Repository logo

Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure

Accepted version



Change log


Mason, DJ 
Stott, I 
Ashenden, Stephanie  ORCID logo
Weinstein, ZB 
Karakoc, I 


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.



antibiotics, drug interactions, drug combinations, Escherichia coli, drug resistance, infectious diseases, combination therapy, machine learning, drug structure

Journal Title

Journal of Medicinal Chemistry

Conference Name

Journal ISSN


Volume Title



American Chemical Society
BBSRC (1502974)
European Research Council (336159)
This work was supported by a grant from Unilever Research and Development to D.J.M., NIGMS Training Program in Biomolecular Pharmacology T32GM008541 to Z.B.W., Turkish Academy of Sciences GEBIP Programme and TUBITAK 115S934 Grant to M.C., and an ERC Starting Grant (MIXTURE) to A.B.