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.

本文旨在揭示预测分子性能和生物活性的神经网络是如何生成并理解隐藏于模型中的学习表示的。我们提出了一种解释神经元如何被用作分类器的方法，用以确定具有药效或毒性结构的成分，从而为化学、药理学和生物化学领域提供新的视角与重要的知识。此外，我们还提出了一种从模型中提取新型药效或毒性结构的方法，并证明这些结构与文献中的发现是一致的。通过这些可解释的知识，我们能更好地开发和设计新的药物分子，同时理解当前方法的失败和成功。