Interpretation of Structure–Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence

In silico models
based on Deep Neural Networks (DNNs) are promising
for predicting activities and properties of new molecules. Unfortunately,
their inherent black-box character hinders our understanding, as to
which structural features are important for activity. However, this
information is crucial for capturing the underlying structure–activity
relationships (SARs) to guide further optimization. To address this
interpretation gap, “Explainable Artificial Intelligence”
(XAI) methods recently became popular. Herein, we apply and compare
multiple XAI methods to projects of lead optimization data sets with
well-established SARs and available X-ray crystal structures. As we
can show, easily understandable and comprehensive interpretations
are obtained by combining DNN models with some powerful interpretation
methods. In particular, SHAP-based methods are promising for this
task. A novel visualization scheme using atom-based heatmaps provides
useful insights into the underlying SAR. It is important to note that
all interpretations are only meaningful in the context of the underlying
models and associated data.