  Without any means of interpretation, neural networks that predict molecular
properties and bioactivities are merely black boxes. We will unravel these
black boxes and will demonstrate approaches to understand the learned
representations which are hidden inside these models. We show how single
neurons can be interpreted as classifiers which determine the presence or
absence of pharmacophore- or toxicophore-like structures, thereby generating
new insights and relevant knowledge for chemistry, pharmacology and
biochemistry. We further discuss how these novel pharmacophores/toxicophores
can be determined from the network by identifying the most relevant components
of a compound for the prediction of the network. Additionally, we propose a
method which can be used to extract new pharmacophores from a model and will
show that these extracted structures are consistent with literature findings.
We envision that having access to such interpretable knowledge is a crucial aid
in the development and design of new pharmaceutically active molecules, and
helps to investigate and understand failures and successes of current methods.
