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Bayesian alignment of continuous molecular shapes using random fields

Czogiel, Irina; Dryden, Ian L.; Brignell, Christopher J.


Irina Czogiel

Ian L. Dryden

Christopher J. Brignell


Statistical methodology is proposed for comparing
molecular shapes. In order to account for the continuous nature of molecules, classical shape analysis methods are combined with techniques used for predicting random fields in spatial statistics. Applying a modification of Procrustes analysis, Bayesian inference is carried out using Markov chain Monte Carlo methods for the pairwise alignment of the resulting molecular fields. Superimposing entire fields rather than the configuration matrices of nuclear positions thereby solves the problem that there is usually no clear one--to--one correspondence between the atoms of the two molecules under consideration. Using a similar concept, we also propose an adaptation of the generalised Procrustes analysis algorithm for the simultaneous alignment of multiple molecular fields. The methodology is applied to a dataset of 31 steroid molecules.


Czogiel, I., Dryden, I. L., & Brignell, C. J. Bayesian alignment of continuous molecular shapes using random fields. Manuscript submitted for publication

Journal Article Type Article
Deposit Date May 8, 2009
Journal ..
Peer Reviewed Not Peer Reviewed
Public URL


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