Update pub.bib (#291)

Author: cloudcloud666

source/_data/pub.bib

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+@Article{Chen_arXiv_2025_p2506.00880,
+    author =   {Zhuo Chen and Yizhen Zheng and Huan Yee Koh and Hongxin Xiang and
+             Linjiang Chen and Wenjie Du and Yang Wang},
+    title =    {{ModuLM: Enabling Modular and Multimodal Molecular Relational Learning
+             with Large Language Models}},
+    journal =  {arXiv},
+    year =     2025,
+    pages =    {2506.00880},
+    doi =      {10.48550/arXiv.2506.00880},
+    abstract = {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.},
+}
+
 @Article{Zeng_JChemTheoryComput_2025_v21_p4375,
     author =   {Jinzhe Zeng and Duo Zhang and Anyang Peng and Xiangyu Zhang and Sensen
              He and Yan Wang and Xinzijian Liu and Hangrui Bi and Yifan Li and Chun