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：精准预测药物分子性质是人工智能药物发现（AIDD）领域的核心挑战，而有效的分子表征方法成为该研究的关键突破口。当前前沿研究多采用自监督学习（SSL）技术，从大规模无标注分子数据中提取结构性表征，再通过微调适配下游任务。然而现有研究普遍存在局限性：仅依赖单一分子信息模态（如分子图像或SMILES表征），忽视了多模态信息的互补潜力。为此，我们提出MolIG——一种基于图像与图结构预测分子性质的新型多模态分子预训练框架。该模型创新性地利用分子图与分子图像之间的连贯性与相关性执行自监督任务，有效融合两种分子表征形式的优势，既捕获关键分子结构特征，又提取高层语义信息。预训练完成后，采用图神经网络（GNN）编码器执行下游任务预测。与先进基线模型相比，MolIG在MoleculeNet基准组和ADMET基准组等分子性质预测相关下游任务中展现出更优性能。