Abstract
Root mean square displacement (RMSD) calculations play a fundamental role in the comparison of different conformers of the same ligand. This is particularly important in the evaluation of protein-ligand docking, where different ligand poses are generated by docking software and their quality is usually assessed by RMSD calculations. Unfortunately, many RMSD calculation tools do not take into account the symmetry of the molecule, remain difficult to integrate flawlessly in cheminformatics and machine learning pipelines—which are often written in Python—or are shipped within large code bases. Here we present a new open-source RMSD calculation tool written in Python, designed to be extremely lightweight and easy to integrate into existing software.
Highlights
Computational structure-based drug discovery has steadily gained traction partially thanks to the constant improvements in available software, often free and open source
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) [1], other metrics have been suggested [2]
Despite being somewhat slower than other state-of-theart tools for RMSD calculation, we believe that spyrmsd could be extremely useful to the community: it is a lightweight tool with focussed functionality, it is easy to use and integrate in existing Python codebases and pipelines, and it is easy to install via popular package managers
Summary
Computational structure-based drug discovery has steadily gained traction partially thanks to the constant improvements in available software, often free and open source. 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, avoiding the problems outlined above. We present a new Python tool, spyrmsd, for the calculation of symmetry-corrected RMSDs based on graph isomorphisms.
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