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Conformator: A Novel Method for the Generation of Conformer Ensembles

Friedrich, Nils-Ole; Flachsenberg, Florian; Meyder, Agnes; Sommer, Kai; Kirchmair, Johannes; Rarey, Matthias
Journal article, Peer reviewed
Accepted version
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https://hdl.handle.net/11250/2738720
Utgivelsesdato
2019
Metadata
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  • Department of Chemistry [335]
  • Registrations from Cristin [5248]
Originalversjon
Journal of Chemical Information and Modeling. 2019, 59 (2), 731–742.   10.1021/acs.jcim.8b00704
Sammendrag
Computer-aided drug design methods such as docking, pharmacophore searching, 3D database searching, and the creation of 3D-QSAR models need conformational ensembles to handle the flexibility of small molecules. Here, we present Conformator, an accurate and effective knowledge-based algorithm for generating conformer ensembles. With 99.9% of all test molecules processed, Conformator stands out by its robustness with respect to input formats, molecular geometries, and the handling of macrocycles. With an extended set of rules for sampling torsion angles, a novel algorithm for macrocycle conformer generation, and a new clustering algorithm for the assembly of conformer ensembles, Conformator reaches a median minimum root-mean-square deviation (measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers) of 0.47 Å with no significant difference to the highest-ranked commercial algorithm OMEGA and significantly higher accuracy than seven free algorithms, including the RDKit DG algorithm. Conformator is freely available for noncommercial use and academic research.
Utgiver
American Chemical Society
Tidsskrift
Journal of Chemical Information and Modeling
Opphavsrett
Copyright 2019 American Chemical Society.

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