Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure

Combination antibiotic
therapies are clinically important in the
fight against bacterial infections. However, the search space of drug
combinations is large, making the identification of effective combinations
a challenging task. Here, we present a computational framework that
uses substructure profiles derived from the molecular structures of
drugs and predicts antibiotic interactions. Using a previously published
data set of 153 drug pairs, we showed that substructure profiles are
useful in predicting synergy. We experimentally measured the interaction
of 123 new drug pairs, as a prospective validation set for our approach,
and identified 37 new synergistic pairs. Of the 12 pairs predicted
to be synergistic, 10 were experimentally validated, corresponding
to a 2.8-fold enrichment. Having thus validated our methodology, we
produced a compendium of interaction predictions for all pairwise
combinations among 100 antibiotics. Our methodology can make reliable
antibiotic interaction predictions for any antibiotic pair within
the applicability domain of the model since it solely requires chemical
structures as an input.