Benchmarking molecular feature attribution methods with activity cliffs

17 September 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Feature attribution techniques are popular choices within the explainable artificial intelligence toolbox, as they can help elucidate which parts of the provided inputs used by an underlying supervised-learning method are considered relevant for a specific prediction. In the context of molecular design, these approaches typically involve the coloring of molecular graphs, whose presentation to medicinal chemists can be useful for making a decision of which compounds to synthesize or prioritize. The consistency of the highlighted moieties alongside expert background knowledge is expected to contribute to the understanding of machine-learning models in drug design. Quantitative evaluation of such coloring approaches, however, has so far been limited to substructure identification tasks. We here present an approach that is based on maximum common substructure algorithms applied to experimentally-determined activity cliffs. Using the proposed benchmark, we found that molecule coloring approaches in conjunction with classical machine-learning models tend to outperform more modern, deep-learning-based alternatives. However, none of the tested feature attribution methods sufficiently and consistently generalized when confronted with unseen examples.


machine learning
explainable ai
medicinal chemistry
activity cliffs
feature attribution
graph neural networks

Supplementary materials

Supporting data
Influence of variables such as molecular similarity between training and benchmark sets, training set size, and out-of-fold performance on color agreement for all model combinations.

Supplementary weblinks


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