Premium
Modeling and predicting binding affinity of phencyclidine‐like compounds using machine learning methods
Author(s) -
Erdas Ozlem,
Buyukbingol Erdem,
Alpaslan Ferda Nur,
Adejare Adeboye
Publication year - 2010
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1265
Subject(s) - phencyclidine , affinities , cartesian coordinate system , support vector machine , chemistry , computer science , artificial intelligence , binding affinities , machine learning , stereochemistry , mathematics , biochemistry , nmda receptor , receptor , geometry
Machine learning methods have always been promising in the science and engineering fields, and the use of these methods in chemistry and drug design has advanced especially since the 1990s. In this study, molecular electrostatic potential (MEP) surfaces of phencyclidine‐like (PCP‐like) compounds are modeled and visualized in order to extract features that are useful in predicting binding affinities. In modeling, the Cartesian coordinates of MEP surface points are mapped onto a spherical self‐organizing map (SSOM). The resulting maps are visualized using electrostatic potential (ESP) values. These values also provide features for a prediction system. Support vector machines and partial least‐squares method are used for predicting binding affinities of compounds. Copyright © 2009 John Wiley & Sons, Ltd.