BIBLIOS

Tipo
Artigos em Revista

Tipo de Documento
Artigo Completo

Título
Comparison of Multiple Linear Regressions and Neural Networks Based QSAR Models for the Design of New Antitubercular Compounds.

Participantes na publicação
C. Ventura (Author)
D. A. R. S. Latino (Author)
F. Martins (Author)
Dep. Química e Bioquímica
CQB

Resumo
The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R2 of 0.874 and RMSE of 0.437 against R2 of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models.


Instituição
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA

Suporte
EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY

Identificadores da Publicação
ISSN - 0223-5234

Editora
Elsevier



Identificadores do Documento
URL - http://dx.doi.org/10.1016/j.ejmech.2013.10.029
DOI - https://doi.org/10.1016/j.ejmech.2013.10.029

Identificadores de Qualidade
SCIMAGO Q1 (2013) - 1.216 - Drug Discovery
Web Of Science Q1 (2013) - 3.432 - Medicinal Chemistry

Keywords
QSARs Neural Networks Multiple Linear Regressions Antitubercular activity Hydrazides