QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA–MLR)

This publication has 37 references indexed in Scilit:

• The discovery of aminopyrazines as novel, potent Nav1.7 antagonists: Hit-to-lead identification and SAR
  Bioorganic & Medicinal Chemistry Letters, 2012
• QSAR study on melanocortin-4 receptors by support vector machine
  European Journal of Medicinal Chemistry, 2010
• Familial pain syndromes from mutations of the Na_v1.7 sodium channel
  Annals of the New York Academy of Sciences, 2010
• A novel Na_v1.7 mutation producing carbamazepine‐responsive erythromelalgia
  Annals of Neurology, 2009
• Inherited Neuronal Ion Channelopathies: New Windows on Complex Neurological Diseases
  Journal of Neuroscience, 2008
• Application of principal component-genetic algorithm-artificial neural network for prediction acidity constant of various nitrogen-containing compounds in water
  Monatshefte für Chemie - Chemical Monthly, 2008
• QSRR Study of GC Retention Indices of Essential-Oil Compounds by Multiple Linear Regression with a Genetic Algorithm
  Chromatographia, 2008
• A single sodium channel mutation produces hyper- or hypoexcitability in different types of neurons
  Proceedings of the National Academy of Sciences, 2006
• Clustering techniques: The user's dilemma
  Pattern Recognition, 1976
• p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure
  Journal of the American Chemical Society, 1964