contents | Molecular Relational Learning (MRL) aims to understand interactions between molecular pairs, playing a critical role in advancing biochemical research. With the recent development of large language models (LLMs), a growing number of studies have explored the integration of MRL with LLMs and achieved promising results. However, the increasing availability of diverse LLMs and molecular structure encoders has significantly expanded the model space, presenting major challenges for benchmarking. Currently, there is no LLM framework that supports both flexible molecular input formats and dynamic architectural switching. To address these challenges, reduce redundant coding, and ensure fair model comparison, we propose ModuLM, a framework designed to support flexible LLM-based model construction and diverse molecular representations. ModuLM provides a rich suite of modular components, including 8 types of 2D molecular graph encoders, 11 types of 3D molecular conformation encoders, 7 types of interaction layers, and 7 mainstream LLM backbones. Owing to its highly flexible model assembly mechanism, ModuLM enables the dynamic construction of over 50,000 distinct model configurations. In addition, we provide comprehensive results to demonstrate the effectiveness of ModuLM in supporting LLM-based MRL tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00880 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models Chen, Zhuo Zheng, Yizhen Koh, Huan Yee Xiang, Hongxin Chen, Linjiang Du, Wenjie Wang, Yang Machine Learning Artificial Intelligence Biomolecules Quantitative Methods Molecular Relational Learning (MRL) aims to understand interactions between molecular pairs, playing a critical role in advancing biochemical research. With the recent development of large language models (LLMs), a growing number of studies have explored the integration of MRL with LLMs and achieved promising results. However, the increasing availability of diverse LLMs and molecular structure encoders has significantly expanded the model space, presenting major challenges for benchmarking. Currently, there is no LLM framework that supports both flexible molecular input formats and dynamic architectural switching. To address these challenges, reduce redundant coding, and ensure fair model comparison, we propose ModuLM, a framework designed to support flexible LLM-based model construction and diverse molecular representations. ModuLM provides a rich suite of modular components, including 8 types of 2D molecular graph encoders, 11 types of 3D molecular conformation encoders, 7 types of interaction layers, and 7 mainstream LLM backbones. Owing to its highly flexible model assembly mechanism, ModuLM enables the dynamic construction of over 50,000 distinct model configurations. In addition, we provide comprehensive results to demonstrate the effectiveness of ModuLM in supporting LLM-based MRL tasks. |
| title | ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models |
| topic | Machine Learning Artificial Intelligence Biomolecules Quantitative Methods |
| url | https://arxiv.org/abs/2506.00880 |