title: Application of the novel molecular alignment method using the Hopfield Neural Network to 3D-QSAR.
abstract: Recently, we investigated and proposed the novel molecular alignment method with the Hopfield Neural Network (HNN). Molecules are represented by four kinds of chemical properties (hydrophobic group, hydrogen-bonding acceptor, hydrogen-bonding donor, and hydrogen-bonding donor/acceptor), and then those properties between two molecules correspond to each other using HNN. The 12 pairs of enzyme-inhibitors were used for validation, and our method could successfully reproduce the real molecular alignments obtained from X-ray crystallography. In this paper, we apply the molecular alignment method to three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. The two data sets (human epidermal growth factor receptor-2 inhibitors and cyclooxygenase-2 inhibitors) were investigated to validate our method. As a result, the robust and predictive 3D-QSAR models were successfully obtained in both data sets.
mesh_terms: D051546:Cyclooxygenase 2; D052246:Cyclooxygenase 2 Inhibitors; D016861:Cyclooxygenase Inhibitors / Q000737:chemistry / Q000494:pharmacology; D016208:Databases, Factual; D004791:Enzyme Inhibitors / Q000737:chemistry* / Q000494:pharmacology; D066246:ErbB Receptors / Q000037:antagonists & inhibitors; D006801:Humans; D007527:Isoenzymes / Q000037:antagonists & inhibitors; D008565:Membrane Proteins; D008956:Models, Chemical; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011451:Prostaglandin-Endoperoxide Synthases; D021281:Quantitative Structure-Activity Relationship
chemical_list: D052246:Cyclooxygenase 2 Inhibitors; D016861:Cyclooxygenase Inhibitors; D004791:Enzyme Inhibitors; D007527:Isoenzymes; D008565:Membrane Proteins; D051546:Cyclooxygenase 2; C496540:PTGS2 protein, human; D011451:Prostaglandin-Endoperoxide Synthases; D066246:ErbB Receptors