Identification of all of the influential conformers of
biomolecules
is a crucial step in many tasks of computational biochemistry. Specifically,
molecular docking, a key component of in silico drug
development, requires a comprehensive set of conformations for potential
candidates in order to generate the optimal ligand–receptor
poses and, ultimately, find the best drug candidates. However, the
presence of flexible cycles in a molecule complicates the initial
search for conformers since exhaustive sampling algorithms via torsional random and systematic searches become very
inefficient. The devised inverse-kinematics-based Monte Carlo with
refinement (MCR) algorithm identifies independently rotatable dihedral
angles in (poly)cyclic molecules and uses them to perform global conformational
sampling, outperforming popular alternatives (MacroModel, CREST, and
RDKit) in terms of speed and diversity of the resulting conformer
ensembles. Moreover, MCR quickly and accurately recovers naturally
occurring macrocycle conformations for most of the considered molecules.