Reliable predictions

The Pred-Skin application is based on externally predictive QSAR models of skin sensitization. The models were built using the most extensive database containing human, in vivo (LLNA), in chemico (DPRA), and in vitro (KeratinoSens and H-CLAT) data, addressing all the key steps of the skin sensitization adverse outcome pathway (AOP). So far, PredSkin is the only tool available for predicting skin sensitization based on human data!

Machine Learning Technology

PredSkin was developed as a tool to identify putative skin sensitizers. The integrative Naïve Bayes model was generated using the predictions of each QSAR model developed independently for the five skin sensitization assays. This model achieved balanced accuracy, sensitivity, and specificity up to 0.89-1.00, and it has shown to be a better predictive of the human response than the LLNA.

Probability Maps

The probability maps allow the visualization of the fragment contributions predicted by the QSAR models. This method provides a straightforward interpretation of the predicted activity, assisting users to propose structural modifications to reduce the skin sensitization potential of chemicals.

Predict a single molecule





Instructions


Insert SMILES

Directly paste the SMILES representation of the desired chemical structure.

or Draw

Draw the structure using the "Molecular Editor".

Predict

Click on the “Predict Skin Sensitization” button.

Draw molecule or load a file

If this server was useful to you, please cite our work and help us maintaining this service up to date.

1. Pred-Skin: A fast and reliable tool to assess chemically-induced skin sensitization. Braga, R. C.; Alves, V. M.; Muratov, E. N.; Strickland, J.; Kleinstreuer, N.; Trospsha, A.; Andrade, C. H. J. Chem. Inf. Model. 2017, 57 (5), 1013-1017.

3. A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment. Alves, V. M.; Capuzzi, S. J.; Braga, R. C.; Borba, J. V. B.; Silva, A. C.; Luechtefeld, T.; Hartung, T.; Andrade, C. H.; Muratov, E. N.; Tropsha, A.  ACS Sustainable Chem. Eng. 2018 6 (3), 2845-2859

2. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization. Alves, V. M.; Capuzzi, S. J.; Muratov, E. N.; Braga, R. C.; Thornton, T. E.; Fourches, D.; Strickland, J.; Kleinstreuer, N.; Andrade, C. H.; Tropsha, A. Green Chemistry 2016  18(24), 6501-6515.

3. A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment. Alves, V. M.; Capuzzi, S. J.; Braga, R. C.; Borba, J. V. B.; Silva, A. C.; Luechtefeld, T.; Hartung, T.; Andrade, C. H.; Muratov, E. N.; Tropsha, A.  ACS Sustainable Chem. Eng. 2018 6 (3), 2845-2859

Real Life Applications

To get started with this tutorial, you must follow an example of Pred-Skin 3.0 on real-life applications for skincare and personal care products.

Tutorial

 

More info

Stay tuned! Read the Pre-print version with detailed information and some industrials applications presented by authors.

Pre-Print Datasets Bayesian Info