  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.
