Optimal neighbor selection in molecular similarity: comparison of arbitrary versus tailored prediction spaces†

†Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Shanghai, China, October 29–November 1, 2005).

Abstract

Three classes of arbitrary quantitative molecular similarity analysis (QMSA) methods have been computed using atom pairs (APs), topological indices (TIs), and principal components (PCs) derived from topological indices. Tailored QMSA models have been developed from TIs selected through ridge regression. K-nearest neighbor (kNN) based estimation has been applied to all of the methods to estimate normal vapor pressure (p _vap) and water solubility (sol) for a set of 194 chemicals. Results show that the tailored QMSA methods are superior to arbitrary similarity methods in estimating both of these properties for the given set of chemicals.

†Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Shanghai, China, October 29–November 1, 2005).

Keywords:

• Quantitative molecular similarity analysis (QMSA)
• Tailored QMSA
• Atom pairs
• Topological indices
• kNN

Acknowledgements

This manuscript is contribution number 396 from the Center for Water and the Environment of the Natural Resources Research Institute. This material is based on research sponsored by the Air Force Research Laboratory, under agreement number F49620-02-1-0138. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.

The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Research Laboratory or the US Government.

Notes

†Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Shanghai, China, October 29–November 1, 2005).