This paper presents regression models obtained from a process of blind prediction of peptide binding affinity from provided descriptors for several distinct datasets as part of the 2006 Comparative Evaluation of Prediction Algorithms (COEPRA) contest. This paper finds that kernel partial least squares, a nonlinear partial least squares (PLS) algorithm, outperforms PLS, and that the incorporation of transferable atom equivalent features improves predictive capability.
@article{arxiv.1108.5397,
title = {Prediction of peptide bonding affinity: kernel methods for nonlinear modeling},
author = {Charles Bergeron and Theresa Hepburn and C. Matthew Sundling and Michael Krein and Bill Katt and Nagamani Sukumar and Curt M. Breneman and Kristin P. Bennett},
journal= {arXiv preprint arXiv:1108.5397},
year = {2011}
}