Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence

Liver microsomal stability, a crucial aspect of metabolic
stability,
significantly impacts practical drug discovery. However, current models
for predicting liver microsomal stability are based on limited molecular
information from a single species. To address this limitation, we
constructed the largest public database of compounds from three common
species: human, rat, and mouse. Subsequently, we developed a series
of classification models using both traditional descriptor-based and
classic graph-based machine learning (ML) algorithms. Remarkably,
the best-performing models for the three species achieved Matthews
correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively,
on the test set. Furthermore, through the construction of consensus
models based on these individual models, we have demonstrated their
superior predictive performance in comparison with the existing models
of the same type. To explore the similarities and differences in the
properties of liver microsomal stability among multispecies molecules,
we conducted preliminary interpretative explorations using the Shapley
additive explanations (SHAP) and atom heatmap approaches for the models
and misclassified molecules. Additionally, we further investigated
representative structural modifications and substructures that decrease
the liver microsomal stability in different species using the matched
molecule pair analysis (MMPA) method and substructure extraction techniques.
The established prediction models, along with insightful interpretation
information regarding liver microsomal stability, will significantly
contribute to enhancing the efficiency of exploring practical drugs
for development.