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 will improve both MPP regression and classification tasks by upto 3.98% and 1.72%, respectively. We also discover that the utilizing 3-dimensional information with 1-dimensional and 2-dimensional information simultaneously can substantially enhance MPP upto 4.2%. The two consolidated insights offer crucial guidance for future advancements in drug discovery.

翻译：分子性质的精确预测对于药物开发（特别是虚拟筛选和化合物优化）的进展至关重要。近年来，众多深度学习方法的引入在增强分子性质预测（MPP）方面展现出显著潜力，尤其是在提升准确性和对分子结构的洞察力方面。然而，两个关键问题随之产生：领域知识的整合是否能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能产生更精确的结果？为探究这些问题，我们基于多种基准对近期深度学习方法进行了全面综述与定量分析。研究发现，整合分子信息可分别将MPP回归任务和分类任务的性能提升高达3.98%和1.72%。同时，将三维信息与一维、二维信息联合使用，能使MPP性能显著提升高达4.2%。这两个整合性洞见为未来药物发现领域的进展提供了关键指导。