  New antibiotics are needed to battle growing antibiotic resistance, but the
development process from hit, to lead, and ultimately to a useful drug, takes
decades. Although progress in molecular property prediction using
machine-learning methods has opened up new pathways for aiding the antibiotics
development process, many existing solutions rely on large datasets and finding
structural similarities to existing antibiotics. Challenges remain in modelling
of unconventional antibiotics classes that are drawing increasing research
attention. In response, we developed an antimicrobial activity prediction model
for conjugated oligoelectrolyte molecules, a new class of antibiotics that
lacks extensive prior structure-activity relationship studies. Our approach
enables us to predict minimum inhibitory concentration for E. coli K12, with 21
molecular descriptors selected by recursive elimination from a set of 5,305
descriptors. This predictive model achieves an R2 of 0.65 with no prior
knowledge of the underlying mechanism. We find the molecular representation
optimum for the domain is the key to good predictions of antimicrobial
activity. In the case of conjugated oligoelectrolytes, a representation
reflecting the 3-dimensional shape of the molecules is most critical. Although
it is demonstrated with a specific example of conjugated oligoelectrolytes, our
proposed approach for creating the predictive model can be readily adapted to
other novel antibiotic candidate domains.
