Prediction and Interpretation Microglia Cytotoxicity by Machine Learning

Ameliorating microglia-mediated neuroinflammation is
a crucial
strategy in developing new drugs for neurodegenerative diseases. Plant
compounds are an important screening target for the discovery of drugs
for the treatment of neurodegenerative diseases. However, due to the
spatial complexity of phytochemicals, it becomes particularly important
to evaluate the effectiveness of compounds while avoiding the mixing
of cytotoxic substances in the early stages of compound screening.
Traditional high-throughput screening methods suffer from high cost
and low efficiency. A computational model based on machine learning
provides a novel avenue for cytotoxicity determination. In this study,
a microglia cytotoxicity classifier was developed using a machine
learning approach. First, we proposed a data splitting strategy based
on the molecule murcko generic scaffold, under this condition, three
machine learning approaches were coupled with three kinds of molecular
representation methods to construct microglia cytotoxicity classifier,
which were then compared and assessed by the predictive accuracy,
balanced accuracy, F1-score, and Matthews Correlation Coefficient.
Then, the recursive feature elimination integrated with support vector
machine (RFE-SVC) dimension reduction method was introduced to molecular
fingerprints with high dimensions to further improve the model performance.
Among all the microglial cytotoxicity classifiers, the SVM coupled
with ECFP4 fingerprint after feature selection (ECFP4-RFE-SVM) obtained
the most accurate classification for the test set (ACC of 0.99, BA
of 0.99, F1-score of 0.99, MCC of 0.97). Finally, the Shapley
additive explanations (SHAP) method was used in interpreting the microglia
cytotoxicity classifier and key substructure smart identified as structural
alerts. Experimental results show that ECFP4-RFE-SVM have reliable
classification capability for microglia cytotoxicity, and SHAP can
not only provide a rational explanation for microglia cytotoxicity
predictions, but also offer a guideline for subsequent molecular cytotoxicity
modifications.