  It is well known that Drug Design is often a costly process both in terms of
time and economic effort. While good Quantitative Structure-Activity
Relationship models (QSAR) can help predicting molecular properties without the
need to synthesize them, it is still required to come up with new molecules to
be tested. This is mostly done in lack of tools to determine which
modifications are more promising or which aspects of a molecule are more
influential for the final activity/property. Here we present an automatic
process which involves Graph Convolutional Network models and input-attribution
methods to generate new molecules. We also explore the problems of
over-optimization and applicability, recognizing them as two important aspects
in the practical use of such automatic tools.
