Create Bayesian Model

Description
Builds a user model using Bayesian categorization.

Uses Bayesian categorization to build a model that distinguishes between "good" and "bad" ligands. "Good" ligands are defined by having a value of "1" for the property set by Property for Active.

A user model is created, named according to Model Name. The model can be used to predict how "good" ligands are using Calculate Molecular Properties.

Information
Name
Create Bayesian Model
Status
Success
User
Administrator
DS Version
3.5.0.12159
PP Version
8.5.0.200
DS Client Version
3.5.0.12158
Server Name
localhost (Windows64)
Server Ports
9944 (9943)
Start Time
01/04/21 09:39:43
Finish Time
01/04/21 09:39:53
Execution Time
00:00:10
Summary
ROC score is 0.721 (leave-one-out).
Best cutoff for this model is 0.142.
See ModelDescription.html for more detailed information about this model.
Test set validation: ROC score = 0.767065390749602
Model Rating: Accuracy 0.767: Fair
Confusion Matrix: True Positives = 76, False Negatives = 34, False Positives = 36, True Negatives = 78
5-Fold Cross-Validation Result
Model Name ROC Score ROC Rating True Positive False Negative False Positive True Negative Sensitivity Specificity Concordance
BayesianTempModel-10 0.702 Fair 394 45 52 402 0.897 0.885 0.891
Validation Result Using External Test Set testset-3.sd
Model Name ROC Score ROC Rating True Positive False Negative False Positive True Negative Sensitivity Specificity Concordance
BayesianTempModel-10 0.767 Fair 76 34 36 78 0.691 0.684 0.688
Parameters
Protocol Settings
 
Input Ligands
 
Input Test Ligands
 
Property for Active
class
 
Model Name
BayesianTempModel-10
Independent Properties
ALogP,ECFP_14,Apol,Molecular_Weight,Num_H_Donors,Wiener
Calculable Properties
ALogP,ECFP_14,Apol,Molecular_Weight,Num_H_Donors,Wiener
User Properties
Cross Validation
True
Folds
5
Learn Options
Validate Models
Remove Uninformative Bins
Equipopulate Bins
Model Domain Fingerprint
FCFP_2
Additional Properties
Advanced
Number of Bins
10

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