ModuLM让分子关系学习可灵活组合模型，提升研究效率。
ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models
- 模块化设计支持多种分子结构编码与交互层动态组合
- 可构建超5万种模型配置，覆盖2D/3D分子表示与主流LLM
- 适合分子互作研究者快速搭建与对比不同模型架构
分子关系学习（MRL）旨在理解分子对之间的相互作用，在推动生物化学研究中具有关键作用。随着大语言模型（LLM）的发展，越来越多研究尝试将MRL与LLM结合并取得显著进展。然而，多样化的LLM和分子结构编码器的涌现极大扩展了模型空间，带来基准测试难题。当前尚无支持灵活输入格式与动态架构切换的LLM框架。为此，我们提出ModuLM，一个支持灵活构建基于LLM的分子关系学习模型并兼容多种分子表征的框架。ModuLM提供丰富模块组件，包括8类2D分子图编码器、11类3D分子构象编码器、7类交互层及7个主流LLM主干网络。得益于高度灵活的模型组装机制，可动态构建超过50,000种不同模型配置。此外，我们提供了全面结果，验证了ModuLM在支持基于LLM的MRL任务中的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。