MARS: Computing Three-Dimensional Alignments for Multiple Ligands Using Pairwise Similarities

The three-dimensional (3D) superimposition of molecules
of one
biological target reflecting their relative bioactive orientation
is key for several ligand-based drug design studies (e.g., QSAR studies,
pharmacophore modeling). However, with the lack of sufficient ligand-protein
complex structures, an experimental alignment is difficult or often
impossible to obtain. Several computational 3D alignment tools have
been developed by academic or commercial groups to address this challenge.
Here, we present a new approach, MARS (Multiple Alignments by ROCS-based Similarity), that is based on the pairwise alignment
of all molecules within the data set using the tool ROCS (Rapid Overlay of Chemical Structures). Each pairwise alignment
is scored, and the results are captured in a score matrix. The ideal
superimposition of the compounds in the set is then identified by
the analysis of the score matrix building stepwise a superimposition
of all molecules. The algorithm exploits similarities among all molecules
in the data set to compute an optimal 3D alignment. This alignment
tool presented here can be used for several applications, including
pharmacophore model generation, 3D QSAR modeling, 3D clustering, identification
of structural outliers, and addition of compounds to an already existing
alignment. Case studies are shown, validating the 3D alignments for
six different data sets.