Comparing and Validating Machine Learning Models for <i>Mycobacterium tuberculosis</i> Drug Discovery

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