Enhancing Hit Identification in Mycobacterium tuberculosis Drug Discovery Using Validated Dual-Event Bayesian Models

Ekins, Sean; Reynolds, Robert C.; Franzblau, Scott G.; Wan, Baojie; Freundlich, Joel S.; Bunin, Barry A.

doi:10.1371/journal.pone.0063240

Cited by 52 publications

(112 citation statements)

References 65 publications

Supporting

Mentioning

107

Contrasting

Order By: Relevance

“…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 . These models in turn have been validated with additional non-overlapping datasets, demonstrating that it is possible to use publically accessible data to find novel in vitro active antituberculars.…”

Section: Discussionmentioning

confidence: 99%

“…RP Single Trees had a minimum of ten samples per node and a maximum tree depth of 20. In all cases, 5-fold cross validation or leave out 50% × 100 fold cross validation was used to calculate the Receiver Operator Curve (ROC) for the models generated 28, 29 .…”

Section: Methodsmentioning

confidence: 99%

“…We proposed that if we could learn from the many compounds already screened for anti-EBOV activity, we could more efficiently find additional compounds and perhaps understand the key molecular features needed for antiviral activity 14 . We speculated then that Laplacian-corrected Naïve Bayesian classifier models might be useful as they have been for M. tuberculosis 28, 29 and more recently for T. cruzi 30 . To our knowledge machine learning approaches to identify EBOV inhibitors have not been attempted elsewhere.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

The search for small molecule inhibitors of Ebola virus (EBOV) has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs) with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC 50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in vitro.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…In our previous publications we described the generation and validation of the Laplacian-corrected Bayesian classifier models developed with cytotoxicity data to create dual-event models 19, 24, 25 using Discovery Studio 3.5 (San Diego, CA). 17, 31-34 These individual models were developed based on several unique datasets: a. MLSMR dose-response and cytotoxicity; b. TAACF-CB2 dose-response and cytotoxicity; and c. TAACF-kinase dose-response and cytotoxicity, where cytotoxicity was determined for Vero cells for each set.…”

Section: Methodsmentioning

confidence: 99%

Are Bigger Data Sets Better for Machine Learning? Fusing Single-Point and Dual-Event Dose Response Data for Mycobacterium tuberculosis

Ekins

Freundlich

Reynolds

2014

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

Tuberculosis is a major neglected disease for which the quest to find new treatments continues. There is an abundance of data from large phenotypic screens in the public domain against Mycobacterium tuberculosis (Mtb). Since machine learning methods can learn from past data, we were interested in addressing whether more data builds better models. We now describe using Bayesian machine learning to assess whether we can improve our models by combining the large quantities of single-point data with the much smaller (higher quality) dual-event datasets, which use both dose-response data for both whole-cell antitubercular activity and Vero cell cytotoxicity. We have evaluated 12 models ranging from different single-point, dual-event dose response, single-point and dual-event dose response as well as combined datasets for three distinct datasets from the same laboratory. We used a fourth dataset of active and inactive compounds from the same group as well as a smaller set of 177 active compounds from GlaxoSmithKline as test sets. Our data suggest combining single-point with dual-event dose response data does not diminish the internal or external predictive ability of the models based on the receiver operator curve (ROC) for these models (internal ROC range 0.83-0.91, external ROC range 0.62-0.83) compared to the orders of magnitude smaller dual event models (internal ROC range 0.6-0.83 and external ROC 0.54-0.83). In conclusion, models developed with 1200-5000 compounds appear to be as predictive as those generated with 25,000 to 350,000 molecules. Our results have implications for justifying further HTS versus focused testing based on model predictions.

show abstract

“…The use of cheminformatics for tuberculosis drug discovery has been summarized [45-47] and can be readily implemented early in the process as a means to limit the number of compounds needing to be screened, therefore saving time and money [48-52]. Recent publications in this area have hit rates >20% and focus on favorable compounds with low or no cytotoxicity [51, 52]. More recently, combining datasets to use all 350,000 molecules with in vitro data from a single laboratory for computational models has been attempted.…”

Section: Introductionmentioning

confidence: 99%

Bigger data, collaborative tools and the future of predictive drug discovery

Ekins

Clark²,

Swamidass

et al. 2014

J Comput Aided Mol Des

Self Cite

View full text Add to dashboard Cite

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.

show abstract

Enhancing Hit Identification in Mycobacterium tuberculosis Drug Discovery Using Validated Dual-Event Bayesian Models

Cited by 52 publications

References 65 publications

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

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

Are Bigger Data Sets Better for Machine Learning? Fusing Single-Point and Dual-Event Dose Response Data for Mycobacterium tuberculosis

Bigger data, collaborative tools and the future of predictive drug discovery

Contact Info

Product

Resources

About