Such packages are also responsible for building the adjacency matrix representing the molecular graph. In the standalone tool, molecular input is handled by OpenBabel (via its Python interface pybel ) or RDKit. The hrmsd function is not exposed in the CLI, to avoid erroneous calculations. Spyrmsd also offers a standalone RMSD tool as a command line interface (CLI) exposing the functionality of the rmsd and symmrmsd functions. This simple API makes spyrmsd completely agnostic of the way molecules are stored in different software, as long as they can provide the minimal information required. For this reason the application programming interface (API) is minimalistic: only atomic coordinates and atomic numbers ( rmsd and hrmsd), and molecular adjacency matrices ( symmrmsd) have to be passed to RMSD functions in the form of numpy arrays. Spyrmsd is designed to be easily integrated in existing Python libraries or pipelines. The minimum RMSD (computed using the QCP method ) can be obtained with the keyword minimize=True, with and without symmetry-corrections. hrmsd is provided for comparison with existing implementations and should not be used otherwise, because of the problems outlined above. rmsd is provided to compute the standard RMSD when symmetry does not play a role (or when the molecular graph is too large to efficiently apply symmetry-corrections) and atoms are listed in the same order. Symmrmsd should always be used for small molecules, in order to get the right symmetry-corrected RMSD. Symmrmsd for the computation of symmetry-corrected RMSD, Hrmsd for the computation of RMSD using the Hungarian algorithm, Rmsd for the computation of the standard RMSD, The following functions are available to the user: The main module of spyrmsd is the rmsd module, where all the high-level RMSD functions are implemented. In order to improve speed, graph isomorphisms are cached by default when computing the RMSD between multiple conformations of the same molecule. The graph isomorphism problem is a non-polynomial (NP) problem and therefore symmetry-corrected RMSD calculations are only suited for small to medium sized molecules. All possible graph isomorphisms are computed using the VF2 algorithm and the lowest RMSD among all isomorphisms is retained. Spyrmsd can leverage networkx or graph-tool for graph representation and graph matching. $$\begin\) the RMSD between the two molecules can be computed using the standard RMSD formulation of Eq. ( 1). Here we present a new Python tool, spyrmsd, for the calculation of symmetry-corrected RMSDs based on graph isomorphisms. Since molecular connectivity is naturally represented by graphs (atoms as vertices and bonds as edges), tools from graph theory can be used to obtain the correct atom-atom mapping for two different conformers of the same molecule, thus avoiding the problems outlined above. In the case of symmetric molecules, different binding poses can be chemically identical but different in terms of atom-atom mapping. the order of atoms is different in the two structures being compared-and/or for symmetric molecules. This assumption breaks down when such labels are not conserved-i.e. In different words, atoms are often assumed to be labelled according to their position in a coordinate file (or data structure) and they are paired according to such label. Many simple scripts to compute RMSDs are based on the assumption of a direct one-to-one mapping between atoms of different conformers of the same ligand. RMSD calculations are also used in other contexts, for example for the evaluation of diversity in generated conformers. A common metric to evaluate the difference between the predicted binding pose and the crystallographic pose is the heavy-atoms root mean square displacement (RMSD), although other metrics have been suggested. The performance of docking programs is often assessed by their ability to reproduce the crystallographic pose of the bound ligand. Protein-ligand docking consists of the prediction of binding modes and binding affinity of a (flexible) ligand to a target of known structure. Protein-ligand docking in particular is now a standard tool employed in the early stages of drug discovery pipelines in order to screen possible drugs acting on a known target of interest. Computational structure-based drug discovery has steadily gained traction partially thanks to the constant improvements in available software, now often free and open source.
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