  In this work, we present a new method for predicting bioaccumulation factor
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 (3D-RISM). We have shown
that the method allows one to achieve a good accuracy of prediction. 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 physics-based models.
