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

Multisite λ-dynamics (MSLD) is a highly efficient
binding
free energy calculation method that samples multiple ligands in a
single round by assigning different λ values to the alchemical
part of each ligand. This method holds great promise for lead optimization
(LO) in drug discovery. However, the complex data preparation and
simulation process limits its widespread application in diverse protein–ligand
systems. To address this challenge, we developed a comprehensive,
open-source, and automated workflow for MSLD calculations based on
the BLaDE dynamics engine. This workflow incorporates the Ligand Internal
and Cartesian coordinate reconstruction-based alignment algorithm
(LIC-align) and an optimized maximum common substructure (MCS) search
algorithm to accurately generate MSLD multiple topologies with ideal
perturbation patterns. Furthermore, our workflow is highly modularized,
allowing straightforward integration and extension of various simulation
techniques, and is highly accessible to nonexperts. This workflow
was validated by calculating the relative binding free energies of
large-scale congeneric ligands, many of which have large perturbing
groups. The agreement between the calculations and experiments was
excellent, with an average unsigned error of 1.08 ± 0.47 kcal/mol.
More than 57.1% of the ligands had an error of less than 1.0 kcal/mol,
and the perturbations of 6 targets were fully connected via the calculations,
while those of 2 targets were connected via both calculations and
experimental data. The Pearson correlation coefficient reached 0.88,
indicating that the MSLD workflow provides accurate predictions that
can guide lead optimization in drug discovery. We also examined the
impact of single-site versus multisite perturbations, ligand grouping
by perturbing group size, and the position of the anchor atom on the
MSLD performance. By integrating our proposed LIC-align and optimized
MCS search algorithm along with the coping strategies to handle challenging
molecular substructures, our workflow can handle many realistic scenarios
more reasonably than all previously published methods. Moreover, we
observed that our MSLD workflow achieved similar accuracy to free
energy perturbation (FEP) while improving computational efficiency
by over 1 order of magnitude in speedup. These findings provide valuable
insights and strategies for further MSLD development, making MSLD
a competitive tool for lead optimization.