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Mapping the Electrostatic Potential of Muscarinic and Nicotinic Agonists with Artificial Neural Networks
Author(s) -
Gasteiger Johann,
Li Xinzhi
Publication year - 1994
Publication title -
angewandte chemie international edition in english
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.831
H-Index - 550
eISSN - 1521-3773
pISSN - 0570-0833
DOI - 10.1002/anie.199406431
Subject(s) - muscarinic acetylcholine receptor , nicotinic agonist , receptor , artificial neural network , acetylcholine receptor , chemistry , neuroscience , biophysics , biology , computer science , biochemistry , artificial intelligence
Distinct similarities can be detected with artificial neural networks between molecules that bind to the muscarinic receptors (e.g. 1 and 2 ) and between molecules that bind to nicotinic receptors (e.g. 3 and 4 ). Furthermore such networks highlight differences between molecules that bind at these two different receptors.