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Predicting the effect of hydrophobicity surface on binding affinity of PCP-like compounds using machine learning methods
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Date
2011
Author
Yoldaş, Mine
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This study aims to predict the binding affinity of the PCP-like compounds by means of molecular hydrophobicity. Molecular hydrophobicity is an important property which aff ects the binding affinity of molecules. The values of molecular hydrophobicity of molecules are obtained on three-dimensional coordinate system. Our aim is to reduce the number of points on the hydrophobicity surface of the molecules. This is modeled by using self organizing maps (SOM) and k-means clustering. The feature sets obtained from SOM and k-means clustering are used in order to predict binding affinity of molecules individually. Support vector regression and partial least squares regression are used for prediction.
Subject Keywords
Support vector machines
,
Machine learning
URI
http://etd.lib.metu.edu.tr/upload/12613215/index.pdf
https://hdl.handle.net/11511/20556
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Graduate School of Natural and Applied Sciences, Thesis
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M. Yoldaş, “Predicting the effect of hydrophobicity surface on binding affinity of PCP-like compounds using machine learning methods,” M.S. - Master of Science, Middle East Technical University, 2011.
