用虚拟3D环境预训练分子关系模型，提升药物与催化剂设计性能
3D Interaction Geometric Pre-training for Molecular Relational Learning
- 构建虚拟3D交互环境，让2D模型学习分子三维几何信息
- 在40个任务中最高提升24.93%性能，涵盖分布外和外推场景
- 适合分子建模、药物发现领域研究者快速迁移使用
分子关系学习（MRL）是快速发展的领域，关注分子间相互作用动态，对催化剂设计与药物发现至关重要。尽管已有进展，早期方法仅依赖分子二维拓扑结构，因获取三维交互几何成本过高而受限。本文提出一种新型3D几何预训练策略（3DMRL），构建3D虚拟交互环境，克服传统量子计算方法成本高的问题。通过该环境，3DMRL使2D MRL模型学习分子相互作用的全局与局部3D几何特征。在多个真实数据集上的广泛实验表明，该方法在40项任务中性能最高提升24.93%，涵盖分布外和外推场景。代码已公开于https://github.com/Namkyeong/3DMRL。
原文摘要 · Abstract (English)
Molecular Relational Learning (MRL) is a rapidly growing field that focuses on understanding the interaction dynamics between molecules, which is crucial for applications ranging from catalyst engineering to drug discovery. Despite recent progress, earlier MRL approaches are limited to using only the 2D topological structure of molecules, as obtaining the 3D interaction geometry remains prohibitively expensive. This paper introduces a novel 3D geometric pre-training strategy for MRL (3DMRL) that incorporates a 3D virtual interaction environment, overcoming the limitations of costly traditional quantum mechanical calculation methods. With the constructed 3D virtual interaction environment, 3DMRL trains 2D MRL model to learn the global and local 3D geometric information of molecular interaction. Extensive experiments on various tasks using real-world datasets, including out-of-distribution and extrapolation scenarios, demonstrate the effectiveness of 3DMRL, showing up to a 24.93% improvement in performance across 40 tasks. Our code is publicly available at https://github.com/Namkyeong/3DMRL.
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