Wydział Chemii

Paolo Mazzatorta

Marjan Vračko

Aneta Jezierska

E. Benfenati

2003

Journal of Chemical Information and Computer Sciences

43

485-492

10.1021/ci0256182

Naukowa

Angielski

Artykuł

Counterprogation neural network is shown to be a powerful and suitable tool for the investigation of toxicity. This study mined a data set of 568 chemicals. Two hundred eighty-two objects were used as the training set and 286 as the test set. The final model developed presents high performances on the data set R² = 0.83 (R² = 0.97 on the training set, R² = 0.59 on the test set). This technique distinguishes itself also for the ability to give to the expert two-dimensional maps suitable for the study of the distribution/clustering of the data and the identification of outliers.

http://dx.doi.org/10.1021/ci0256182

https://www.acs.org/content/acs/en.html