Molecular property prediction is a key component of AI-driven drug discovery
and molecular characterization learning. Despite recent advances, existing
methods still face challenges such as limited ability to generalize, and
inadequate representation of learning from unlabeled data, especially for tasks
specific to molecular structures. To address these limitations, we introduce
DIG-Mol, a novel self-supervised graph neural network framework for molecular
property prediction. This architecture leverages the power of contrast learning
with dual interaction mechanisms and unique molecular graph enhancement
strategies. DIG-Mol integrates a momentum distillation network with two
interconnected networks to efficiently improve molecular characterization. The
framework's ability to extract key information about molecular structure and
higher-order semantics is supported by minimizing loss of contrast. We have
established DIG-Mol's state-of-the-art performance through extensive
experimental evaluation in a variety of molecular property prediction tasks. In
addition to demonstrating superior transferability in a small number of
learning scenarios, our visualizations highlight DIG-Mol's enhanced
interpretability and representation capabilities. These findings confirm the
effectiveness of our approach in overcoming challenges faced by traditional
methods and mark a significant advance in molecular property prediction.

DIG-Mol 是一种新颖的自我监督图神经网络框架，可以有效地改善分子特征化，通过对比学习的力量和独特的分子图增强策略，来提取关于分子结构和高阶语义的关键信息，并证实了其在分子性质预测方面的出色性能。

对比双交互图神经网络用于分子性质预测

Contrastive Dual-Interaction Graph Neural Network for Molecular Property  Prediction

Molecular docking, a pivotal computational tool for drug discovery, predicts
the binding interactions between small molecules (ligands) and target proteins
(receptors). Conventional physics-based docking tools, though widely used, face
limitations in precision due to restricted conformational sampling and
imprecise scoring functions. Recent endeavors have employed deep learning
techniques to enhance docking accuracy, but their generalization remains a
concern due to limited training data. Leveraging the success of extensive and
diverse data in other domains, we introduce HelixDock, a novel approach for
site-specific molecular docking. Hundreds of millions of binding poses are
generated by traditional docking tools, encompassing diverse protein targets
and small molecules. Our deep learning-based docking model, a SE(3)-equivariant
network, is pre-trained with this large-scale dataset and then fine-tuned with
a small number of precise receptor-ligand complex structures. Comparative
analyses against physics-based and deep learning-based baseline methods
highlight HelixDock's superiority, especially on challenging test sets. Our
study elucidates the scaling laws of the pre-trained molecular docking models,
showcasing consistent improvements with increased model parameters and
pre-train data quantities. Harnessing the power of extensive and diverse
generated data holds promise for advancing AI-driven drug discovery.

通过利用大规模和多样化的生成数据，我们引入了一种用于特定位点分子对接的新方法 HelixDock，该方法基于深度学习预训练网络和小数量的精确受体 - 配体复合物结构，相较于传统基于物理和基于深度学习的基线方法，在具有挑战性的测试集上表现出卓越的优势，研究揭示了预训练分子对接模型的扩展规律，展示了随着模型参数和预训练数据数量的增加而持续改进的效果，充分利用广泛而多样化的生成数据的力量有望推进以人工智能驱动的药物发现。