领域知识与多模态对智能分子性质预测的影响：一项系统性综述
Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey
AI总结：
本文系统综述并定量分析了领域知识与多模态融合对分子性质预测的影响，发现整合分子信息及结合1D、2D、3D多模态数据可显著提升回归和分类性能，为药物发现提供指导。
摘要：
分子性质的精确预测对于药物开发至关重要，尤其是在虚拟筛选和化合物优化方面。近年来，大量基于深度学习的方法被提出，在提升分子性质预测（MPP）方面展现出显著潜力，特别是在提高准确性以及加深对分子结构的理解方面。然而，两个关键问题随之而来：领域知识的整合是否能够提高分子性质预测的准确性？采用多模态数据融合是否比单一数据源方法产生更精确的结果？为了探讨这些问题，我们基于多个基准对近期的深度学习方法进行了全面回顾和定量分析。我们发现，整合分子信息能够显著改善回归任务和分类任务中的分子性质预测（MPP）。具体而言，以均方根误差（RMSE）的降低来衡量，回归性能提升最高可达4.0%；以受试者工作特征曲线下面积（ROC-AUC）来衡量，分类性能提升最高可达1.7%。我们还发现，用一维SMILES丰富二维图可将回归任务的多模态学习性能提升最高达9.1%，而用三维信息增强二维图可将分类任务的性能提升最高达13.2%，这两项提升均使用ROC-AUC进行衡量。这两项综合性的见解为未来药物发现的进展提供了重要指导。
英文摘要：
The precise prediction of molecular properties is essential for advancements in drug development, particularly in virtual screening and compound optimization. The recent introduction of numerous deep learning-based methods has shown remarkable potential in enhancing molecular property prediction (MPP), especially improving accuracy and insights into molecular structures. Yet, two critical questions arise: does the integration of domain knowledge augment the accuracy of molecular property prediction and does employing multi-modal data fusion yield more precise results than unique data source methods? To explore these matters, we comprehensively review and quantitatively analyze recent deep learning methods based on various benchmarks. We discover that integrating molecular information significantly improves molecular property prediction (MPP) for both regression and classification tasks. Specifically, regression improvements, measured by reductions in root mean square error (RMSE), are up to 4.0%, while classification enhancements, measured by the area under the receiver operating characteristic curve (ROC-AUC), are up to 1.7%. We also discover that enriching 2D graphs with 1D SMILES boosts multi-modal learning performance for regression tasks by up to 9.1%, and augmenting 2D graphs with 3D information increases performance for classification tasks by up to 13.2%, with both enhancements measured using ROC-AUC. The two consolidated insights offer crucial guidance for future advancements in drug discovery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
