  Lipophilicity is one of the factors determining the permeability of the cell
membrane to a drug molecule. Hence, accurate lipophilicity prediction is an
essential step in the development of new drugs. In this paper, we introduce a
novel approach to encoding additional graph information by extracting molecular
substructures. By adding a set of generalized atomic features of these
substructures to an established Direct Message Passing Neural Network (D-MPNN)
we were able to achieve a new state-of-the-art result at the task of prediction
of two main lipophilicity coefficients, namely logP and logD descriptors. We
further improve our approach by employing a multitask approach to predict logP
and logD values simultaneously. Additionally, we present a study of the model
performance on symmetric and asymmetric molecules, that may yield insight for
further research.
