Monday, May 1, 2017
A new hierarchical method to determine molecular similarity is presented. The goal of this method is to detect if a pair of molecules have the same spatial and chemical structure by estimating a rigid transformation that aligns the molecules and a correspondence function that matches their atoms. The algorithm tries to detect similarity based on the global spatial structure. If this analysis is not sufficient, the algorithm computes novel local structural rotationinvariant descriptors for the atom neighbourhood and uses this information to match atoms. Two strategies (deterministic and stochastic) on the matching based alignment computation are tested. As result, the atommatching based on local similarity indexes decreases the number of testing trials and significantly reduces the dimensionality of the Hungarian assignation problem. The experiments on wellknown datasets show that our proposal outperforms stateoftheart methods in terms of the required computational time and accuracy.
The proposal tackles hierarchically the detection of molecular similarity. First, global structure information is computed in order to align the molecules and determine their similarity: as for instance the bluegreenred inertia axes. When the global analysis fails, novel local rotationinvariant descriptors are atomwise computed to estimate atomtoatom similarity (matchings): the atoms' color in molecule B indicates their local similarity to the highlighted jth atom in molecule A; the actual corresponding atom in B is also highlighted. 
Contact: alram@cimat.mx








