Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.
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 (R(2) of 0.874 and RMSE of 0.437 against R(2) 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.
Centro de Química e Bioquímica (CQB), Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade de Lisboa, Ed. C8, Campo Grande, 1749-016 Lisboa, Portugal; Instituto Superior de Educação e Ciências, Alameda das Linhas de Torres 179, 1750 Lisboa, Portugal. Electronic address: cventura@isec.universitas.pt.
Cristina
C
Ventura
Diogo A R S
DA
Latino
Filomena
F
Martins
D000995:Antitubercular Agents / Q000138:chemical synthesis / Q000737:chemistry / Q000494:pharmacology*; D004305:Dose-Response Relationship, Drug; D015195:Drug Design; D016014:Linear Models; D008826:Microbial Sensitivity Tests; D008958:Models, Molecular; D015394:Molecular Structure; D009169:Mycobacterium tuberculosis / Q000187:drug effects*; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship
D000995:Antitubercular Agents
AA; AAE; AE; ANN; Antitubercular activity; Artificial Neural Network; AsNNs; CPNNs; Counter-Propagation Neural Network; EnsFFNNs; Ensembles of Feed-Forward Neural Networks; FFNNs; Feed-Forward Neural Networks; HIV; Hydrazides; LMO; LOO; M. tuberculosis; MDR-TB; MIC; MLR; MLT; Multiple Linear Regression; Multiple Linear Regressions; Mycobacterium tuberculosis; NNs; Neural Networks; Q(2); QSARs; QSPRs; RFs; RMSE; SD; SL; SOMs; TB; TDR-TB; WHO; World Health Organization; XDR-TB; absolute average error; antitubercular activity; associative neural networks; average error; cross-validated correlation coefficient; extensively drug-resistant tuberculosis; human immunodeficiency virus; k-Nearest Neighbor; kNN; leave-many-out; leave-one-out; machine learning techniques; minimum inhibitory concentration; multidrug-resistant tuberculosis; quantitative structure–activity relationships; quantitative structure–property relationships; random forests; root mean squared error; self-organizing maps; significance level; standard deviation; totally drug-resistant tuberculosis; tuberculosis