ModuLM：利用大语言模型实现模块化与多模态分子关系学习
ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models
AI总结：
ModuLM提出一个模块化框架，通过组合多种2D/3D分子编码器、交互层和LLM骨干，支持超过5万种模型配置，解决分子关系学习中的灵活建模与公平基准测试问题。
摘要：
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发挥着关键作用。随着大语言模型（LLMs）的近期发展，越来越多的研究探索了MRL与LLMs的整合，并取得了有前景的成果。然而，多样化的LLMs和分子结构编码器的日益普及显著扩大了模型空间，给基准测试带来了重大挑战。目前，尚无LLM框架能够同时支持灵活的分子输入格式和动态架构切换。为应对这些挑战、减少冗余编码并确保公平的模型比较，我们提出了ModuLM，一个旨在支持灵活的基于LLM的模型构建和多样化分子表示的框架。ModuLM提供了丰富的模块化组件套件，包括8种二维分子图编码器、11种三维分子构象编码器、7种交互层和7种主流LLM骨干网络。凭借其高度灵活的模型组装机制，ModuLM能够动态构建超过50,000种不同的模型配置。此外，我们提供了全面的结果，以证明ModuLM在支持基于LLM的MRL任务方面的有效性。
英文摘要：
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.
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