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Journal of Computer-Aided Molecular Design

, Volume 27, Issue 9, pp 755–770 | Cite as

Lead optimization mapper: automating free energy calculations for lead optimization

  • Shuai Liu
  • Yujie Wu
  • Teng Lin
  • Robert Abel
  • Jonathan P. Redmann
  • Christopher M. Summa
  • Vivian R. Jaber
  • Nathan M. Lim
  • David L. MobleyEmail author
Article

Abstract

Alchemical free energy calculations hold increasing promise as an aid to drug discovery efforts. However, applications of these techniques in discovery projects have been relatively few, partly because of the difficulty of planning and setting up calculations. Here, we introduce lead optimization mapper, LOMAP, an automated algorithm to plan efficient relative free energy calculations between potential ligands within a substantial library of perhaps hundreds of compounds. In this approach, ligands are first grouped by structural similarity primarily based on the size of a (loosely defined) maximal common substructure, and then calculations are planned within and between sets of structurally related compounds. An emphasis is placed on ensuring that relative free energies can be obtained between any pair of compounds without combining the results of too many different relative free energy calculations (to avoid accumulation of error) and by providing some redundancy to allow for the possibility of error and consistency checking and provide some insight into when results can be expected to be unreliable. The algorithm is discussed in detail and a Python implementation, based on both Schrödinger’s and OpenEye’s APIs, has been made available freely under the BSD license.

Keywords

Binding free energy Alchemical Planning Molecular dynamics Molecular simulations Lead optimization 

Notes

Acknowledgments

We are grateful to Michael Shirts (University of Virginia) and Pavel Klimovich (UCI) for helpful discussions, and to John Chodera (Memorial Sloan Kettering Cancer Center) for both useful input and an initial MCSS tool that helped motivate the work. We acknowledge the financial support of the National Institutes of Health (1R15GM096257-01A1), the National Science Foundation LA-SiGMA program (EPS-1003897), the Louisiana Board of Regents Research Competitiveness and Research Enhancement Subprograms, Children’s Hospital of New Orleans, and computer time from the UCI GreenPlanet cluster, supported in part by NSF CHE-0840513.

Supplementary material

10822_2013_9678_MOESM1_ESM.pdf (190 kb)
PDF (190 KB)

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Shuai Liu
    • 1
  • Yujie Wu
    • 3
  • Teng Lin
    • 3
  • Robert Abel
    • 3
  • Jonathan P. Redmann
    • 4
  • Christopher M. Summa
    • 4
  • Vivian R. Jaber
    • 2
  • Nathan M. Lim
    • 1
  • David L. Mobley
    • 1
    • 2
    Email author
  1. 1.Department of Pharmaceutical Sciences and Department of ChemistryUniversity of California, IrvineIrvineUSA
  2. 2.Department of ChemistryUniversity of New OrleansNew OrleansUSA
  3. 3.SchrödingerNew YorkUSA
  4. 4.Department of Computer ScienceUniversity of New OrleansNew OrleansUSA

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