2014
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Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis
Abstract: The search for compounds active against Mycobacterium tuberculosis is reliant upon high throughput screening (HTS) in whole cells. We have used Bayesian machine learning models which can predict anti-tubercular activity to filter an internal library of over 150,000 compounds prior to in vitro testing. We used this to select and test 48 compounds in vitro; 11 were active with MIC values ranging from 0.4 µM to 10.2 µM, giving a high hit rate of 22.9%. Among the hits, we identified several compounds belonging to … Show more
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“…Over the past decade, whole-cell HTS approaches have been used to identify novel compounds with antitubercular activity, resulting in very low hit rates. Virtual screening using machine learning modeling utilizes data (actives and inactives) from these screens and smaller scale studies to improve the time and cost efficiencies of hit discovery. Machine learning has been similarly used in many areas of biomedical and environmental research. This study is a natural extension of our prior machine learning studies applied to Mtb , which demonstrated an enhancement in hit rate. To further leverage public data to enhance the performance of our models, we have now curated a large, chemically diverse Mtb data set through careful analysis of in vitro data mined from various sources, such as the primary literature and ChEMBL, and also included in vivo data.…”
Section: Discussionmentioning
confidence: 80%
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD).
Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD.
The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC).
Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.
“…Over the past decade, whole-cell HTS approaches have been used to identify novel compounds with antitubercular activity, resulting in very low hit rates. Virtual screening using machine learning modeling utilizes data (actives and inactives) from these screens and smaller scale studies to improve the time and cost efficiencies of hit discovery. Machine learning has been similarly used in many areas of biomedical and environmental research. This study is a natural extension of our prior machine learning studies applied to Mtb , which demonstrated an enhancement in hit rate. To further leverage public data to enhance the performance of our models, we have now curated a large, chemically diverse Mtb data set through careful analysis of in vitro data mined from various sources, such as the primary literature and ChEMBL, and also included in vivo data.…”
Section: Discussionmentioning
confidence: 80%
“…We curated ∼2700 compounds from the primary literature published in 2014–2016, a data set extracted from ChEMBL of approximately 12 000 molecules, the SRI/NIAID data set containing ∼7000 molecules, and the ∼800 compounds from in vivo data. After duplicate removal, our training set consisted of 18 886 molecules.…”
Section: Resultsmentioning
confidence: 99%
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD).
Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD.
The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC).
Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.
“…29 These types of models (Bayesian, SVM, and RP) are commonly used for drug discovery applications in virtual screening and balance fitting the training set data with external predictive capability outside of the training set's chemical property space. Such approaches have been described by us in some detail previously, 29,55 and the machine learning with the Mtb in vivo data paralleled our practices with previous Mtb in vitro data sets. 8,22,28,29 We utilized FCFP_6 fingerprints 55,56 and the following set of readily interpretable molecular descriptors: ALogP, 57 molecular weight, number of H-bond identical ROC AUC values with leave-one-out (0.77), leave-out 50% × 100 (0.72), and the five-fold cross validation (0.73, Tables 2 and 3).…”
Section: ■ Resultsmentioning
confidence: 95%
“…Such approaches have been described by us in some detail previously, 29,55 and the machine learning with the Mtb in vivo data paralleled our practices with previous Mtb in vitro data sets. 8,22,28,29 We utilized FCFP_6 fingerprints 55,56 and the following set of readily interpretable molecular descriptors: ALogP, 57 molecular weight, number of H-bond identical ROC AUC values with leave-one-out (0.77), leave-out 50% × 100 (0.72), and the five-fold cross validation (0.73, Tables 2 and 3). With five-fold cross validation (leave out 20% × 5), the concordance (79.0%), specificity (90.3%), and sensitivity (66.3%) also suggested a bias toward predicting inactive compounds (Table 3), although all the values are higher compared to leave out 50% × 100 fold.…”
Section: ■ Resultsmentioning
confidence: 95%
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD).
Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD.
The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC).
Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.
“…In prior work, we have used Bayesian machine learning to build models of whole-cell screens of small molecule compounds active against Mtb 14 , which led to the identification of new active compounds 13 14 44 . After testing 94 molecules against Mtb ThyX at 100 μM (the training set, Supplemental Table 6 ), we generated a Bayesian model using molecules with >70% inhibition as actives.…”
Section: Resultsmentioning
confidence: 99%
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD).
Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD.
The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC).
Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.
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