The quest for accurate prediction of drug molecule properties poses a fundamental challenge in the realm of Artificial Intelligence Drug Discovery (AIDD). An effective representation of drug molecules emerges as a pivotal component in this pursuit. Contemporary leading-edge research predominantly resorts to self-supervised learning (SSL) techniques to extract meaningful structural representations from large-scale, unlabeled molecular data, subsequently fine-tuning these representations for an array of downstream tasks. However, an inherent shortcoming of these studies lies in their singular reliance on one modality of molecular information, such as molecule image or SMILES representations, thus neglecting the potential complementarity of various molecular modalities. In response to this limitation, we propose MolIG, a novel MultiModaL molecular pre-training framework for predicting molecular properties based on Image and Graph structures. MolIG model innovatively leverages the coherence and correlation between molecule graph and molecule image to execute self-supervised tasks, effectively amalgamating the strengths of both molecular representation forms. This holistic approach allows for the capture of pivotal molecular structural characteristics and high-level semantic information. Upon completion of pre-training, Graph Neural Network (GNN) Encoder is used for the prediction of downstream tasks. In comparison to advanced baseline models, MolIG exhibits enhanced performance in downstream tasks pertaining to molecular property prediction within benchmark groups such as MoleculeNet Benchmark Group and ADMET Benchmark Group.

翻译：准确预测药物分子特性是人工智能药物发现领域的核心挑战。有效表征药物分子是实现该目标的关键要素。当前前沿研究主要借助自监督学习技术从大规模未标记分子数据中提取有意义的结构表征，再将其微调后应用于各类下游任务。然而，此类研究的固有缺陷在于单一依赖某一模态的分子信息（如分子图像或SMILES表征），忽视了不同分子模态间的潜在互补性。针对这一局限，我们提出MolIG——一种基于图像与图结构预测分子特性的新型多模态分子预训练框架。MolIG模型创新性地利用分子图与分子图像间的连贯性与相关性执行自监督任务，有效融合了两种分子表征形式的优势。这种整体方法可捕获关键分子结构特征与高层语义信息。预训练完成后，使用图神经网络编码器进行下游任务预测。与先进基线模型相比，MolIG在MoleculeNet基准组和ADMET基准组等分子属性预测下游任务中展现出更优性能。