SiSi3D: Using Significantly Similar Subgraphs for Similarity Search in 3D

Tobias Girschick, Technical University Munich, Germany

Our approach - SiSi3D (significant similarities in 3D) - addresses the task of similarity search for 3D molecular graphs, where atoms are annotated with coordinates in three-dimensional Euclidean space and atomic descriptors. The similarity of two molecules is determined by the occurrence of significantly similar subgraphs, more precisely sets of annotated paths spanning the molecule. SiSi3D not only finds similar molecules, it also provides significance estimates of how unlikely it is to obtain such similarity based on a statistical background model.
Our experiments on two drug classes (sartans and statins) as well as on several PubChem BioAssays show that SiSi3D outperforms standard feature-vector (PubChem fingerprint) and maximum common subgraph (MCS) based methods for identifying structures with similar modes of action in a database of unrelated structures.

(presenting author: Tobias Girschick)