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General Information
    • ISSN: 2010-3700
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus(since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(3): 213-218 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.116

Modelling Hydrogen Bond Stability by Regression Trees

Igor Chikalov, Mikhail Moshkov, Peggy Yao, and Jean-Claude Latombe

Abstract—Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. However, H-bonds greatly vary in stability. Different local interactions may reinforce or weaken an H-bond. This paper describes inductive learning methods to train a protein-independent probabilistic model of H-bond stability from molecular dynamics (MD) simulation trajectories. The training data describes H-bond occurrences at successive times along these trajectories by the values of 32 attributes. A trained model is constructed in the form of a regression tree. Experimental results demonstrate that such models can predict H-bond stability quite well. In particular, their performance is roughly 20% better than that of models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a given conformation. The paper discusses several extensions that may yield further improvements.

Index Terms—Molecular dynamics, machine learning, regression tree.

I. Chikalov and Mikhail Moshkov are with Mathematical and CS & Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia (e-mail: igor.chikalov@kaust.edu.sa).
P. Yao and J. C. Latombe are with Computer Science Department, Stanford University, Stanford, CA 94305, USA (e-mail: latombe@cs.stanford.edu)


Cite: Igor Chikalov, Mikhail Moshkov, Peggy Yao, and Jean-Claude Latombe, "Modelling Hydrogen Bond Stability by Regression Trees," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 213-218, 2012.

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