College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China.
Department of AIDD, Shanghai Yuyao Biotechnology Co., Ltd., Shanghai 201109, China.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae394.
The molecular property prediction (MPP) plays a crucial role in the drug discovery process, providing valuable insights for molecule evaluation and screening. Although deep learning has achieved numerous advances in this area, its success often depends on the availability of substantial labeled data. The few-shot MPP is a more challenging scenario, which aims to identify unseen property with only few available molecules. In this paper, we propose an attribute-guided prototype network (APN) to address the challenge. APN first introduces an molecular attribute extractor, which can not only extract three different types of fingerprint attributes (single fingerprint attributes, dual fingerprint attributes, triplet fingerprint attributes) by considering seven circular-based, five path-based, and two substructure-based fingerprints, but also automatically extract deep attributes from self-supervised learning methods. Furthermore, APN designs the Attribute-Guided Dual-channel Attention module to learn the relationship between the molecular graphs and attributes and refine the local and global representation of the molecules. Compared with existing works, APN leverages high-level human-defined attributes and helps the model to explicitly generalize knowledge in molecular graphs. Experiments on benchmark datasets show that APN can achieve state-of-the-art performance in most cases and demonstrate that the attributes are effective for improving few-shot MPP performance. In addition, the strong generalization ability of APN is verified by conducting experiments on data from different domains.
分子性质预测（MPP）在药物发现过程中起着至关重要的作用，为分子评估和筛选提供了有价值的见解。尽管深度学习在这一领域取得了许多进展，但它的成功往往取决于大量可用的标记数据。少样本 MPP 是一个更具挑战性的场景，旨在仅使用少量可用分子来识别未见的性质。在本文中，我们提出了一种属性引导原型网络（APN）来应对这一挑战。APN 首先引入了一种分子属性提取器，它不仅可以通过考虑七种基于环的、五种基于路径的和两种基于子结构的指纹来提取三种不同类型的指纹属性（单指纹属性、双指纹属性、三指纹属性），还可以通过自监督学习方法自动提取深度属性。此外，APN 设计了属性引导双通道注意模块，以学习分子图和属性之间的关系，并细化分子的局部和全局表示。与现有工作相比，APN 利用了高层人为定义的属性，并帮助模型明确地概括分子图中的知识。在基准数据集上的实验表明，APN 在大多数情况下都能达到最先进的性能，并证明属性对于提高少样本 MPP 性能是有效的。此外，通过在来自不同领域的数据上进行实验，验证了 APN 的强大泛化能力。
