Predicting biological activity of 2,4,6-trisubstituted 1,3,5-triazines

Citation:
El-Atta, A. A. H., M. I. Moussa, and A. E. Hassenian, " Predicting biological activity of 2,4,6-trisubstituted 1,3,5-triazines", 5ththe 5th International Conference on Innovations in Bio-Inspired Computing and Applications - IBICA2014 (Springer), Ostrava, Czech Republic., 22-24 June, 2013.

Date Presented:

22-24 June

This paper presents an approach to predict the activity of
analogues of 2,4,6-trisubstituted 1,3,5-triazines as cannabinoid recep-
tor (CB2) agonists using random forest technique. We compute twenty
molecular descriptors for a data set of 58 analogues for the component,
and depending on values of these descriptors we train random forest
to nd a relation between biological activity and molecular structure of
analogues. The results obtained by random forest were compared with
the decision tree and support vector machine classi ers and the random
forest has 100% overall predicting accuracy and for decision tree and
support vector machine were 93% and 67% respectively.