Exploring the octanol–water partition coefficient dataset using deep learning techniques and data augmentation

Deep neural networks are potent tools for computational chemistry, but experimental feed data can limit their reach. Here the authors develop deep neural network data augmentation models to predict octanol–water partition coefficients (log P) of a variety of tautomers.

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Detalles Bibliográficos
Autores principales: Nadin Ulrich, Kai-Uwe Goss, Andrea Ebert
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/9d61fbcad06746a6a19e36cf2342853f
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Sumario:Deep neural networks are potent tools for computational chemistry, but experimental feed data can limit their reach. Here the authors develop deep neural network data augmentation models to predict octanol–water partition coefficients (log P) of a variety of tautomers.