Modeling and predicting binding affinity of phencyclidine-like compounds using machine learning methods

• Published in: Journal of chemometrics 2010-01, Vol.24 (1), p.1-13
• Main Authors: Erdas, Ozlem, Buyukbingol, Erdem, Alpaslan, Ferda Nur, Adejare, Adeboye
• Format: Article
• Language: English
• Subjects: Affinity; Artificial intelligence; Binding; binding affinity prediction; Chemical compounds; Chemistry; Chemometrics; Design engineering; Electrostatics; Exact sciences and technology; Extraction processes; General and physical chemistry; General. Nomenclature, chemical documentation, computer chemistry; Least squares method; Machine learning; Mathematical models; spherical self-organizing maps; Studies; support vector machines; Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry
• ISSN: 0886-9383; 1099-128X; 1099-128X
• DOI: 10.1002/cem.1265

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. In this study, molecular electrostatic potential surfaces of phencyclidine‐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. The resulting maps are visualized using electrostatic potential 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.