Quality of Fit and Predictive Ability of a continuous QSAR Model
Quality of Fit and Predictive Ability of a continuous QSAR Model
According to A. Tropsha et al. (QSAR Comb. Sci. 22 (2003) 69-77 & Mol. Inf. 2010, 29, 476-488) the following statistical criteria must be satisfied by a predictive model:
where:
R^2 Correlation coefficient between the predicted and observed activities
Rcvext^2 External cross validation
R0^2 Coefficient of determination: predicted versus observed activities
R'0^2 Coefficient of determination: observed versus predicted activities
k = slope: predicted versus observed activities regression lines through the origin
k’= slope: observed versus predicted activities regression lines through the origin
If this node is useful to you, please cite the following papers:
G. Melagraki, Α. Afantitis, H. Sarimveis, P.A. Koutentis, O. Igglessi – Markopoulou and G. Kollias "Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors" Molecular Diversity 13 (3), pp. 301-311.
Α. Afantitis, G. Melagraki, H. Sarimveis, P.A. Koutentis, O. Igglessi – Markopoulou and G. Kollias "A combined LS-SVM and MLR QSAR workflow for predicting the inhibition of CXCR3 receptor by quinazolinone analogues" Molecular Diversity (2010) 14:225–235
KNIME Node Options:
Input Ports
0 Values for the dependent variable, predicted by the model (ypred)
1 Values for the dependent variable for the test set (yexp)
2 Values for the dependent variable for the training set (ytr)
Output Ports 0 Quality of Fit and Predictive Ability Statistics of a continuous QSAR Model
Views
Enalos Model Acceptability Criteria node provides a summary View with information about the predictive ability of model.
Download: Enalos Model Acceptability Criteria Node from:KNIME Update Community Contributions Nightly
Instructions: Open Knime, Go to Help, Install New Sofware, Point to (work with) Nighty (Community Contributions), Select Enalos Nodes for KNIME (view figure)