A collaborative database and computational models for tuberculosis drug discovery

Abstract

The search for molecules with activity against Mycobacterium tuberculosis (Mtb) is employing many approaches in parallel including high throughput screening and computational methods. We have developed a database (CDD TB) to capture public and private Mtb data while enabling data mining and collaborations with other researchers. We have used the public data along with several cheminformatics approaches to produce models that describe active and inactive compounds. We have compared these datasets to those for known FDA approved drugs and between Mtb active and inactive compounds. The distribution of polar surface area and pK(a) of active compounds was found to be a statistically significant determinant of activity against Mtb. Hydrophobicity was not always statistically significant. Bayesian classification models for 220, 463 molecules were generated and tested with external molecules, and enabled the discrimination of active or inactive substructures from other datasets in the CDD TB. Computational pharmacophores based on known Mtb drugs were able to map to and retrieve a small subset of some of the Mtb datasets, including a high percentage of Mtb actives. The combination of the database, dataset analysis, Bayesian and pharmacophore models provides new insights into molecular properties and features that are determinants of activity in whole cells. This study provides novel insights into the key 1D molecular descriptors, 2D chemical substructures and 3D pharmacophores which can be used to mine the chemistry space, prioritizing those molecules with a higher probability of activity against Mtb.

Machine learning models identify molecules active against the Ebola virus in vitro

“…Specifically we have previously analyzed large datasets for Mycobacterium tuberculosis to build machine learning models that use single point data, dose-response data 43, 45 , combine bioactivity and cytotoxicity data (e.g. Vero, HepG2 or other model mammalian cells) 28, 29, 46 or combinations of these sets 47, 48 .…”

Validating New Tuberculosis Computational Models with Public Whole Cell Screening Aerobic Activity Datasets

“…In this study, we first used the Novartis aerobic assay hits as a test set for the previously described Bayesian models (10,20) to provide further validation of the approach using the published data from a different group. The mean maximal Tanimoto similarity was 0.67±0.13 for the dose response model and 0.48±0.12 for the single point model (using the MDL fingerprints).…”

“…Once all the samples had predictions, a ROC plot was generated, and the cross-validated ROC area under the curve (XV ROC AUC) was calculated (23). Other statistics were also generated as previously described elsewhere (20,23). The model was additionally evaluated by leaving out 50% of the data and rebuilding the model 100 times using a custom protocol in Discovery Studio (available from the author on request) for validation, in order to generate the ROC AUC (23).…”

“…The Novartis compounds and, in particular, those that were aerobic or anaerobic hits were compared to MLSMR and TAACF-NIAID-CB2 hits (10,20) using simple and readily interpretable calculated molecular properties including logP, number of hydrogen bond donor, number of hydrogen bond acceptor, Lipinski Rule of Five alerts, polar surface area, molecular weight, rotatable bonds, and atom counts, which were computed using the Marvin plug-in (ChemAxon, Budapest, Hungary) within the CDD database (10,20).…”

“…The development of the CDD TB database (Collaborative Drug Discovery Inc. Burlingame, CA) has been previously described (20). Screening datasets were collected and uploaded in CDD TB from sdf files and mapped to custom protocols (21).…”