College of Culture and Creativity, Weifang Vocational College, Weifang, China.
Department of Statistics, University of Minnesota, Minneapolis, MN, USA.
J Biomed Inform. 2023 Sep;145:104447. doi: 10.1016/j.jbi.2023.104447. Epub 2023 Jul 21.
Molecular property prediction based on artificial intelligence technology has significant prospects in speeding up drug discovery and reducing drug discovery costs. Among them, molecular property prediction based on graph neural networks (GNNs) has received extensive attention in recent years. However, the existing graph neural networks still face the following challenges in node representation learning. First, the number of nodes increases exponentially with the expansion of the perception field, which limits the exploration ability of the model in the depth direction. Secondly, the large number of nodes in the perception field brings noise, which is not conducive to the model's representation learning of the key structures. Therefore, a graph neural network model based on structure generation is proposed in this paper. The model adopts the depth-first strategy to generate the key structures of the graph, to solve the problem of insufficient exploration ability of the graph neural network in the depth direction. A tendentious node selection method is designed to gradually select nodes and edges to generate the key structures of the graph, to solve the noise problem caused by the excessive number of nodes. In addition, the model skillfully realizes forward propagation and iterative optimization of structure generation by using an attention mechanism and random bias. Experimental results on public data sets show that the proposed model achieves better classification results than the existing best models.
基于人工智能技术的分子性质预测在加速药物发现和降低药物发现成本方面具有广阔的前景。其中，基于图神经网络（GNN）的分子性质预测近年来受到了广泛关注。然而，现有的图神经网络在节点表示学习方面仍然面临以下挑战。首先，随着感知域的扩展，节点数量呈指数级增长，限制了模型在深度方向上的探索能力。其次，感知域中的大量节点带来了噪声，不利于模型对关键结构的表示学习。因此，本文提出了一种基于结构生成的图神经网络模型。该模型采用深度优先策略生成图的关键结构，解决了图神经网络在深度方向上探索能力不足的问题。设计了一种有偏节点选择方法，逐步选择节点和边生成图的关键结构，解决了节点数量过多带来的噪声问题。此外，该模型通过注意力机制和随机偏差巧妙地实现了结构生成的前向传播和迭代优化。在公共数据集上的实验结果表明，所提出的模型比现有的最佳模型取得了更好的分类结果。
