Recent advances in Molecular Graph-Language Models (MGLMs) have demonstrated promising capabilities in molecular understanding tasks. However, existing approaches face critical limitations: (1) shallow alignment methods which employ identical processing modules for both modalities, resulting in compromised expressiveness and catastrophic forgetting of pre-trained language capabilities; and (2) over-reliance on high-level molecular representations that inadequately capture fine-grained structural information essential for comprehensive molecular understanding. To address these challenges, we present DeepMolTex, a novel framework for Deep fusion of Molecular structure and Textual representations across multiple scales. Our approach introduces a Mixture of Modality Experts (MoME) architecture that facilitates deep alignment between molecular graph features and large language models while preserving language capabilities, and a multi-scale graph projector that extracts and aligns molecular features at atom, motif, and molecule levels. Experimental results demonstrate that DeepMolTex significantly outperforms existing methods on fundamental molecular understanding tasks, including molecule description generation and IUPAC name prediction, while effectively preserving the language capabilities of the pre-trained LLM.

In graph classification, the out-of-distribution (OOD) issue is attracting great attention. To address this issue, a prevailing idea is to learn stable features, on the assumption that they are substructures causally determining the label and that their relationship with the label is stable to the distributional uncertainty. In contrast, the complementary parts termed environmental features, fail to determine the label solely and hold varying relationships with the label, thus ascribed to the possible reason for the distribution shift. Existing generalization efforts mainly encourage the model’s insensitivity to environmental features. While the sensitivity to stable features is promising to distinguish the crucial clues from the distributional uncertainty but largely unexplored. A paradigm of simultaneously exploring the sensitivity to stable features and insensitivity to environmental features is until-now lacking to achieve the generalizable graph classification, to the best of our knowledge. In this work, we conjecture that generalizable models should be sensitive to stable features and insensitive to environmental features. To this end, we propose a simple yet effective augmentation strategy for graph classification: Equivariant and Invariant Cross-Data Augmentation (EI-CDA). By employing equivariance, given a pair of input graphs, we first estimate their stable and environmental features via masks. Then, we linearly mix the estimated stable features of two graphs and encourage the model predictions faithfully reflect their mixed semantics. Meanwhile, by using invariance, we swap the estimated environmental features of two graphs and keep the predictions invariant. This simple yet effective strategy endows the models with both sensitivity to stable features and insensitivity to environmental features. Extensive experiments show that EI-CDA significantly improves performance and outperforms leading baselines. Our codes are available at: https://github.com/yongduosui/EI-GNN .

Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments.In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels.Each channel encodes KGs via different relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive entities respectively, which are further combined via pooling techniques.Moreover, we also infer and transfer rule knowledge for completing two KGs consistently.MuGNN is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments.Extensive experiments on five publicly available datasets demonstrate our superior performance (5% Hits@1 up on average).