  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.
