Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation

Abstract

The search for new tuberculosis treatments continues as we need to find molecules that can act more quickly, be accommodated in multidrug regimens, and overcome ever increasing levels of drug resistance. Multiple large scale phenotypic high-throughput screens against Mycobacterium tuberculosis (Mtb) have generated dose response data, enabling the generation of machine learning models. These models also incorporated cytotoxicity data and were recently validated with a large external data set. A cheminformatics data-fusion approach followed by Bayesian machine learning, Support Vector Machine, or Recursive Partitioning model development (based on publicly available Mtb screening data) was used to compare individual data sets and subsequent combined models. A set of 1924 commercially available molecules with promising antitubercular activity (and lack of relative cytotoxicity to Vero cells) were used to evaluate the predictive nature of the models. We demonstrate that combining three data sets incorporating antitubercular and cytotoxicity data in Vero cells from our previous screens results in external validation receiver operator curve (ROC) of 0.83 (Bayesian or RP Forest). Models that do not have the highest 5-fold cross-validation ROC scores can outperform other models in a test set dependent manner. We demonstrate with predictions for a recently published set of Mtb leads from GlaxoSmithKline that no single machine learning model may be enough to identify compounds of interest. Data set fusion represents a further useful strategy for machine learning construction as illustrated with Mtb. Coverage of chemistry and Mtb target spaces may also be limiting factors for the whole-cell screening data generated to date.

Figure 1

A. Principal Component Analysis of all Mtb datasets (7728 active and inactive compounds) used in this study and overlap of 177 GSK published leads. 3 principal components explain 73% of the variance. B inset to show some of the GSK leads (yellow) widely dispersed and within the chemistry space of the Mtb datasets used for modeling.

Figure 2

Clustering and PCA of TB Mobile data. A. Examination of 745 TB Mobile molecules with interpretable descriptors results in a PCA with 3 PCs, which explain 88% variability. Outlier compounds represent macrocycles (bottom right) and long lipid-like molecules (bottom left). B. 1429 SRI hits from four datasets (active and non-toxic only, from the SRI screens where: IC₉₀ < 10 µg/ml or 10 µM and a selectivity index (SI) greater than ten where the SI is calculated from SI = CC₅₀/IC₉₀) and 745 TB Mobile compounds results in a PCA with 3 PCs explaining 83% variability; SRI compounds are clustered (yellow). C. Examination of 177 GSK leads (yellow) and the TB Mobile compounds results in a PCA with 3 PCs, which explain 88 % of variance.

Figure 2

Clustering and PCA of TB Mobile data. A. Examination of 745 TB Mobile molecules with interpretable descriptors results in a PCA with 3 PCs, which explain 88% variability. Outlier compounds represent macrocycles (bottom right) and long lipid-like molecules (bottom left). B. 1429 SRI hits from four datasets (active and non-toxic only, from the SRI screens where: IC₉₀ < 10 µg/ml or 10 µM and a selectivity index (SI) greater than ten where the SI is calculated from SI = CC₅₀/IC₉₀) and 745 TB Mobile compounds results in a PCA with 3 PCs explaining 83% variability; SRI compounds are clustered (yellow). C. Examination of 177 GSK leads (yellow) and the TB Mobile compounds results in a PCA with 3 PCs, which explain 88 % of variance.