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Optimizing Multisite λ-Dynamics Throughput with Charge Renormalization.

Jonah Z Vilseck1, Luis F Cervantes2, Ryan L Hayes

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Multisite λ-dynamics (MSλD) aids chemical space exploration. A new automated tool, msld_py_prep, and charge renormalization simplify creating multiple topology models for MSλD, accelerating drug design.

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Area of Science:

  • Computational Chemistry
  • Molecular Modeling

Background:

  • Multisite λ-dynamics (MSλD) is effective for exploring large chemical spaces.
  • Creating multiple topology models (MTMs) for MSλD is complex, especially for ligand analogues.
  • Current methods require manual MTM preparation, hindering efficiency.

Purpose of the Study:

  • Introduce an automated workflow, msld_py_prep, for MTM generation in MSλD.
  • Present and evaluate charge renormalization for partitioning atomic charges in MTMs.
  • Enhance the applicability of MSλD in computer-aided drug design.

Main Methods:

  • Developed msld_py_prep, an automated workflow for MTM creation.
  • Implemented and tested charge renormalization for ligand charge partitioning.
  • Validated MTM generation with bookending calculations to correct charge renormalization effects.

Main Results:

  • msld_py_prep significantly reduces the time for preparing MSλD input files.
  • Charge renormalization, with bookending calculations, proves to be a viable method for MTM generation.
  • The workflow supports diverse force field parameters, expanding MSλD's utility.

Conclusions:

  • The msld_py_prep workflow streamlines MTM preparation for MSλD.
  • Charge renormalization offers a robust approach for MTM generation in MSλD.
  • This advancement broadens the scope of MSλD for drug discovery efforts.