In the era of antibiotic resistance

in silico

prediction of bacterial resistome profiles

likely to be associated with inactivation of new potential antibiotics is of utmost importance. Despite this

to the best of our knowledge

no tool exists for such prediction. Therefore

under the rationale that drugs with similar structures have similar resistome profiles

we developed two models

a deterministic model and a stochastic model

to predict the bacterial resistome likely to neutralize uncharacterized but potential chemical structures. The current version of the tool involves the prediction of a resistome for

Escherichia coli

and

Pseudomonas aeruginosa.

The deterministic model on omitting two diverse but relatively less characterized drug classes

polyketides and polypeptides showed an accuracy of 87%

a sensitivity of 85%

and a precision of 89%

whereas the stochastic model predicted antibiotic classes of the test set compounds with an accuracy of 72%

a sensitivity of 75%

and a precision of 83%. The models have been implemented in both a standalone package and an online server

uCAREChemSuiteCLI and uCARE Chem Suite

respectively. In addition to resistome prediction

the online version of the suite enables the user to visualize the chemical structure

classify compounds in 19 predefined drug classes

perform pairwise alignment

and cluster with database compounds using a graphical user interface.