Geometric deep learning for molecular property predictions with chemical accuracy across chemical space

Abstract Chemical engineers heavily rely on precise knowledge of physicochemical properties to model chemical processes. Despite the growing popularity of deep learning, it is only rarely applied for property prediction due to data scarcity and limited accuracy for compounds in industrially-relevant...

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Principais autores:	Maarten R. Dobbelaere, István Lengyel, Christian V. Stevens, Kevin M. Van Geem
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	BMC 2024-08-01
סדרה:	Journal of Cheminformatics
נושאים:	Artificial intelligence Deep learning Thermochemistry Liquid-phase thermodynamics Representation learning
גישה מקוונת:	https://doi.org/10.1186/s13321-024-00895-0
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