Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

Graph neural networks are able to
solve certain drug discovery
tasks such as molecular property prediction and de novo molecule generation. However, these models are considered “black-box”
and “hard-to-debug”. This study aimed to improve modeling
transparency for rational molecular design by applying the integrated
gradients explainable artificial intelligence (XAI) approach for graph
neural network models. Models were trained for predicting plasma protein
binding, hERG channel inhibition, passive permeability, and cytochrome
P450 inhibition. The proposed methodology highlighted molecular features
and structural elements that are in agreement with known pharmacophore
motifs, correctly identified property cliffs, and provided insights
into unspecific ligand–target interactions. The developed XAI
approach is fully open-sourced and can be used by practitioners to
train new models on other clinically relevant endpoints.