Neglected diseases are
infections that thrive mainly among underdeveloped countries, particularly those belonging to regions found in Asia, Africa, and America. One of the most complex diseases is
noma, a dangerous health condition characterized by a polymicrobial and opportunistic nature. The search for potent and safer
antibacterial agents against this disease is therefore a goal of particular interest. Chemoinformatics can be used to rationalize the discovery of
drug candidates, diminishing time and financial resources. However, in the case of
noma, there is no in silico model available for its use in the discovery of efficacious
antibacterial agents. This work is devoted to report the first mtk-QSBER model, which integrates dissimilar kinds of chemical and
biological data. The model was generated with the aim of simultaneously predicting activity against bacteria present in
noma, and ADMET (absorption, distribution, metabolism, elimination, toxicity) parameters. The mtk-QSBER model was constructed by employing a large and heterogeneous dataset of chemicals and displayed accuracies higher than 90% in both training and prediction sets. We confirmed the practical applicability of the model by predicting multiple profiles of the investigational antibacterial
drug delafloxacin, and the predictions converged with the experimental reports. To date, this is the first model focused on the virtual search for desirable anti-
noma agents.