  To be able to predict a molecular graph structure ($W$) given a 2D image of a
chemical compound ($U$) is a challenging problem in machine learning. We are
interested to learn $f: U \rightarrow W$ where we have a fully mediating
representation $V$ such that $f$ factors into $U \rightarrow V \rightarrow W$.
However, observing V requires detailed and expensive labels. We propose graph
aligning approach that generates rich or detailed labels given normal labels
$W$. In this paper we investigate the scenario of domain adaptation from the
source domain where we have access to the expensive labels $V$ to the target
domain where only normal labels W are available. Focusing on the problem of
predicting chemical compound graphs from 2D images the fully mediating layer is
represented using the planar embedding of the chemical graph structure we are
predicting. The use of a fully mediating layer implies some assumptions on the
mechanism of the underlying process. However if the assumptions are correct it
should allow the machine learning model to be more interpretable, generalize
better and be more data efficient at training time. The empirical results show
that, using only 4000 data points, we obtain up to 4x improvement of
performance after domain adaptation to target domain compared to pretrained
model only on the source domain. After domain adaptation, the model is even
able to detect atom types that were never seen in the original source domain.
Finally, on the Maybridge data set the proposed self-labeling approach reached
higher performance than the current state of the art.
