BIBLIOS

Document type
Journal articles

Document subtype
Full paper

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

Participants in the publication
C. Ventura (Author)
D. A. R. S. Latino (Author)
F. Martins (Author)
Dep. Química e Bioquímica
CQB

Summary
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.


Institution
FACULDADE DE CIÊNCIAS DA UNIVERSIDADE DE LISBOA

Where published
EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY

Publication Identifiers
ISSN - 0223-5234

Publisher
Elsevier



Document Identifiers
URL - http://dx.doi.org/10.1016/j.ejmech.2013.10.029
DOI - https://doi.org/10.1016/j.ejmech.2013.10.029

Rankings
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