  The use of machine learning in chemistry has become a common practice. At the
same time, despite the success of modern machine learning methods, the lack of
data limits their use. Using a transfer learning methodology can help solve
this problem. This methodology assumes that a model built on a sufficient
amount of data captures general features of the chemical compound structure on
which it was trained and that the further reuse of these features on a dataset
with a lack of data will greatly improve the quality of the new model. In this
paper, we develop this approach for small organic molecules, implementing
transfer learning with graph convolutional neural networks. The paper shows a
significant improvement in the performance of models for target properties with
a lack of data. The effects of the dataset composition on model quality and the
applicability domain of the resulting models are also considered.
