QSAR modelling of integrin antagonists using enhanced bayesian regularised genetic neural networks

Jalali Heravi, M ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1080/1062936X.2011.569758
  3. Publisher: 2011
  4. Abstract:
  5. Bayesian regularised genetic neural network (BRGNN) has been used for modelling the inhibition activity of 141 biphenylalanine derivatives as integrin antagonists. Three local pattern search (PS) methods, simulated annealing and threshold acceptance were combined with BRGNN in the form of a hybrid genetic algorithm (HGA). The results obtained revealed that PS is a suitable method for improving the ability of BRGNN to break out from the local minima. The proposed HGA technique is able to retrieve important variables from complex systems and nonlinear search spaces for optimisation. Two models with 8-3-1 artificial neural network (ANN) architectures were developed for describingα 4β 7 and α 4β 1 modulatory activities of integrin antagonists. Monte Carlo crossvalidation was performed to validate the models and Q 2 values of 0.75 and 0.74 were obtained for α 4β 7 and α 4β 1 inhibitory activities, respectively. The scrambling technique was used for sensitivity analysis of descriptors appearing in ANN models. Frequencies of repetition and sensitivity analysis of molecular descriptors revealed that 3D-Morse descriptors are influential factors for describing α 4β 7 inhibitory activity, while in the case of α 4β 1 inhibitory activity, the Randic shape index, the lowest eigenvalue of the Burden matrix and the number of rotatable bonds are important parameters
  6. Keywords:
  7. Genetic algorithm ; Integrin antagonists ; Local search methods ; Pattern search optimisation ; Quantitative structure-activity relationship ; Simulated annealing ; Biphenyl derivative ; Artificial neural network ; Chemistry ; Drug antagonism ; Quantitative structure activity relation ; Validation study ; Biphenyl Compounds ; Enzyme Inhibitors ; Integrins ; Neural Networks (Computer)
  8. Source: SAR and QSAR in Environmental Research ; Volume 22, Issue 3-4 , May , 2011 , Pages 293-314 ; 1062936X (ISSN)
  9. URL: http://www.tandfonline.com/doi/abs/10.1080/1062936X.2011.569758