The definition of the applicability domain relevant to skin sensitization for the aromatic nucleophilic substitution mechanism

2012 ◽  
Vol 23 (7-8) ◽  
pp. 649-663 ◽  
Author(s):  
S.J. Enoch ◽  
T.W. Schultz ◽  
M.T.D. Cronin
Keyword(s):  
Nucleophilic Substitution ◽  
Applicability Domain ◽  
Skin Sensitization ◽  
Aromatic Nucleophilic Substitution ◽  
Substitution Mechanism ◽  
Definition Of

scholarly journals Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability

2019 ◽  
Vol 20 (19) ◽  
pp. 4833 ◽  
Author(s):  
Anke Wilm ◽  
Conrad Stork ◽  
Christoph Bauer ◽  
Andreas Schepky ◽  
Jochen Kühnl ◽  
...  
Keyword(s):  
Correlation Coefficients ◽  
Applicability Domain ◽  
Support Vector ◽  
Data Sets ◽  
Skin Sensitization ◽  
Prediction Reliability ◽  
Local Lymph Node ◽  
Definition Of ◽  
The Individual ◽  
In Silico Models

The ability to predict the skin sensitization potential of small organic molecules is of high importance to the development and safe application of cosmetics, drugs and pesticides. One of the most widely accepted methods for predicting this hazard is the local lymph node assay (LLNA). The goal of this work was to develop in silico models for the prediction of the skin sensitization potential of small molecules that go beyond the state of the art, with larger LLNA data sets and, most importantly, a robust and intuitive definition of the applicability domain, paired with additional indicators of the reliability of predictions. We explored a large variety of molecular descriptors and fingerprints in combination with random forest and support vector machine classifiers. The most suitable models were tested on holdout data, on which they yielded competitive performance (Matthews correlation coefficients up to 0.52; accuracies up to 0.76; areas under the receiver operating characteristic curves up to 0.83). The most favorable models are available via a public web service that, in addition to predictions, provides assessments of the applicability domain and indicators of the reliability of the individual predictions.

scholarly journals QSPR modelling of the octanol/water partition coefficient of organometallic substances by optimal SMILES-based descriptors

2009 ◽  
Vol 7 (4) ◽  
pp. 846-856 ◽  
Author(s):  
Andrey Toropov ◽  
Alla Toropova ◽  
Emilio Benfenati
Keyword(s):  
Partition Coefficient ◽  
Organometallic Compounds ◽  
Applicability Domain ◽  
Training Set ◽  
Input Line ◽  
Test Set ◽  
Water Partition Coefficient ◽  
Definition Of

AbstractUsually, QSPR is not used to model organometallic compounds. We have modeled the octanol/water partition coefficient for organometallic compounds of Na, K, Ca, Cu, Fe, Zn, Ni, As, and Hg by optimal descriptors calculated with simplified molecular input line entry system (SMILES) notations. The best model is characterized by the following statistics: n=54, r2=0.9807, s=0.677, F=2636 (training set); n=26, r2=0.9693, s=0.969, F=759 (test set). Empirical criteria for the definition of the applicability domain for these models are discussed.

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