标题： MolGraph-xLSTM：一种基于图的双层xLSTM框架，结合多头专家混合以增强分子表示和可解释性 显示英文标题

标题： MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability

摘要： 预测分子性质对于药物发现至关重要，计算方法可以大大增强这一过程。分子图已成为表示学习的焦点，图神经网络（GNNs）被广泛使用。然而，GNNs常常难以捕捉长距离依赖关系。为了解决这个问题，我们提出了MolGraph-xLSTM，一种新颖的基于图的xLSTM模型，该模型增强了特征提取并有效建模分子的长距离相互作用。我们的方法在两个尺度上处理分子图：原子级和基序级。对于原子级图，一个基于GNN的xLSTM框架结合跳跃知识提取局部特征，并聚合多层信息以有效捕捉局部和全局模式。基序级图提供了互补的结构信息，以获得更广泛的分子视图。两个尺度的嵌入通过多头专家混合（MHMoE）进行优化，进一步增强了表达能力和性能。我们在10个分子性质预测数据集上验证了MolGraph-xLSTM，涵盖了分类和回归任务。我们的模型在所有数据集上表现出一致的性能，与基线相比，在BBBP数据集上的分类任务中提高了7.03%，在ESOL数据集上的回归任务中提高了7.54%。平均而言，与基线方法相比，MolGraph-xLSTM在分类任务中的AUROC提高了3.18%，在回归数据集上的RMSE降低了3.83%。这些结果证实了我们模型的有效性，为药物发现中的分子表示学习提供了一个有前景的解决方案。 显示英文摘要

摘要： Predicting molecular properties is essential for drug discovery, and computational methods can greatly enhance this process. Molecular graphs have become a focus for representation learning, with Graph Neural Networks (GNNs) widely used. However, GNNs often struggle with capturing long-range dependencies. To address this, we propose MolGraph-xLSTM, a novel graph-based xLSTM model that enhances feature extraction and effectively models molecule long-range interactions. Our approach processes molecular graphs at two scales: atom-level and motif-level. For atom-level graphs, a GNN-based xLSTM framework with jumping knowledge extracts local features and aggregates multilayer information to capture both local and global patterns effectively. Motif-level graphs provide complementary structural information for a broader molecular view. Embeddings from both scales are refined via a multi-head mixture of experts (MHMoE), further enhancing expressiveness and performance. We validate MolGraph-xLSTM on 10 molecular property prediction datasets, covering both classification and regression tasks. Our model demonstrates consistent performance across all datasets, with improvements of up to 7.03% on the BBBP dataset for classification and 7.54% on the ESOL dataset for regression compared to baselines. On average, MolGraph-xLSTM achieves an AUROC improvement of 3.18\% for classification tasks and an RMSE reduction of 3.83\% across regression datasets compared to the baseline methods. These results confirm the effectiveness of our model, offering a promising solution for molecular representation learning for drug discovery.