Applicability Domain (APD) based on the Euclidean distances
Applicability Domain (APD) based on the Euclidean distances.
Domain of model applicability must be defined to flag compounds in the test set for which predictions may be unreliable. In this node similarity measurements are used to define the domain of applicability of the model based on the Euclidean distances among all training compounds and the test or virtual screening compounds. The distance of a test compound to its nearest neighbor in the training set is compared to the predefined applicability domain threshold (APD). If the similarity is beyond this threshold, the prediction is considered unreliable (S. Zhang, A. Golbraikh, S. Oloff, H. Kohn, A. Tropsha J. Chem. Inf. Model., 46 (2006), pp. 1984–1995).
APD is calculated as follows:
APD = 'd' + Zσ
Calculation of 'd' and σ is performed as follows: First, the average of Euclidean distances between all pairs of training compounds is calculated. Next, the set of distances that were lower than the average is formulated. 'd' and s are finally calculated as the average and standard deviation of all distances included in this set. Z is an empirical cutoff value and the default value is 0.5.
If this node is useful to you, please cite the following papers:
G. Melagraki, Α. Afantitis, H. Sarimveis, P.A. Koutentis, O. Igglessi – Markopoulou, G. Kollias "In Silico Exploration for Identifying Structure–Activity Relationship of MEK Inhibition and Oral Bioavailability for Isothiazole Derivatives" Chemical Biology and Drug Design 2010; 76: 397–406
A. Afantitis, G. Melagraki, P.A. Koutentis, H. Sarimveis, G. Kollias. Ligand - based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen Maps and Counterpropagation Artificial Neural Networks” European Journal of Medicinal Chemistry 46 (2011) 497-508
KNIME Node Options:
Dialog Options
Z : Z is an empirical cutoff value and the default value is 0.5.
Ports Input Ports
0 Training Set (Please insert only selected attributes)
1 Test or Virtual Screening Set (Please insert the same attributes with Training Set)
Output Ports
0 APD for each compound of the Test/ Virtual Screening Set, APDthreshold for the Training Set, Result: Reliable / Unreliable
View
Each Enalos Domain of Applicability node provides a summary View with information about the percentage of the reliable and the unreliable predictions.
Download Enalos Domain Similarity 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)
Applicability Domain (APD) based on the Euclidean distances. 
