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• W2811492042 startingPage "32LT03" @default.
• W2811492042 abstract "In this work, we present a new method for predicting complex physical-chemical properties of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input. These spatial distributions are obtained by a molecular theory called three-dimensional reference interaction site model. We have shown that the method allows one to achieve a good accuracy of prediction of bioconcentration factor which is difficult to predict by direct application of methods of molecular theory or simulations. Our research demonstrates that combination of molecular theories with modern machine learning approaches can be effectively used for predicting properties that are otherwise inaccessible to purely theory-based models." @default.
• W2811492042 created "2018-07-10" @default.
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• W2811492042 date "2018-07-19" @default.
• W2811492042 modified "2023-09-25" @default.
• W2811492042 title "3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction" @default.
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• W2811492042 doi "https://doi.org/10.1088/1361-648x/aad076" @default.
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• W2811492042 hasPublicationYear "2018" @default.
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