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Lead optimization mapper: automating free energy calculations for lead optimization


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.

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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.

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Correspondence to David L. Mobley.

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Liu, S., Wu, Y., Lin, T. et al. Lead optimization mapper: automating free energy calculations for lead optimization. J Comput Aided Mol Des 27, 755–770 (2013).

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  • Binding free energy
  • Alchemical
  • Planning
  • Molecular dynamics
  • Molecular simulations
  • Lead optimization