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Comprehensive, Open-Source, and Automated Workflow for Multisite λ-Dynamics in Lead Optimization.

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A new automated workflow for Multisite λ-dynamics (MSLD) makes binding free energy calculations more accessible for drug discovery lead optimization. This method significantly improves computational efficiency and accuracy compared to previous approaches.

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Area of Science:

  • Computational chemistry and molecular modeling.
  • Drug discovery and lead optimization.
  • Biophysics and molecular dynamics simulations.

Background:

  • Multisite λ-dynamics (MSLD) is an efficient binding free energy calculation method with potential for lead optimization.
  • Current MSLD methods face challenges due to complex data preparation and simulation processes, limiting their broad application.
  • There is a need for accessible and automated workflows to facilitate MSLD calculations in diverse protein-ligand systems.

Purpose of the Study:

  • To develop a comprehensive, open-source, and automated workflow for MSLD calculations.
  • To improve the accuracy and efficiency of MSLD for lead optimization in drug discovery.
  • To make MSLD calculations more accessible to researchers without specialized expertise.

Main Methods:

  • Development of an automated workflow based on the BLaDE dynamics engine.
  • Integration of the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm.
  • Validation through calculation of relative binding free energies for large-scale congeneric ligands with significant structural variations.

Main Results:

  • Excellent agreement between calculated and experimental binding free energies (average unsigned error of 1.08 ± 0.47 kcal/mol).
  • High accuracy demonstrated, with >57.1% of ligands showing errors < 1.0 kcal/mol and a Pearson correlation coefficient of 0.88.
  • Achieved computational efficiency over one order of magnitude faster than traditional free energy perturbation (FEP) methods with comparable accuracy.

Conclusions:

  • The developed automated MSLD workflow is accurate, efficient, and accessible, significantly advancing its application in drug discovery lead optimization.
  • The workflow successfully handles complex molecular systems and challenging substructures, outperforming previous methods.
  • MSLD, powered by this new workflow, emerges as a competitive and valuable tool for guiding drug discovery efforts.