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• W4280602833 abstract "Abstract 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." @default.
• W4280602833 created "2022-05-22" @default.
• W4280602833 creator A5023686923 @default.
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• W4280602833 date "2022-09-17" @default.
• W4280602833 modified "2023-10-18" @default.
• W4280602833 title "FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction" @default.
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• W4280602833 doi "https://doi.org/10.1093/bib/bbac408" @default.
• W4280602833 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36124766" @default.
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