论文标题
GNN-PT：通过整合蛋白质变压器来增强化合物蛋白质相互作用的预测
GNN-PT: Enhanced Prediction of Compound-protein Interactions by Integrating Protein Transformer
论文作者
论文摘要
蛋白质相互作用（CPI）的预测对于药物发现中的硅内筛查步骤至关重要。最近，与传统的机器学习算法相比，使用深神经网络的许多端到端表示学习方法的性能明显好得多。大量精力集中在化合物表示或从化合物蛋白相互作用中提取的信息提取，以通过利用神经注意机制来提高模型能力。然而，以前的研究几乎不关注代表蛋白质序列，其中残基对的远距离相互作用对于表征蛋白质折叠引起的结构特性至关重要。我们将自我注意事项机制纳入用于CPI建模的蛋白质表示模块中，该模块旨在捕获蛋白质中的远程相互作用信息。与现有的CPI模型相比，与现有CPI模型集成有关蛋白质变压器的拟议模块，称为蛋白质变压器，与现有CPI模型进行了集成，对预测性能有了显着改善。
The prediction of protein interactions (CPIs) is crucial for the in-silico screening step in drug discovery. Recently, many end-to-end representation learning methods using deep neural networks have achieved significantly better performance than traditional machine learning algorithms. Much effort has focused on the compound representation or the information extraction from the compound-protein interaction to improve the model capability by taking the advantage of the neural attention mechanism. However, previous studies have paid little attention to representing the protein sequences, in which the long-range interactions of residue pairs are essential for characterizing the structural properties arising from the protein folding. We incorporate the self-attention mechanism into the protein representation module for CPI modeling, which aims at capturing the long-range interaction information within proteins. The proposed module concerning protein representation, called Protein Transformer, with an integration with an existing CPI model, has shown a significant improvement in the prediction performance when compared with several existing CPI models.
