Abstract
In the methodologies of 3D-QSAR such as CoMFA (Comparative Molecule Field Analysis), appropriate molecular alignment is definitely required for predictive data-modeling and correct analysis of the model. In this paper, the novel method for molecular alignment using Hopfield Neural Network (HNN) is proposed. Each molecule is represented by four chemical properties (Hydrophobic, Hydrogen-bonding donor, Hydrogen-bonding acceptor, Hydrogen-bonding donor/acceptor), and then properties among molecules are corresponded by HNN. In order to validate usefulness of this method, 12 pairs of 24 enzyme inhibitors are used as a test set, and our method could successfully reproduce the alignments obtained from X-ray crystallography.
