QSAR of 3-methylfentanyl derivatives studied with neural networks method
Abstract
AIM: To use neural networks, which simulate the functions of living nervous systems, in QSAR studies;
METHODS: Using the back-propagation neural networks program devised by us, combining with partial least squares (PLS) method, we studied the relationships of quantum chemical indices and analgesic activities of 25 3-methylfentanyl derivatives;
RESULTS: Through learning process, a good QSAR model was established, and the activities of these compounds were predicted; the correlation between the activities and quantum chemical indices: the net charge of the atom N1, the net charge of the atom O16, the torsional angle of atoms C10-C9-N8-C4, the interatomic distance between atom C7 and the center of phenyl plane C9-14 (PhA), is quite well-matched. Based on these results, an interactive pattern between 3-methylfentanyl derivatives and opioid receptors was suggested;
CONCLUSION: Not only are the results of neural networks superior to those of PLS method but they also provide accurate predictions of the activity of the compounds and also combine the PLS method with neural networks.
Keywords:
METHODS: Using the back-propagation neural networks program devised by us, combining with partial least squares (PLS) method, we studied the relationships of quantum chemical indices and analgesic activities of 25 3-methylfentanyl derivatives;
RESULTS: Through learning process, a good QSAR model was established, and the activities of these compounds were predicted; the correlation between the activities and quantum chemical indices: the net charge of the atom N1, the net charge of the atom O16, the torsional angle of atoms C10-C9-N8-C4, the interatomic distance between atom C7 and the center of phenyl plane C9-14 (PhA), is quite well-matched. Based on these results, an interactive pattern between 3-methylfentanyl derivatives and opioid receptors was suggested;
CONCLUSION: Not only are the results of neural networks superior to those of PLS method but they also provide accurate predictions of the activity of the compounds and also combine the PLS method with neural networks.