融合三维构象的分子多模态表示学习新基准
MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning
- 用Transformer统一处理SMILES、2D图和3D构象三模态输入
- 通过构象聚合提升对分子动态结构的建模能力，性能达新高
- 模块化设计易适配任务，适合分子性质预测研究者
本文提出一种基于Transformer的简单但有效的多模态分子表征学习基线，整合三种不同模态：SMILES字符串、2D图表示和分子的3D构象。方法的关键在于对3D构象进行聚合，使模型能考虑分子可采取多种构象这一重要特性。各模态的令牌分别由特定编码器提取：使用Transformer处理SMILES，消息传递神经网络处理2D图，等变神经网络处理3D构象。该框架灵活且模块化，便于替换编码器，适用于不同分子任务。提取的令牌被组合成统一的多模态序列，由下游Transformer完成预测。为高效扩展至大规模多模态数据集，采用Flash Attention 2和bfloat16精度。尽管结构简单，该方法在多个数据集上达到当前最优性能，验证了其作为多模态分子表征学习强基线的有效性。
原文摘要 · Abstract (English)
In this work, we propose a simple transformer-based baseline for multimodal molecular representation learning, integrating three distinct modalities: SMILES strings, 2D graph representations, and 3D conformers of molecules. A key aspect of our approach is the aggregation of 3D conformers, allowing the model to account for the fact that molecules can adopt multiple conformations-an important factor for accurate molecular representation. The tokens for each modality are extracted using modality-specific encoders: a transformer for SMILES strings, a message-passing neural network for 2D graphs, and an equivariant neural network for 3D conformers. The flexibility and modularity of this framework enable easy adaptation and replacement of these encoders, making the model highly versatile for different molecular tasks. The extracted tokens are then combined into a unified multimodal sequence, which is processed by a downstream transformer for prediction tasks. To efficiently scale our model for large multimodal datasets, we utilize Flash Attention 2 and bfloat16 precision. Despite its simplicity, our approach achieves state-of-the-art results across multiple datasets, demonstrating its effectiveness as a strong baseline for multimodal molecular representation learning.
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