Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis

Ekins, Sean; Casey, Allen; Roberts, David M.; Parish, Tanya; Bunin, Barry A.

doi:10.1016/j.tube.2013.12.001

Cited by 37 publications

(52 citation statements)

References 52 publications

(84 reference statements)

Supporting

Mentioning

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%

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%

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

“…Much of this work was centered on models for Mycobacterium tuberculosis 83–85 taking account of cytotoxicity and prospectively evaluating them to show high hit rates compared to random screening 85–87 . We have since followed this with datasets for Chagas disease 88 and Ebola 89 to repurpose approved drugs as well as model ADME properties such as aqueous solubility, mouse liver microsomal stability 90 , Caco-2 cell permeability 62 , toxicology datasets 91 and transporters 66, 92–97 .…”

Section: Introductionmentioning

confidence: 99%

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Korotcov¹,

Tkachenko²,

et al. 2017

Self Cite

View full text Add to dashboard Cite

Machine learning methods have been applied to many datasets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of endpoints relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying datasets that is applicable to pharmaceutical research. Endpoints relevant to pharmaceutical research include absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery datasets. In this study, we have used datasets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis and malaria to compare different machine learning methods using FCFP6 fingerprints. These datasets represent whole cell screens, individual proteins, physicochemical properties as well as a dataset with a complex endpoint. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen’s kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or datasets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further using multiple metrics with much larger scale comparisons, prospective testing as well as assessment of different fingerprints and DNN architectures beyond those used.

show abstract

Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis

Cited by 37 publications

References 52 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

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

Contact Info

Product

Resources

About