FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction

• Published in: Briefings in bioinformatics Vol. 23; no. 6
• Main Authors: Cai, Hanxuan, Zhang, Huimin, Zhao, Duancheng, Wu, Jingxing, Wang, Ling
• Format: Journal Article
• Language: English
• Published: England 19.11.2022
• Subjects: Algorithms; Deep Learning; Drug Discovery - methods; Machine Learning; Neural Networks, Computer; graph attention networks; molecular representation; machine learning; artificial intelligence; drug design and discovery
• DOI: 10.1093/bib/bbac408
• ISSN: 1467-5463; 1477-4054

Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especially for drug design and discovery. In this study, we advanced a novel deep learning architecture, termed FP-GNN (fingerprints and graph neural networks), which combined and simultaneously learned information from molecular graphs and fingerprints for molecular property prediction. 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. Collectively, FP-GNN algorithm can assist chemists, biologists and pharmacists in predicting and discovering better molecules with desired functions or properties.