Tuberculosis is a
global health dilemma. In 2016, the WHO reported 10.4 million incidences
and 1.7 million deaths. The need to develop new treatments for those
infected with <i>Mycobacterium tuberculosis</i> (<i>Mtb</i>) has led to many large-scale phenotypic screens and
many thousands of new active compounds identified <i>in vitro</i>. However, with limited funding, efforts to discover new active molecules
against <i>Mtb</i> needs to be more efficient. Several computational
machine learning approaches have been shown to have good enrichment
and hit rates. We have curated small molecule <i>Mtb</i> data and developed new models with a total of 18,886 molecules with
activity cutoffs of 10 μM, 1 μM, and 100 nM. These data
sets were used to evaluate different machine learning methods (including
deep learning) and metrics and to generate predictions for additional
molecules published in 2017. One <i>Mtb</i> model, a combined <i>in vitro</i> and <i>in vivo</i> data Bayesian model
at a 100 nM activity yielded the following metrics for 5-fold cross
validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity
= 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation
set (<i>n</i> = 153 compounds) published in 2017, and when
used to test our model, it showed the comparable statistics (accuracy
= 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa
= 0.36, and MCC = 0.47). We have also compared these models with additional
machine learning algorithms showing Bayesian machine learning models
constructed with literature <i>Mtb</i> data generated by
different laboratories generally were equivalent to or outperformed
deep neural networks with external test sets. Finally, we have also
compared our training and test sets to show they were suitably diverse
and different in order to represent useful evaluation sets. Such <i>Mtb</i> machine learning models could help prioritize compounds
for testing <i>in vitro</i> and <i>in vivo</i>
