MolSHAP: Interpreting Quantitative Structure–Activity Relationships Using Shapley Values of R‑Groups

Optimizing the activities and properties of lead compounds
is an
essential step in the drug discovery process. Despite recent advances
in machine learning-aided drug discovery, most of the existing methods
focus on making predictions for the desired objectives directly while
ignoring the explanations for predictions. Although several techniques
can provide interpretations for machine learning-based methods such
as feature attribution, there are still gaps between these interpretations
and the principles commonly adopted by medicinal chemists when designing
and optimizing molecules. Here, we propose an interpretation framework,
named MolSHAP, for quantitative structure–activity relationship
analysis by estimating the contributions of R-groups. Instead of attributing
the activities to individual input features, MolSHAP regards the R-group
fragments as the basic units of interpretation, which is in accordance
with the fragment-based modifications in molecule optimization. MolSHAP
is a model-agnostic method that can interpret activity regression
models with arbitrary input formats and model architectures. Based
on the evaluations of numerous representative activity regression
models on a specially designed R-group ranking task, MolSHAP achieved
significantly better interpretation power compared with other methods.
In addition, we developed a compound optimization algorithm based
on MolSHAP and illustrated the reliability of the optimized compounds
using an independent case study. These results demonstrated that MolSHAP
can provide a useful tool for accurately interpreting the quantitative
structure–activity relationships and rationally optimizing
the compound activities in drug discovery.