A NEURAL NETWORK SAR MODEL FOR ALLERGIC CONTACT DERMATITIS

Allergic contact dermatitis results for 259 compounds were used for deriving a qualitative structure-activity relationship (SAR) model. Chemicals were described by means of one physicochemical descriptor, one topological index, and twelve structural alerts (i.e., a value of 1 indicated the presence of the structural feature in a molecule, 0 the absence). A three-layer feed forward neural network trained by the back-propagation algorithm was used as statistical engine. The comparison of the simulation performances of the obtained model to those produced by a classical linear discriminant analysis clearly revealed the usefulness of the nonlinear methods of modeling skin sensitization.

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