论文标题
FP-GNN：一种用于增强分子属性预测的多功能深度学习体系结构
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction
论文作者
论文摘要
深度学习是分子设计的重要方法，并且具有相当大的预测分子特性的能力，包括物理化学，生物活性和ADME/T（吸收，分布，代谢，排泄和毒性）。在这项研究中，我们提出了一种新颖的深度学习结构，称为FP-GNN，该结构同时并从分子图和指纹中学到了信息。为了评估FP-GNN模型，我们对13个公共数据集，无偏见的PCBA数据集和14个表型筛选数据集进行了实验。广泛的评估结果表明，与先进的深度学习和常规机器学习算法相比，FP-GNN算法在这些数据集上实现了最先进的性能。此外，我们分析了不同分子指纹的影响，以及分子图和分子指纹对FP-GNN模型性能的影响。对反噪声能力和解释能力的分析还表明，在现实情况下，FP-GNN具有竞争力。
Deep learning is an important method for molecular design and exhibits considerable ability to predict molecular properties, including physicochemical, bioactive, and ADME/T (absorption, distribution, metabolism, excretion, and toxicity) properties. In this study, we advanced a novel deep learning architecture, termed FP-GNN, which combined and simultaneously learned information from molecular graphs and fingerprints. To evaluate the FP-GNN model, we conducted experiments on 13 public datasets, an unbiased LIT-PCBA dataset, and 14 phenotypic screening datasets for breast cell lines. Extensive evaluation results showed that compared to advanced deep learning and conventional machine learning algorithms, the FP-GNN algorithm achieved state-of-the-art performance on these datasets. In addition, we analyzed the influence of different molecular fingerprints, and the effects of molecular graphs and molecular fingerprints on the performance of the FP-GNN model. Analysis of the anti-noise ability and interpretation ability also indicated that FP-GNN was competitive in real-world situations.
