Interpretation of Predictions in Drug-Gut Bacteria Interactions Using Machine Learning

Abstract

AbstractGut bacteria play a crucial role in host’s metabolism. Both antibiotic and non-antibiotic drugs affect the gut bacteria ecosystem, which negatively affects the host’s health. Also, gut bacteria metabolize drugs, making them ineffective to the target. The structure-activity relationship studies of drugs have the scope to make them more effective, efficient, and specific to the target. Previous machine learning studies use the available data to predict the activity of drugs and gut bacteria on each other, but these models lack interpretability. Herein, we study the drug-gut bacteria interaction using interpretable machine learning models. In this study, we identify the most important physicochemical features of the drug, which decide the drug-gut bacteria interactions with each other. One of the key findings of this work is that the higher-positive charged drug molecules can inhibit the growth of gut bacteria more. In contrast, the higher-negative charged drug molecules have higher possibility to get metabolized by gut bacteria.