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.

综合审视和数量分析基于各种基准的最新深度学习方法，我们发现融合分子信息不仅能提高分子属性预测回归和分类任务的准确性，还能通过同时利用 1 维和 2 维信息的 3 维信息大幅增强分子属性预测进展，在药物研发中具有重要指导意义。

领域知识和多模态对智能分子性质预测的影响：一项系统调查

The Impact of Domain Knowledge and Multi-Modality on Intelligent  Molecular Property Prediction: A Systematic Survey

A lot of deep learning (DL) research these days is mainly focused on
improving on quantitative metrics regardless of other factors. In human
centered applications, like skin lesion classification in dermatology,
DL-driven clinical decision support systems are still in their infancy due to
the limited transparency of their decision-making process. Moreover, the lack
of procedures that can explain the behavior of trained DL algorithms leads to
almost no trust from the clinical physicians. To diagnose skin lesions,
dermatologists rely on both visual assessment of the disease and the data
gathered from the anamnesis of the patient. Data-driven algorithms dealing with
multi-modal data are limited by the separation of feature-level and
decision-level fusion procedures required by convolutional architectures. To
address this issue, we enable single-stage multi-modal data fusion via the
attention mechanism of transformer-based architectures to aid in the diagnosis
of skin diseases. Our method beats other state-of-the-art single- and
multi-modal DL architectures in both image rich and patient-data rich
environments. Additionally, the choice of the architecture enables native
interpretability support for the classification task both in image and metadata
domain with no additional modifications necessary.

本研究利用 transformer-based 架构实现了单阶段多模态数据融合，并在图像与病患数据丰富的环境下击败了其他单模态和多模态深度学习架构，同时，所选架构还提供了本地可解释性支持。