Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Computer Science and Technology, East China Normal University, North Zhongshan Road, Shanghai, 200062, China.
BMC Bioinformatics. 2023 Aug 26;24(1):323. doi: 10.1186/s12859-023-05447-1.
Prediction of drug-target interaction (DTI) is an essential step for drug discovery and drug reposition. Traditional methods are mostly time-consuming and labor-intensive, and deep learning-based methods address these limitations and are applied to engineering. Most of the current deep learning methods employ representation learning of unimodal information such as SMILES sequences, molecular graphs, or molecular images of drugs. In addition, most methods focus on feature extraction from drug and target alone without fusion learning from drug-target interacting parties, which may lead to insufficient feature representation.
In order to capture more comprehensive drug features, we utilize both molecular image and chemical features of drugs. The image of the drug mainly has the structural information and spatial features of the drug, while the chemical information includes its functions and properties, which can complement each other, making drug representation more effective and complete. Meanwhile, to enhance the interactive feature learning of drug and target, we introduce a bidirectional multi-head attention mechanism to improve the performance of DTI.
To enhance feature learning between drugs and targets, we propose a novel model based on deep learning for DTI task called MCL-DTI which uses multimodal information of drug and learn the representation of drug-target interaction for drug-target prediction. In order to further explore a more comprehensive representation of drug features, this paper first exploits two multimodal information of drugs, molecular image and chemical text, to represent the drug. We also introduce to use bi-rectional multi-head corss attention (MCA) method to learn the interrelationships between drugs and targets. Thus, we build two decoders, which include an multi-head self attention (MSA) block and an MCA block, for cross-information learning. We use a decoder for the drug and target separately to obtain the interaction feature maps. Finally, we feed these feature maps generated by decoders into a fusion block for feature extraction and output the prediction results.
MCL-DTI achieves the best results in all the three datasets: Human, C. elegans and Davis, including the balanced datasets and an unbalanced dataset. The results on the drug-drug interaction (DDI) task show that MCL-DTI has a strong generalization capability and can be easily applied to other tasks.
药物-靶点相互作用（DTI）的预测是药物发现和药物重定位的重要步骤。传统方法大多耗时耗力，而基于深度学习的方法解决了这些限制，并应用于工程领域。目前大多数深度学习方法都采用 SMILES 序列、分子图或药物分子图像等单模态信息的表示学习。此外，大多数方法仅侧重于从药物和靶点单独提取特征，而没有从药物-靶点相互作用的各方进行融合学习，这可能导致特征表示不足。
为了捕获更全面的药物特征，我们同时利用药物的分子图像和化学特征。药物的图像主要包含药物的结构信息和空间特征，而化学信息包括其功能和性质，可以相互补充，使药物表示更加有效和完整。同时，为了增强药物和靶点的交互特征学习，我们引入了一种双向多头注意力机制，以提高 DTI 的性能。
为了增强药物和靶点之间的特征学习，我们提出了一种基于深度学习的用于 DTI 任务的新型模型，称为 MCL-DTI，该模型使用药物的多模态信息，并学习药物-靶点相互作用的表示，以进行药物-靶点预测。为了进一步探索更全面的药物特征表示，本文首先利用药物的两种多模态信息，即分子图像和化学文本，来表示药物。我们还引入了双向多头交叉注意力（MCA）方法来学习药物和靶点之间的相互关系。因此，我们构建了两个解码器，其中包括多头自注意力（MSA）块和 MCA 块，用于交叉信息学习。我们分别使用一个解码器用于药物和靶点，以获得交互特征图。最后，我们将这些解码器生成的特征图输入到融合块中进行特征提取，并输出预测结果。
MCL-DTI 在三个数据集（人类、秀丽隐杆线虫和戴维斯）中的所有任务中都取得了最佳结果，包括平衡数据集和不平衡数据集。在药物-药物相互作用（DDI）任务上的结果表明，MCL-DTI 具有很强的泛化能力，并且可以很容易地应用于其他任务。
