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A knowledge-based approach to generating diverse but energetically representative ensembles of ligand conformers

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Abstract

This paper describes a new and efficient stochastic conformational sampling method for generating a range of low-energy molecule conformations. Sampling can be tailored to a specific structural domain (e.g., peptides) by extracting torsional profiles from specific datasets and subsequently applying them to target molecules outside the reference set. The programs that handle creation of the knowledge-based torsional profiles and conformer generation per se are separate and so can be used independently or sequentially, depending on the task at hand. The conformational ensembles produced are contrasted with those generated using local minimization approaches. They are also quantitatively compared with a broader range of techniques in terms of speed and the ability to reproduce bound ligand conformations found in complexes with proteins.

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Acknowledgments

Many thanks to Dr. Edmond Abrahamian (Tripos) for infusing common sense into this publication, noting errors, suggesting additional areas to cover, and asking searching questions.

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Authors and Affiliations

  1. Tripos Informatics Research Center, 1699 S. Hanley Road, St. Louis, MO, 63144, USA

    Roman J. Dorfman, Karl M. Smith, Brian B. Masek & Robert D. Clark

Authors
  1. Roman J. Dorfman
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  2. Karl M. Smith
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  3. Brian B. Masek
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  4. Robert D. Clark
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Corresponding author

Correspondence to Robert D. Clark.

Appendices

Appendix A: Substructure standardization rules

figure a

Appendix B: Rotatable bond definitions used

figure b

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Dorfman, R.J., Smith, K.M., Masek, B.B. et al. A knowledge-based approach to generating diverse but energetically representative ensembles of ligand conformers. J Comput Aided Mol Des 22, 681–691 (2008). https://doi.org/10.1007/s10822-007-9156-5

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  • DOI: https://doi.org/10.1007/s10822-007-9156-5

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