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Chemists understand that most drug sized molecules have some flexibility, and so may have multiple conformations that are accessible at room temperature.  Therefore, if we want to consider modelling the binding of a ligand to a protein, or the 3D similarity between two proteins we need to have access to multiple conformations of ligands.  This is an essential step in virtual screening. We could explore the ‘conformational space’ each time we look at a molecule, but obviously once you have a set of conformations for a molecule, it doesn’t change with the task in hand.  So why not pre-calculate the sets of conformers and store them? The question is how to generate such conformer ensembles, and philosophically more challenging, how to know if the set of generated conformations is ‘good’.  The team at Openeye produced a combined approach, partly using data from crystal structures to identify the well populated torsions for bonds, and then a more ‘first principles’ approach to generate low energy fragment structures and combine the fragments avoiding clashes.  Sets of conformers are then compared to an extremely well curated set of x-ray crystal structures. It’s worth reading the paper just for the exploration of what makes a good crystal structure.

If you’re using crystal structures, docking sets of conformers, comparing sets of molecules in 3D or looking at the results of any of those calculations.

Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database.

Hawkins, Skillman, Warren, Ellingson, & Stahl J. Chem. Inf. Model. (2010), 50, 572–584.

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