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• W4313339747 endingPage "108401" @default.
• W4313339747 startingPage "108401" @default.
• W4313339747 abstract "Recently, molecular representation and property exploration, with the combination of neural network, play a critical role in the field of drug design and discovery for assisting in drug related research. However, previous research in molecular representation relies heavily on artificial extraction of features based on biological experiments which may result in a manually introduced noise of molecular information with high cost in time and money. In this paper, a novel method named Substructural Hierarchical Attention Network (SuHAN) is proposed to discover inherent characteristics of molecules for representation learning. Specifically, SuHAN is composed of the cascaded layer: atom-level layer and substructure-level layer. Molecule in the SMILES format is divided into several substructural fragments by predefined partition rules, and then they are fed into atom-level layer and substructure-level layer successively to obtain feature representation from different perspective: atomic view and substructural view. In this way, the prominent structural features that may be omitted in global extraction are excavated from a fine-grained viewpoint and fused to reconstruct representative pattern in an overall view. Experiments on biophysics and physiology datasets demonstrate that our model is competitive with a significant improvement of both accuracy and stability in performance. We confirmed that the substructural segments and progressive hierarchical networks lead to an effective molecular representation for downstream tasks. These results provide a novel perspective about reconstructing overall pattern through local prominent structure." @default.
• W4313339747 created "2023-01-06" @default.
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• W4313339747 date "2023-03-01" @default.
• W4313339747 modified "2023-10-16" @default.
• W4313339747 title "SuHAN: Substructural hierarchical attention network for molecular representation" @default.
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• W4313339747 doi "https://doi.org/10.1016/j.jmgm.2022.108401" @default.
• W4313339747 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36584590" @default.
• W4313339747 hasPublicationYear "2023" @default.
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