title: A neural networks study of quinone compounds with trypanocidal activity.
abstract: This work investigates neural network models for predicting the trypanocidal activity of 28 quinone compounds. Artificial neural networks (ANN), such as multilayer perceptrons (MLP) and Kohonen models, were employed with the aim of modeling the nonlinear relationship between quantum and molecular descriptors and trypanocidal activity. The calculated descriptors and the principal components were used as input to train neural network models to verify the behavior of the nets. The best model for both network models (MLP and Kohonen) was obtained with four descriptors as input. The descriptors were T5 (torsion angle), QTS1 (sum of absolute values of the atomic charges), VOLS2 (volume of the substituent at region B) and HOMO-1 (energy of the molecular orbital below HOMO). These descriptors provide information on the kind of interaction that occurs between the compounds and the biological receptor. Both neural network models used here can predict the trypanocidal activity of the quinone compounds with good agreement, with low errors in the testing set and a high correctness rate. Thanks to the nonlinear model obtained from the neural network models, we can conclude that electronic and structural properties are important factors in the interaction between quinone compounds that exhibit trypanocidal activity and their biological receptors. The final ANN models should be useful in the design of novel trypanocidal quinones having improved potency.
authors.affiliation: Departamento de Química e Física Molecular, Instituto de Química de São Carlos, Universidade de São Paulo, CP 780, 13560-970, São Carlos, SP, Brasil.
authors.forename: Fábio Alberto
authors.initials: FA
authors.lastname: de Molfetta
authors.forename: Wagner Fernando Delfino
authors.initials: WF
authors.lastname: Angelotti
authors.forename: Roseli Aparecida Francelin
authors.initials: RA
authors.lastname: Romero
authors.forename: Carlos Alberto
authors.initials: CA
authors.lastname: Montanari
authors.forename: Albérico Borges Ferreira
authors.initials: AB
authors.lastname: da Silva
mesh_terms: D016000:Cluster Analysis; D016571:Neural Networks, Computer; D011809:Quinones / Q000494:pharmacology*; D013329:Structure-Activity Relationship; D014344:Trypanocidal Agents / Q000494:pharmacology*
chemical_list: D011809:Quinones; D014344:Trypanocidal Agents