PDF(Pubmed)
Abstract：
BACKGROUND: The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module.
RESULTS: In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo\'s dataset, Davis, and KIBA datasets.
CONCLUSIONS: Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches.
RESULTS: In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo\'s dataset, Davis, and KIBA datasets.
CONCLUSIONS: Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches.
摘要：
背景：药物-靶标相互作用（DTI）预测使用药物分子和蛋白质序列作为输入来预测结合亲和力值。近年来,基于深度学习的模型得到了更多的关注。这些方法有两个模块:特征提取模块和任务预测模块。在大多数基于深度学习的方法中，简单的任务预测损失(即，分类任务的分类交叉熵和回归任务的均方误差)用于学习模型。在机器学习中,开发了基于对比的损失函数来学习更多的判别特征空间。在基于深度学习的模型中，提取更具辨别力的特征空间可以提高任务预测模块的性能。
结果：在本文中，我们使用多模态知识作为输入，并提出了一种基于注意力的融合技术来结合这些知识。此外，我们研究了如何在任务预测损失中利用对比损失函数可以帮助该方法学习更强大的模型。考虑了四个对比损失函数：(1)最大边际对比损失函数，(2)三元组损失函数，（3）多类N对损失目标，和(4)NT-Xent损失函数。所提出的模型使用四个著名的数据集进行评估：Wang等人。数据集,罗的数据集，戴维斯,和KIBA数据集。
结论：因此，在回顾了最先进的方法之后，我们通过结合蛋白质序列和药物分子开发了多模态特征提取网络，以及蛋白质-蛋白质相互作用网络和药物-药物相互作用网络。结果表明，它的性能明显优于可比的最新方法。
结果：在本文中，我们使用多模态知识作为输入，并提出了一种基于注意力的融合技术来结合这些知识。此外，我们研究了如何在任务预测损失中利用对比损失函数可以帮助该方法学习更强大的模型。考虑了四个对比损失函数：(1)最大边际对比损失函数，(2)三元组损失函数，（3）多类N对损失目标，和(4)NT-Xent损失函数。所提出的模型使用四个著名的数据集进行评估：Wang等人。数据集,罗的数据集，戴维斯,和KIBA数据集。
结论：因此，在回顾了最先进的方法之后，我们通过结合蛋白质序列和药物分子开发了多模态特征提取网络，以及蛋白质-蛋白质相互作用网络和药物-药物相互作用网络。结果表明，它的性能明显优于可比的最新方法。
